Search results for: machine failures

Authors: Philipp Zopf, Franz Haas

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Manufacturing of very hard and refractory materials like ceramics, glass or carbide poses particular challenges on tools and machines. The company Sauer GmbH developed especially for this application area ultrasonic tool holders working in a frequency range from 15 to 60 kHz and superimpose the common tool movement in the vertical axis. This technique causes a structural weakening in the contact area and facilitates the machining. The possibility of the force reduction for these special materials especially in drilling of carbide with diamond tools up to 30 percent made the authors try to expand the application range of this method. To make the results evaluable, the authors decide to start with existing processes in which the positive influence of the ultrasonic assistance is proven to understand the mechanism. The comparison of a grinding process the Institute use to machine materials mentioned in the beginning and steel could not be more different. In the first case, the authors use tools with geometrically undefined edges. In the second case, the edges are geometrically defined. To get valid results of the tests, the authors decide to investigate two manufacturing methods, drilling and milling. The main target of the investigation is to reduce the cutting force measured with a force measurement platform underneath the workpiece. Concerning to the direction of the ultrasonic assistance, the authors expect lower cutting forces and longer endurance of the tool in the drilling process. To verify the frequencies and the amplitudes an FFT-analysis is performed. It shows the increasing damping depending on the infeed rate of the tool. The reducing of amplitude of the cutting force comes along.

Keywords: drilling, machining, milling, ultrasonic

Procedia PDF Downloads 274

Authors: Somayeh Komeylian

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The direction of arrival (DoA) estimation is the crucial aspect of the radar technologies for detecting and dividing several signal sources. In this scenario, the antenna array output modeling involves numerous parameters including noise samples, signal waveform, signal directions, signal number, and signal to noise ratio (SNR), and thereby the methods of the DoA estimation rely heavily on the generalization characteristic for establishing a large number of the training data sets. Hence, we have analogously represented the two different optimization models of the DoA estimation; (1) the implementation of the decision directed acyclic graph (DDAG) for the multiclass least-squares support vector machine (LS-SVM), and (2) the optimization method of the deep neural network (DNN) radial basis function (RBF). We have rigorously verified that the LS-SVM DDAG algorithm is capable of accurately classifying DoAs for the three classes. However, the accuracy and robustness of the DoA estimation are still highly sensitive to technological imperfections of the antenna arrays such as non-ideal array design and manufacture, array implementation, mutual coupling effect, and background radiation and thereby the method may fail in representing high precision for the DoA estimation. Therefore, this work has a further contribution on developing the DNN-RBF model for the DoA estimation for overcoming the limitations of the non-parametric and data-driven methods in terms of array imperfection and generalization. The numerical results of implementing the DNN-RBF model have confirmed the better performance of the DoA estimation compared with the LS-SVM algorithm. Consequently, we have analogously evaluated the performance of utilizing the two aforementioned optimization methods for the DoA estimation using the concept of the mean squared error (MSE).

Keywords: DoA estimation, Adaptive antenna array, Deep Neural Network, LS-SVM optimization model, Radial basis function, and MSE

Procedia PDF Downloads 100

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

Procedia PDF Downloads 402

Authors: Juzhong Tan, William Kerr

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Conching is one critical procedure in chocolate processing, where special flavors are developed, and smooth mouse feel the texture of the chocolate is developed due to particle size reduction of cocoa mass and other additives. Therefore, determination of the particle size and volatile compounds profile of cocoa bean is important for chocolate manufacturers to ensure the quality of chocolate products. Currently, precise particle size measurement is usually done by laser scattering which is expensive and inaccessible to small/medium size chocolate manufacturers. Also, some other alternatives, such as micrometer and microscopy, can’t provide good measurements and provide little information. Volatile compounds analysis of cocoa during conching, has similar problems due to its high cost and limited accessibility. In this study, a self-made electronic nose system consists of gas sensors (TGS 800 and 2000 series) was inserted to a conching machine and was used to monitoring the volatile compound profile of chocolate during the conching. A model correlated volatile compounds profiles along with factors including the content of cocoa, sugar, and the temperature during the conching to particle size of chocolate particles by genetic programming was established. The model was used to predict the particle size reduction of chocolates with different cocoa mass to sugar ratio (1:2, 1:1, 1.5:1, 2:1) at 8 conching time (15min, 30min, 1h, 1.5h, 2h, 4h, 8h, and 24h). And the predictions were compared to laser scattering measurements of the same chocolate samples. 91.3% of the predictions were within the range of later scatting measurement ± 5% deviation. 99.3% were within the range of later scatting measurement ± 10% deviation.

Keywords: cocoa bean, conching, electronic nose, genetic programming

Procedia PDF Downloads 255

Authors: Imam Taufiqurrahman, Anugrah Adiwilaga, Egi Hidayat, Bambang Riyanto Trilaksono

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Autonomous Underwater Vehicle glider is one of the renewal of underwater vehicles. This vehicle is one of the autonomous underwater vehicles that are being developed in Indonesia. Glide ability is obtained by controlling the buoyancy and attitude of the vehicle using the movers within the vehicle. The glider motion mechanism is expected to provide energy resistance from autonomous underwater vehicles so as to increase the cruising range of rides while performing missions. The control system on the vehicle consists of three parts: controlling the attitude of the pitch, the buoyancy engine controller and the yaw controller. The buoyancy and pitch controls on the vehicle are sequentially referring to the finite state machine with pitch angle and depth of diving inputs to obtain a gliding cycle. While the yaw control is done through the rudder for the needs of the guide system. This research is focused on design and implementation of control system of Autonomous Underwater Vehicle glider based on PID anti-windup. The control system is implemented on an ARM TS-7250-V2 device along with a mathematical model of the vehicle in MATLAB using the hardware-in-the-loop simulation (HILS) method. The TS-7250-V2 is chosen because it complies industry standards, has high computing capability, minimal power consumption. The results show that the control system in HILS process can form glide cycle with depth and angle of operation as desired. In the implementation using half control and full control mode, from the experiment can be concluded in full control mode more precision when tracking the reference. While half control mode is considered more efficient in carrying out the mission.

Keywords: control system, PID, underwater glider, marine robotics

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Authors: Ouafa Amira, Jiangshe Zhang

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Clustering is an unsupervised machine learning technique; its aim is to extract the data structures, in which similar data objects are grouped in the same cluster, whereas dissimilar objects are grouped in different clusters. Clustering methods are widely utilized in different fields, such as: image processing, computer vision , and pattern recognition, etc. Fuzzy c-means clustering (fcm) is one of the most well known fuzzy clustering methods. It is based on solving an optimization problem, in which a minimization of a given cost function has been studied. This minimization aims to decrease the dissimilarity inside clusters, where the dissimilarity here is measured by the distances between data objects and cluster centers. The degree of belonging of a data point in a cluster is measured by a membership function which is included in the interval [0, 1]. In fcm clustering, the membership degree is constrained with the condition that the sum of a data object’s memberships in all clusters must be equal to one. This constraint can cause several problems, specially when our data objects are included in a noisy space. Regularization approach took a part in fuzzy c-means clustering technique. This process introduces an additional information in order to solve an ill-posed optimization problem. In this study, we focus on regularization by relative entropy approach, where in our optimization problem we aim to minimize the dissimilarity inside clusters. Finding an appropriate membership degree to each data object is our objective, because an appropriate membership degree leads to an accurate clustering result. Our clustering results in synthetic data sets, gaussian based data sets, and real world data sets show that our proposed model achieves a good accuracy.

Keywords: clustering, fuzzy c-means, regularization, relative entropy

Procedia PDF Downloads 259

Authors: Mostafa Mabrouk, Basma E. Abdel-Ghany, Mona Moaness, Bothaina M. Abdel-Hady, Hanan H. Beherei

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Tissue engineering became a promising field for bone repair and regenerative medicine in which cultured cells, scaffolds and osteogenic inductive signals are used to regenerate tissues. The annual cost of treating bone defects in Egypt has been estimated to be many billions, while enormous costs are spent on imported bone grafts for bone injuries, tumors, and other pathologies associated with defective fracture healing. The current study is aimed at developing a more strategic approach in order to speed-up recovery after bone damage. This will reduce the risk of fatal surgical complications and improve the quality of life of people affected with such fractures. 3D scaffolds loaded with cheap nano-particles that possess an osteogenic effect were prepared by nano-electrospinning. The Microstructure and morphology characterizations of the 3D scaffolds were monitored using scanning electron microscopy (SEM). The physicochemical characterization was investigated using X-ray diffractometry (XRD) and infrared spectroscopy (IR). The Physicomechanical properties of the 3D scaffold were determined by a universal testing machine. The in vitro bioactivity of the 3D scaffold was assessed in simulated body fluid (SBF). The bone-bonding ability of novel 3D scaffolds was also evaluated. The obtained nanofibrous scaffolds demonstrated promising microstructure, physicochemical and physicomechanical features appropriate for enhanced bone regeneration. Therefore, the utilized nanomaterials loaded with the drug are greatly recommended as cheap alternatives to growth factors.

Keywords: bone regeneration, cheap scaffolds, nanomaterials, active molecules

Procedia PDF Downloads 188

Authors: Jude K. Safo

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Knowledge Graphs KG) and their relation to Graph Embeddings (GE) represent a unique data structure in the landscape of machine learning (relative to image, text and acoustic data). Unlike the latter, GEs are the only data structure sufficient for representing hierarchically dense, semantic information needed for use-cases like supply chain data and protein folding where the search space exceeds the limits traditional search methods (e.g. page-rank, Dijkstra, etc.). While GEs are effective for compressing low rank tensor data, at scale, they begin to introduce a new problem of ’data retreival’ which we observe in Large Language Models. Notable attempts by transE, TransR and other prominent industry standards have shown a peak performance just north of 57% on WN18 and FB15K benchmarks, insufficient practical industry applications. They’re also limited, in scope, to next node/link predictions. Traditional linear methods like Tucker, CP, PARAFAC and CANDECOMP quickly hit memory limits on tensors exceeding 6.4 million nodes. This paper outlines a topological framework for linear mapping between concepts in KG space and GE space that preserve cardinality. Most importantly we introduce a traceable framework for composing dense linguistic strcutures. We demonstrate performance on WN18 benchmark this model hits. This model does not rely on Large Langauge Models (LLM) though the applications are certainy relevant here as well.

Keywords: representation theory, large language models, graph embeddings, applied algebraic topology, applied knot theory, combinatorics

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Authors: Behnaz Sohani, Sahand Shahalinezhad, Amir Rahmani, Aliyu Aliyu

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Recently, medical imaging and specifically medical image processing is becoming one of the most dynamically developing areas of medical science. It has led to the emergence of new approaches in terms of the prevention, diagnosis, and treatment of various diseases. In the process of diagnosis of lung cancer, medical professionals rely on computed tomography (CT) scans, in which failure to correctly identify masses can lead to incorrect diagnosis or sampling of lung tissue. Identification and demarcation of masses in terms of detecting cancer within lung tissue are critical challenges in diagnosis. In this work, a segmentation system in image processing techniques has been applied for detection purposes. Particularly, the use and validation of a novel lung cancer detection algorithm have been presented through simulation. This has been performed employing CT images based on multilevel thresholding. The proposed technique consists of segmentation, feature extraction, and feature selection and classification. More in detail, the features with useful information are selected after featuring extraction. Eventually, the output image of lung cancer is obtained with 96.3% accuracy and 87.25%. The purpose of feature extraction applying the proposed approach is to transform the raw data into a more usable form for subsequent statistical processing. Future steps will involve employing the current feature extraction method to achieve more accurate resulting images, including further details available to machine vision systems to recognise objects in lung CT scan images.

Keywords: lung cancer detection, image segmentation, lung computed tomography (CT) images, medical image processing

Procedia PDF Downloads 101

Authors: Prodipta Bhowmik, Poulami Saha, Preety Mehra, Yogesh Soni, Triloki Nath Jha

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In India, the smart city concept is growing day by day. So, for smart city development, a better traffic management and monitoring system is a very important requirement. Nowadays, road accidents increase due to more vehicles on the road. Reckless driving is mainly responsible for a huge number of accidents. So, an efficient traffic management system is required for all kinds of roads to control the traffic speed. The speed limit varies from road to road basis. Previously, there was a radar system but due to high cost and less precision, the radar system is unable to become favorable in a traffic management system. Traffic management system faces different types of problems every day and it has become a researchable topic on how to solve this problem. This paper proposed a computer vision and machine learning-based automated system for multiple vehicle detection, tracking, and speed estimation of vehicles using image processing. Detection of vehicles and estimating their speed from a real-time video is tough work to do. The objective of this paper is to detect vehicles and estimate their speed as accurately as possible. So for this, a real-time video is first captured, then the frames are extracted from that video, then from that frames, the vehicles are detected, and thereafter, the tracking of vehicles starts, and finally, the speed of the moving vehicles is estimated. The goal of this method is to develop a cost-friendly system that can able to detect multiple types of vehicles at the same time.

Keywords: OpenCV, Haar Cascade classifier, DLIB, YOLOV3, centroid tracker, vehicle detection, vehicle tracking, vehicle speed estimation, computer vision

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Authors: Meng Su

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High-dimensional data analysis often presents challenges in capturing the complex, nonlinear relationships and manifold structures inherent to the data. This article presents a novel approach that leverages the strengths of two powerful techniques, Diffusion Maps and Diffusion Probabilistic Models (DPMs), to address these challenges. By integrating the dimensionality reduction capability of Diffusion Maps with the data modeling ability of DPMs, the proposed method aims to provide a comprehensive solution for analyzing and generating high-dimensional data. The Diffusion Map technique preserves the nonlinear relationships and manifold structure of the data by mapping it to a lower-dimensional space using the eigenvectors of the graph Laplacian matrix. Meanwhile, DPMs capture the dependencies within the data, enabling effective modeling and generation of new data points in the low-dimensional space. The generated data points can then be mapped back to the original high-dimensional space, ensuring consistency with the underlying manifold structure. Through a detailed example implementation, the article demonstrates the potential of the proposed hybrid approach to achieve more accurate and effective modeling and generation of complex, high-dimensional data. Furthermore, it discusses possible applications in various domains, such as image synthesis, time-series forecasting, and anomaly detection, and outlines future research directions for enhancing the scalability, performance, and integration with other machine learning techniques. By combining the strengths of Diffusion Maps and DPMs, this work paves the way for more advanced and robust data analysis methods.

Keywords: diffusion maps, diffusion probabilistic models (DPMs), manifold learning, high-dimensional data analysis

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Authors: Noriyuki Suyama

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The progress and spread of digitalization have led to the provision of a variety of new services. The recent progress in digitization can be attributed to rapid developments in science and technology. First, the research and diffusion of artificial intelligence (AI) has made dramatic progress. Around 2000, the third wave of AI research, which had been underway for about 50 years, arrived. Specifically, machine learning and deep learning were made possible in AI, and the ability of AI to acquire knowledge, define the knowledge, and update its own knowledge in a quantitative manner made the use of big data practical even for commercial PCs. On the other hand, with the spread of social media, information exchange has become more common in our daily lives, and the lending and borrowing of goods and services, in other words, the sharing economy, has become widespread. The scope of this trend is not limited to any industry, and its momentum is growing as the SDGs take root. In addition, the Social Network Service (SNS), a part of social media, has brought about the evolution of the retail business. In the past few years, social network services (SNS) involving users or companies have especially flourished. The People's Republic of China (hereinafter referred to as "China") is a country that is stimulating enormous consumption through its own unique SNS, which is different from the SNS used in developed countries around the world. This paper focuses on the effectiveness and challenges of influencer marketing by focusing on the influence of influencers on users' behavior and satisfaction with Chinese SNSs. Specifically, Conducted was the quantitative survey of Tik Tok users living in China, with the aim of gaining new insights from the analysis and discussions. As a result, we found several important findings and knowledge.

Keywords: customer satisfaction, social networking services, influencer marketing, Chinese consumers’ behavior

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Authors: Roman Knizek, Denisa Knizkova

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This paper describes the development of a nanofiber membrane for sport and outdoor use at the Technical University of Liberec (TUL) and the following cooperation with a private Czech company which launched this product onto the market. For making this membrane, Polyurethan was electrospun on the Nanospider spinning machine, and a wire string electrode was used. The created nanofiber membrane with a nanofiber diameter of 150 nm was subsequently hydrophobisied using a low vacuum plasma and Fluorocarbon monomer C6 type. After this hydrophobic treatment, the nanofiber membrane contact angle was higher than 125o, and its oleophobicity was 6. The last step was a lamination of this nanofiber membrane with a woven or knitted fabric to create a 3-layer laminate. Gravure printing technology and polyurethane hot-melt adhesive were used. The gravure roller has a mesh of 17. The resulting 3-layer laminate has a water vapor permeability Ret of 1.6 [Pa.m2.W-1] (– measured in compliance with ISO 11092), it is 100% windproof (– measured in compliance with ISO 9237), and the water column is above 10 000 mm (– measured in compliance with ISO 20811). This nanofiber membrane which was developed in the laboratories of the Technical University of Liberec was then produced industrially by a private company. A low vacuum plasma line and a lamination line were needed for industrial production, and the process had to be fine-tuned to achieve the same parameters as those achieved in the TUL laboratories. The result of this work is a newly developed nanofiber membrane which offers much better properties, especially water vapor permeability, than other competitive membranes. It is an example of product development and the consequent fine-tuning for industrial production; it is also an example of the cooperation between a Czech state university and a private company.

Keywords: nanofiber membrane, start-up, state university, private company, product

Procedia PDF Downloads 142

Authors: Arturo Reyes Roman, Daniza Castillo Godoy, Francisca Balarezo Olivares, Francisco Arriagada Castro, Miguel Maulen Tapia

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Mining waste–based geopolymers are a sustainable alternative to conventional cement materials due to their contribution to the valorization of mining wastes as well as to the new construction materials with reduced fingerprints. The objective of this study was to determine the potential of leaching gravel (LG) from hydrometallurgical copper processing to be used as a raw material in the manufacture of geopolymer. NaOH, Na2SiO3 (modulus 1.5), and LG were mixed and then wetted with an appropriate amount of tap water, then stirred until a homogenous paste was obtained. A liquid/solid ratio of 0.3 was used for preparing mixtures. The paste was then cast in cubic moulds of 50 mm for the determination of compressive strengths. The samples were left to dry for 24h at room temperature, then unmoulded before analysis after 28 days of curing time. The compressive test was conducted in a compression machine (15/300 kN). According to the laser diffraction spectroscopy (LDS) analysis, 90% of LG particles were below 500 μm. The X-ray diffraction (XRD) analysis identified crystalline phases of albite (30 %), Quartz (16%), Anorthite (16 %), and Phillipsite (14%). The X-ray fluorescence (XRF) determinations showed mainly 55% of SiO2, 13 % of Al2O3, and 9% of CaO. ICP (OES) concentrations of Fe, Ca, Cu, Al, As, V, Zn, Mo, and Ni were 49.545; 24.735; 6.172; 14.152, 239,5; 129,6; 41,1;15,1, and 13,1 mg kg-1, respectively. The geopolymer samples showed resistance ranging between 2 and 10 MPa. In comparison with the raw material composition, the amorphous percentage of materials in the geopolymer was 35 %, whereas the crystalline percentage of main mineral phases decreased. Further studies are needed to find the optimal combinations of materials to produce a more resistant and environmentally safe geopolymer. Particularly are necessary compressive resistance higher than 15 MPa are necessary to be used as construction unit such as bricks.

Keywords: mining waste, geopolymer, construction material, alkaline activation

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Authors: M. K. Wijayarathna, S. M. Buddika Harshanath

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Monitoring student engagement has become a crucial part of the educational process and a reliable indicator of the capacity to retain information. As online learning classrooms are now more common these days, students' attention levels have become increasingly important, making it more difficult to check each student's concentration level in an online classroom setting. To profile student attention to various gradients of engagement, a study is a plan to conduct using machine learning models. Using a convolutional neural network, the findings and confidence score of the high accuracy model are obtained. In this research, convolutional neural networks are using to help discover essential emotions that are critical in defining various levels of participation. Students' attention levels were shown to be influenced by emotions such as calm, enjoyment, surprise, and fear. An improved virtual learning system was created as a result of these data, which allowed teachers to focus their support and advise on those students who needed it. Student participation has formed as a crucial component of the learning technique and a consistent predictor of a student's capacity to retain material in the classroom. Convolutional neural networks have a plan to implement the platform. As a preliminary step, a video of the pupil would be taken. In the end, researchers used a convolutional neural network utilizing the Keras toolkit to take pictures of the recordings. Two convolutional neural network methods are planned to use to determine the pupils' attention level. Finally, those predicted student attention level results plan to display on the graphical user interface of the System.

Keywords: HTML5, JavaScript, Python flask framework, AI, graphical user

Procedia PDF Downloads 99

Authors: Yasser F. Hassan

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Social networking involves use of an online platform or website that enables people to communicate, usually for a social purpose, through a variety of services, most of which are web-based and offer opportunities for people to interact over the internet, e.g. via e-mail and ‘instant messaging’, by analyzing the voting behavior and ratings of judges in a popular comments in social networks. While most of the party literature omits the electorate, this paper presents a model where elites and parties are emergent consequences of the behavior and preferences of voters. The research in artificial intelligence and psychology has provided powerful illustrations of the way in which the emergence of intelligent behavior depends on the development of representational structure. As opposed to the classical voting system (one person – one decision – one vote) a new voting system is designed where agents with opposed preferences are endowed with a given number of votes to freely distribute them among some issues. The paper uses ideas from machine learning, artificial intelligence and soft computing to provide a model of the development of voting system response in a simulated agent. The modeled development process involves (simulated) processes of evolution, learning and representation development. The main value of the model is that it provides an illustration of how simple learning processes may lead to the formation of structure. We employ agent-based computer simulation to demonstrate the formation and interaction of coalitions that arise from individual voter preferences. We are interested in coordinating the local behavior of individual agents to provide an appropriate system-level behavior.

Keywords: voting system, rough sets, multi-agent, social networks, emergence, power indices

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Authors: Rupita Ghosh, Ritwik Sarkar, Sumit K. Pal, Soumitra Paul

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The use of Hydroxyapatite bioceramics as restorative implants is widely known. These materials can be manufactured by pressing and sintering route to a particular shape. However machining processes are still a basic requirement to give a near net shape to those implants for ensuring dimensional and geometrical accuracy. In this context, optimising the machining parameters is an important factor to understand the machinability of the materials and to reduce the production cost. In the present study a method has been optimized to produce true particulate drilled composite of Hydroxyapatite Yttrium Phosphate. The phosphates are used in varying ratio for a comparative study on the effect of flexural strength, hardness, machining (drilling) parameters and bioactivity.. The maximum flexural strength and hardness of the composite that could be attained are 46.07 MPa and 1.02 GPa respectively. Drilling is done with a conventional radial drilling machine aided with dynamometer with high speed steel (HSS) and solid carbide (SC) drills. The effect of variation in drilling parameters (cutting speed and feed), cutting tool, batch composition on torque, thrust force and tool wear are studied. It is observed that the thrust force and torque varies greatly with the increase in the speed, feed and yttrium phosphate content in the composite. Significant differences in the thrust and torque are noticed due to the change of the drills as well. Bioactivity study is done in simulated body fluid (SBF) upto 28 days. The growth of the bone like apatite has become denser with the increase in the number of days for all the composition of the composites and it is comparable to that of the pure hydroxyapatite.

Keywords: Bioactivity, Drilling, Hydroxyapatite, Yttrium Phosphate

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Authors: L. Krpalkova, N. O' Mahony, A. Carvalho, S. Campbell, G. Corkery, E. Broderick, J. Walsh

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Farm management and operations will drastically change due to access to real-time data, real-time forecasting, and tracking of physical items in combination with Internet of Things developments to further automate farm operations. Dairy farms have embraced technological innovations and procured vast amounts of permanent data streams during the past decade; however, the integration of this information to improve the whole farm-based management and decision-making does not exist. It is now imperative to develop a system that can collect, integrate, manage, and analyse on-farm and off-farm data in real-time for practical and relevant environmental and economic actions. The developed systems, based on machine learning and artificial intelligence, need to be connected for useful output, a better understanding of the whole farming issue, and environmental impact. Evolutionary computing can be very effective in finding the optimal combination of sets of some objects and, finally, in strategy determination. The system of the future should be able to manage the dairy farm as well as an experienced dairy farm manager with a team of the best agricultural advisors. All these changes should bring resilience and sustainability to dairy farming as well as improving and maintaining good animal welfare and the quality of dairy products. This review aims to provide an insight into the state-of-the-art of big data applications and evolutionary computing in relation to smart dairy farming and identify the most important research and development challenges to be addressed in the future. Smart dairy farming influences every area of management, and its uptake has become a continuing trend.

Keywords: big data, evolutionary computing, cloud, precision technologies

Procedia PDF Downloads 189

Authors: Shahad Nagoor, Lucy Hederman, Kevin Koidl, Annalina Caputo

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Doctors’ notes reflect their impressions, attitudes, clinical sense, and opinions about patients’ conditions and progress, and other information that is essential for doctors’ daily clinical decisions. Despite their value, clinical notes are insufficiently researched within the language processing community. Automatically extracting information from unstructured text data is known to be a difficult task as opposed to dealing with structured information such as vital physiological signs, images, and laboratory results. The aim of this research is to investigate how Natural Language Processing (NLP) techniques and machine learning techniques applied to clinician notes can assist in doctors’ decision-making in Intensive Care Unit (ICU) for coronavirus disease 2019 (COVID-19) patients. The hypothesis is that clinical outcomes like survival or mortality can be useful in influencing the judgement of clinical sentiment in ICU clinical notes. This paper introduces two contributions: first, we introduce COVID_ICU_BERT, a fine-tuned version of clinical transformer models that can reliably predict clinical sentiment for notes of COVID patients in the ICU. We train the model on clinical notes for COVID-19 patients, a type of notes that were not previously seen by clinicalBERT, and Bio_Discharge_Summary_BERT. The model, which was based on clinicalBERT achieves higher predictive accuracy (Acc 93.33%, AUC 0.98, and precision 0.96 ). Second, we perform data augmentation using clinical contextual word embedding that is based on a pre-trained clinical model to balance the samples in each class in the data (survived vs. deceased patients). Data augmentation improves the accuracy of prediction slightly (Acc 96.67%, AUC 0.98, and precision 0.92 ).

Keywords: BERT fine-tuning, clinical sentiment, COVID-19, data augmentation

Procedia PDF Downloads 206

Authors: Hamidreza Hoshyarmanesh, Benjamin Durante, Alex Irwin, Sanju Lama, Kourosh Zareinia, Garnette R. Sutherland

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This study reports on a proposed method to evaluate the accuracy and precision of Excalibur I, a neurosurgery-specific haptic hand controller, designed and developed at Project neuroArm. Having an efficient and successful robot-assisted telesurgery is considerably contingent on how accurate and precise a haptic hand controller (master/local robot) would be able to interpret the kinematic indices of motion, i.e., position and orientation, from the surgeon’s upper limp to the slave/remote robot. A proposed test rig is designed and manufactured according to standard ASTM F2554-10 to determine the accuracy and precision range of Excalibur I at four different locations within its workspace: central workspace, extreme forward, far left and far right. The test rig is metrologically characterized by a coordinate measuring machine (accuracy and repeatability < ± 5 µm). Only the serial linkage of the haptic device is examined due to the use of the Structural Length Index (SLI). The results indicate that accuracy decreases by moving from the workspace central area towards the borders of the workspace. In a comparative study, Excalibur I performs on par with the PHANToM PremiumTM 3.0 and more accurate/precise than the PHANToM PremiumTM 1.5. The error in Cartesian coordinate system shows a dominant component in one direction (δx, δy or δz) for the movements on horizontal, vertical and inclined surfaces. The average error magnitude of three attempts is recorded, considering all three error components. This research is the first promising step to quantify the kinematic performance of Excalibur I.

Keywords: accuracy, advanced metrology, hand controller, precision, robot-assisted surgery, tele-operation, workspace

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Authors: Yoftahe Nigussie Worku

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This paper explores the utilization of vapor absorption refrigeration systems (VARS) as an alternative to the conventional vapor compression refrigerant systems (VCRS) in vehicle air conditioning (AC) systems. Currently, most vehicles employ VCRS, which relies on engine power to drive the compressor, leading to additional fuel consumption. In contrast, VARS harnesses low-grade heat, specifically from the exhaust of high-power internal combustion engines, reducing the burden on the vehicle's engine. The historical development of vapor absorption technology is outlined, dating back to Michael Faraday's discovery in 1824 and the subsequent creation of the first vapor absorption refrigeration machine by Ferdinand Carre in 1860. The paper delves into the fundamental principles of VARS, emphasizing the replacement of mechanical processes with physicochemical interactions, utilizing heat rather than mechanical work. The study compares the basic concepts of the current vapor compression systems with the proposed vapor absorption systems, highlighting the efficiency gains achieved by eliminating the need for engine-driven compressors. The vapor absorption refrigeration cycle (VARC) is detailed, focusing on the generator's role in separating and vaporizing ammonia, chosen for its low-temperature evaporation characteristics. The project's statement underscores the need for increased efficiency in vehicle AC systems beyond the limitations of VCRS. By introducing VARS, driven by low-grade heat, the paper advocates for a reduction in engine power consumption and, consequently, a decrease in fuel usage. This research contributes to the ongoing efforts to enhance sustainability and efficiency in automotive climate control systems.

Keywords: VCRS, VARS, efficiency, sustainability

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Authors: Hojjat Mohammadi, Randall Divito, Gary J. E. Kramer

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Tunnelling and pipe jacking in very soft soils (fat clays), even with an Earth Pressure Balance tunnel boring machine (EPBM), can cause large ground displacements. In this study, the short-term and long-term ground and tunnel response is predicted for twin, pipe-jacked EPBM 3 meter diameter tunnels with a narrow pillar width. Initial modelling indicated complete closure of the annulus gap at the tail shield onto the centrifugally cast, glass-fiber-reinforced, polymer mortar jacking pipe (FRP). Numerical modelling was employed to simulate the excavation and support installation sequence, examine the ground response during excavation, confirm the adequacy of the pillar width and check the structural adequacy of the installed pipe. In the numerical models, Mohr-Coulomb constitutive model with the effect of unloading was adopted for the fat clays, while for the bedrock layer, the generalized Hoek-Brown was employed. The numerical models considered explicit excavation sequences and different levels of ground convergence prior to support installation. The well-studied excavation sequences made the analysis possible for this study on a very soft clay, otherwise, obtaining the convergency in the numerical analysis would be impossible. The predicted results indicate that the ground displacements around the tunnel and its effect on the pipe would be acceptable despite predictions of large zones of plastic behaviour around the tunnels and within the entire pillar between them due to excavation-induced ground movements.

Keywords: finite element modeling (FEM), pipe-jacked tunneling, very soft clay, EPBM

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Authors: Geetanjali Sharma

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Guillain Barre’ syndrome (GBS) is an auto-immune mediated demyelination poly-radiculo-neuropathy. Clinical features include progressive symmetrical ascending muscle weakness of more than two limbs, areflexia with or without sensory, autonomic and brainstem abnormalities, the purpose of this study was to determine subclinical neurological changes of CNS with GBS and to establish the presence of central demyelination in GBS. The study was prospective and conducted in the Department of Physiology, Pt. B. D. Sharma Post-graduate Institute of Medical Sciences, University of Health Sciences, Rohtak, Haryana, India to find out early central demyelination in clinically diagnosed patients of GBS. These patients were referred from the department of Medicine of our Institute to our department for electro-diagnostic evaluation. The study group comprised of 40 subjects (20 clinically diagnosed GBS patients and 20 healthy individuals as controls) aged between 6-65 years. Brain Stem evoked Potential (BAEP) were done in both groups using RMS EMG EP mark II machine. BAEP parameters included the latencies of waves I to IV, inter peak latencies I-III, III-IV & I-V. Statistically significant increase in absolute peak and inter peak latencies in the GBS group as compared with control group was noted. Results of evoked potential reflect impairment of auditory pathways probably due to focal demyelination in Schwann cell derived myelin sheaths that cover the extramedullary portion of auditory nerves. Early detection of the sub-clinical abnormalities is important as timely intervention reduces morbidity.

Keywords: brainstem, demyelination, evoked potential, Guillain Barre’

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Authors: Leonardo C. Pacheco-Londoño, Nataly J Galan-Freyle, Lisandro Pacheco-Lugo, Antonio Acosta-Hoyos, Elkin Navarro, Gustavo Aroca-Martinez, Karin Rondón-Payares, Alberto C. Espinosa-Garavito, Samuel P. Hernández-Rivera

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At the Life Science Research Center at Simon Bolivar University, a primary focus is the diagnosis of various diseases, and the use of gold nanoparticles (Au-NPs) in diverse biomedical applications is continually expanding. In the present study, Au-NPs were employed as substrates for Surface-Enhanced Raman Spectroscopy (SERS) aimed at diagnosing kidney diseases arising from Lupus Nephritis (LN), preeclampsia (PC), and Hypertension (H). Discrimination models were developed for distinguishing patients with and without kidney diseases based on the SERS signals from urine samples by partial least squares-discriminant analysis (PLS-DA). A comparative study of the Raman signals across the three conditions was conducted, leading to the identification of potential metabolite signals. Model performance was assessed through cross-validation and external validation, determining parameters like sensitivity and specificity. Additionally, a secondary analysis was performed using machine learning (ML) models, wherein different ML algorithms were evaluated for their efficiency. Models’ validation was carried out using cross-validation and external validation, and other parameters were determined, such as sensitivity and specificity; the models showed average values of 0.9 for both parameters. Additionally, it is not possible to highlight this collaborative effort involved two university research centers and two healthcare institutions, ensuring ethical treatment and informed consent of patient samples.

Keywords: SERS, Raman, PLS-DA, kidney diseases

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Authors: Joshua Loiselle

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This paper discusses the challenges engineering teams face in conducting Failure Modes and Effects Analysis (FMEA) studies. This paper focuses on the specific topic of improving the efficiency and effectiveness of FMEA studies. Modern economic needs and increased business competition require engineers to constantly develop newer and better solutions within shorter timeframes and tighter margins. In addition, documentation requirements for meeting standards/regulatory compliance and customer needs are becoming increasingly complex and verbose. Managing open actions and continuous improvement activities across all projects, product variations, and processes in addition to daily engineering tasks is cumbersome, time consuming, and is susceptible to errors, omissions, and non-conformances. FMEA studies are proven methods for improving products and processes while subsequently reducing engineering workload and improving machine and resource availability through a pre-emptive, systematic approach of identifying, analyzing, and improving high-risk components. If implemented correctly, FMEA studies significantly reduce costs and improve productivity. However, the value of an effective FMEA is often shrouded by a lack of clarity and structure, misconceptions, and previous experiences and, as such, FMEA studies are frequently grouped with the other required information and documented retrospectively in preparation of customer requirements or audits. Performing studies in this way only adds cost to a project and perpetuates the misnomer that FMEA studies are not value-added activities. This paper discusses the benefits of effective FMEA studies, the challenges related to conducting FMEA studies, best practices for efficiently overcoming challenges via structure and automation, and the benefits of implementing those practices.

Keywords: FMEA, quality, APQP, PPAP

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Authors: Agada N. Ihuoma, Nagata Yasunori

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Artificial intelligence (AI) techniques play a crucial role in predicting the expected energy outcome and its performance, analysis, modeling, and control of renewable energy. Renewable energy is becoming more popular for economic and environmental reasons. In the face of global energy consumption and increased depletion of most fossil fuels, the world is faced with the challenges of meeting the ever-increasing energy demands. Therefore, incorporating artificial intelligence to predict solar radiation outcomes from the intermittent sunlight is crucial to enable a balance between supply and demand of energy on loads, predict the performance and outcome of solar energy, enhance production planning and energy management, and ensure proper sizing of parameters when generating clean energy. However, one of the major problems of forecasting is the algorithms used to control, model, and predict performances of the energy systems, which are complicated and involves large computer power, differential equations, and time series. Also, having unreliable data (poor quality) for solar radiation over a geographical location as well as insufficient long series can be a bottleneck to actualization. To overcome these problems, this study employs the anaconda Navigator (Jupyter Notebook) for machine learning which can combine larger amounts of data with fast, iterative processing and intelligent algorithms allowing the software to learn automatically from patterns or features to predict the performance and outcome of Solar Energy which in turns enables the balance of supply and demand on loads as well as enhance production planning and energy management.

Keywords: artificial Intelligence, backward elimination, linear regression, solar energy

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Authors: Rasheed Amao Busari, Ahmed Ibrahim

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The engineering properties of giant palms are crucial for the reasonable design of the processing and handling systems. The research was conducted to investigate some engineering properties of giant palm seeds in relation to their oil potential. The ripe giant palm fruit was sourced from some parts of Zaria in Kaduna State and Ado Ekiti in Ekiti State, Nigeria. The mesocarps of the fruits collected were removed to obtain the nuts, while the collected nuts were dried under ambient conditions for several days. The actual moisture content of the nuts at the time of the experiment was determined using KT100S Moisture Meter, with moisture content ranged 17.9% to 19.15%. The physical properties determined are axial dimension, geometric mean diameter, arithmetic mean diameter, sphericity, true and bulk densities, porosity, angles of repose, and coefficients of friction. The nuts were measured using a vernier caliper for physical assessment of their sizes. The axial dimensions of 100 nuts were taken and the result shows that the size ranges from 7.30 to 9.32cm for major diameter, 7.2 to 8.9 cm for intermediate diameter, and 4.2 to 6.33 for minor diameter. The mechanical properties determined were compressive force, compressive stress, and deformation both at peak and break using Instron hydraulic universal tensile testing machine. The work also revealed that giant palm seed can be classified as an oil-bearing seed. The seed gave 18% using the solvent extraction method. The results obtained from the study will help in solving the problem of equipment design, handling, and further processing of the seeds.

Keywords: giant palm seeds, engineering properties, oil potential, moisture content, and giant palm fruit

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Authors: Edward Shizha, Edward Makwarimba

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This literature review explores current research on the educational strengths and barriers of newcomer and refugee youth in Canada. Canada’s shift in immigration policy in the past three decades, from Europe to Asian and African countries as source continents of recent immigrants to Canada, has tremendously increased the ethnic, linguistic, cultural and religious diversity of the population, including that of students in its education system. Over 18% of the country’s population was born in another country, of which 70% are visible minorities. There has been an increase in admitted immigrants and refugees, with a total of 226,203 between July 2020 and June 2021. Newcomer parents and their children in all major destination countries, including Canada, face tremendous challenges, including racism and discrimination, lack of English language skills, poverty, income inequality, unemployment, and underemployment. They face additional challenges, including discrimination against those who cannot speak the official languages, English or French. The severity of the challenges depends on several intersectional factors, including immigrant status (asylum seeker, refugee, or immigrant), age, gender, level of education and others. Through the lens of intersectionality as an explanatory perspective, this literature review examines the educational attainment and outcomes of newcomer and refugee youth in Canada in order to understand their educational needs, educational barriers and strengths. Newcomer youths’ experiences are shaped by numerous intersectional and interconnected sociocultural, sociopolitical, and socioeconomic factors—including gender, migration status, racialized status, ethnicity, socioeconomic class, sexual minority status, age, race—that produce and perpetuate their disadvantage. According to research, immigrants and refugees from visible minority ethnic backgrounds experience exclusions more than newcomers from other backgrounds and groups from the mainstream population. For many immigrant parents, migration provides financial and educational opportunities for their children. Yet, when attending school, newcomer and refugee youth face unique challenges related to racism and discrimination, negative attitudes and stereotypes from teachers and other school authorities, language learning and proficiency, differing levels of acculturation, and different cultural views of the role of parents in relation to teachers and school, and unfamiliarity with the social or school context in Canada. Recognizing discrepancies in educational attainment of newcomer and refugee youth based on their race and immigrant status, the paper develops insights into existing research and data gaps related to educational strengths and challenges for visible minority newcomer youth in Canada. The paper concludes that the educational successes or failures of the newcomer and refugee youth and their settlement and integration into the school system in Canada may depend on where their families settle, the attitudes of the host community and the school officials (teachers, guidance counsellors and school administrators) after-school support programs and their own set of coping mechanisms. Conceivably a unique approach to after-school programming should provide learning supports and opportunities that consider newcomer and refugee youth’s needs, experiences, backgrounds and circumstances. This support is likely to translate into significant academic and psychological well-being of newcomer students.

Keywords: deficit discourse, discrimination, educational outcomes, newcomer and refugee youth, racism, strength-based approach, whiteness

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Authors: Muna Khethier Abbass, Khairia Salman Hussan, Huda Mohummed AbdudAlaziz

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This work aims to study the effect of shot peening on the mechanical properties of welded joints which performed by two different welding processes: Tungsten inert gas (TIG) welding and friction stir welding (FSW) processes of aluminum alloy 6061 T6. Arc welding process (TIG) was carried out on the sheet with dimensions of (100x50x6 mm) to obtain many welded joints with using electrode type ER4043 (AlSi5) as a filler metal and argon as shielding gas. While the friction stir welding process was carried out using CNC milling machine with a tool of rotational speed (1000 rpm) and welding speed of (20 mm/min) to obtain the same butt welded joints. The welded pieces were tested by X-ray radiography to detect the internal defects and faulty welded pieces were excluded. Tensile test specimens were prepared from welded joints and base alloy in the dimensions according to ASTM17500 and then subjected to shot peening process using steel ball of diameter 0.9 mm and for 15 min. All specimens were subjected to Vickers hardness test and micro structure examination to study the effect of welding process (TIG and FSW) on the micro structure of the weld zones. Results showed that a general decay of mechanical properties of TIG and FSW welded joints comparing with base alloy while the FSW welded joint gives better mechanical properties than that of TIG welded joint. This is due to the micro structure changes during the welding process. It has been found that the surface hardening by shot peening improved the mechanical properties of both welded joints, this is due to the compressive residual stress generation in the weld zones which was measured using X-Ray diffraction (XRD) inspection.

Keywords: friction stir welding, TIG welding, mechanical properties, shot peening

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Authors: Deepak Singh, Rail Kuliev

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The abstract highlights the critical importance of optimizing data integration and management strategies in the upstream oil and gas industry. With its complex and dynamic nature generating vast volumes of data, efficient data integration and management are essential for informed decision-making, cost reduction, and maximizing operational performance. Challenges such as data silos, heterogeneity, real-time data management, and data quality issues are addressed, prompting the proposal of several strategies. These strategies include implementing a centralized data repository, adopting industry-wide data standards, employing master data management (MDM), utilizing real-time data integration technologies, and ensuring data quality assurance. Training and developing the workforce, “reskilling and upskilling” the employees and establishing robust Data Management training programs play an essential role and integral part in this strategy. The article also emphasizes the significance of data governance and best practices, as well as the role of technological advancements such as big data analytics, cloud computing, Internet of Things (IoT), and artificial intelligence (AI) and machine learning (ML). To illustrate the practicality of these strategies, real-world case studies are presented, showcasing successful implementations that improve operational efficiency and decision-making. In present study, by embracing the proposed optimization strategies, leveraging technological advancements, and adhering to best practices, upstream oil and gas companies can harness the full potential of data-driven decision-making, ultimately achieving increased profitability and a competitive edge in the ever-evolving industry.

Keywords: master data management, IoT, AI&ML, cloud Computing, data optimization

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