Search results for: virtual machine migration

Authors: Choy Sonny Yip Hong

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This abstract presents an exploration into the creation of a sustainable bio-polymer compound for additive manufacturing, specifically 3D printing, with a focus on eggshells and polylactide (PLA) polymer. The project initially conducted experiments using a variety of food by-products to create bio-polymers, and promising results were obtained when combining eggshells with PLA polymer. The research journey involved precise measurements, drying of PLA to remove moisture, and the utilization of a filament-making machine to produce 3D printable filaments. The project began with exploratory research and experiments, testing various combinations of food by-products to create bio-polymers. After careful evaluation, it was discovered that eggshells and PLA polymer produced promising results. The initial mixing of the two materials involved heating them just above the melting point. To make the compound 3D printable, the research focused on finding the optimal formulation and production process. The process started with precise measurements of the PLA and eggshell materials. The PLA was placed in a heating oven to remove any absorbed moisture. Handmade testing samples were created to guide the planning for 3D-printed versions. The scrap PLA was recycled and ground into a powdered state. The drying process involved gradual moisture evaporation, which required several hours. The PLA and eggshell materials were then placed into the hopper of a filament-making machine. The machine's four heating elements controlled the temperature of the melted compound mixture, allowing for optimal filament production with accurate and consistent thickness. The filament-making machine extruded the compound, producing filament that could be wound on a wheel. During the testing phase, trials were conducted with different percentages of eggshell in the PLA mixture, including a high percentage (20%). However, poor extrusion results were observed for high eggshell percentage mixtures. Samples were created, and continuous improvement and optimization were pursued to achieve filaments with good performance. To test the 3D printability of the DIY filament, a 3D printer was utilized, set to print the DIY filament smoothly and consistently. Samples were printed and mechanically tested using a universal testing machine to determine their mechanical properties. This testing process allowed for the evaluation of the filament's performance and suitability for additive manufacturing applications. In conclusion, the project explores the creation of a sustainable bio-polymer compound using eggshells and PLA polymer for 3D printing. The research journey involved precise measurements, drying of PLA, and the utilization of a filament-making machine to produce 3D printable filaments. Continuous improvement and optimization were pursued to achieve filaments with good performance. The project's findings contribute to the advancement of additive manufacturing, offering opportunities for design innovation, carbon footprint reduction, supply chain optimization, and collaborative potential. The utilization of eggshell PLA polymer in additive manufacturing has the potential to revolutionize the manufacturing industry, providing a sustainable alternative and enabling the production of intricate and customized products.

Keywords: additive manufacturing, 3D printing, eggshell PLA polymer, design innovation, carbon footprint reduction, supply chain optimization, collaborative potential

Procedia PDF Downloads 58

Authors: C. Silvestros, K. Mellou, T. Georgakopoulou, A. Koustenis, E. Kokkinou, C. Botsi, A. Terzidis

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'PHILOS – Emergency health response to refugee crisis' is a programme of the Greek Ministry of Health, implemented by the Hellenic Center for Disease Control and Prevention (HCDCP) funded by the Asylum, Migration and Integration Fund (AMIF) of EU’s DG Migration and Home Affairs. One of the main objectives of the program is the immunization coverage of the target – population to assure the prevention of vaccine-preventable diseases. The program foresees vaccination needs assessment of children hosted at camps at the mainland and implementation of interventions to cover the vaccination gaps in co-operation with the Ministry of Health. The National Immunization Advisory Committee in Greece recommended that MMR (Measles, Mumps, and Rubella), PCV (Pneumococcal conjugate vaccine) and HEXA (diphtheria, tetanus, pertussis, polio, hepatitis B and Haemophilus influenzae type b) vaccines should be performed in priority. Recording was completed at 24 camps (May - June 2017); 3381 children (0-18 years) were recorded. The median number of children hosted at each camp was 95 (range: 5-553). For 68% of the children, the WHO vaccination booklet was available. 44%, 48.5% and 61% of the children were vaccinated with at least one dose of PCV, HEXA, and MMR, respectively. The proportion of vaccinated children for the three vaccines mentioned above is significantly lower for the remaining doses; PCV (second dose 8%, third dose 1.3%), HEXA (second dose 13%, third dose 2.7%, forth dose 0.1%) and MMR (second dose 23%). None of the 37 (10 from Afghanistan, 3 from Bangladesh, 23 from Pakistan, 1 from Syria) recorded unaccompanied children did not have a WHO vaccination booklet and were considered unvaccinated. There is no differentiation in vaccination coverage among different ethnicities. Massive catch up vaccination was performed at 4 camps, and 671 vaccinations were performed (245 PCV, 307 HEXA, and 119 MMR). Similar interventions are planned for all camps of the country. Recording reveled gaps in vaccination coverage of the population, mainly because of the mobility of the population, the influx of refugees- which is still ongoing- and new births. Mass vaccination campaigns are considered vital in order to increase vaccination coverage, and continuous efforts are needed in order all children living at the camps to have full access to the National Childhood Immunization Program.

Keywords: vaccine preventable, refugee–migrants camps, vaccination coverage, PCV, MMR, HEXA

Procedia PDF Downloads 171

Authors: Marcel Chevalier, Léo Dupont, Sylvain Marié, Frédérique Roffet, Elena Stolyarova, William Templier, Costin Vasile

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Industrial end-users are currently facing an increasing need to reduce the risk of unexpected failures and optimize their maintenance. This calls for both short-term analysis and long-term ageing anticipation. At Schneider Electric, we tackle those two issues using both machine learning and first principles models. Machine learning models are incrementally trained from normal data to predict expected values and detect statistically significant short-term deviations. Ageing models are constructed by breaking down physical systems into sub-assemblies, then determining relevant degradation modes and associating each one to the right kinetic law. Validating such anomaly detection and maintenance models is challenging, both because actual incident and ageing data are rare and distorted by human interventions, and incremental learning depends on human feedback. To overcome these difficulties, we propose to simulate physics, systems, and humans -including asset maintenance operations- in order to validate the overall approaches in accelerated time and possibly choose between algorithmic alternatives.

Keywords: degradation models, ageing, anomaly detection, soft sensor, incremental learning

Procedia PDF Downloads 113

Authors: Sung-Jun Yoo, Kazuhide Ito

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In this study, a comprehensive computer simulated person (CSP) that integrates computational human model (virtual manikin) and respiratory tract model (virtual airway), was applied for estimation of indoor environmental quality. Moreover, an inclusive prediction method was established by integrating computational fluid dynamics (CFD) analysis with advanced CSP which is combined with physiologically-based pharmacokinetic (PBPK) model, unsteady thermoregulation model for analysis targeting micro-climate around human body and respiratory area with high accuracy. This comprehensive method can estimate not only the contaminant inhalation but also constant interaction in the contaminant transfer between indoor spaces, i.e., a target area for indoor air quality (IAQ) assessment, and respiratory zone for health risk assessment. This study focused on the usage of the CSP as an air/thermal quality sensor in indoors, which means the application of comprehensive model for assessment of IAQ and thermal environmental quality. Demonstrative analysis was performed in order to examine the applicability of the comprehensive model to the heating, ventilation, air conditioning (HVAC) control scheme. CSP was located at the center of the simple model room which has dimension of 3m×3m×3m. Formaldehyde which is generated from floor material was assumed as a target contaminant, and flow field, sensible/latent heat and contaminant transfer analysis in indoor space were conducted by using CFD simulation coupled with CSP. In this analysis, thermal comfort was evaluated by thermoregulatory analysis, and respiratory exposure risks represented by adsorption flux/concentration at airway wall surface were estimated by PBPK-CFD hybrid analysis. These Analysis results concerning IAQ and thermal comfort will be fed back to the HVAC control and could be used to find a suitable ventilation rate and energy requirement for air conditioning system.

Keywords: CFD simulation, computer simulated person, HVAC control, indoor environmental quality

Procedia PDF Downloads 350

Authors: Jayanta Kumar Ghosh, Swastik Sunil Goriwale, Himangshu Sarkar

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Groundwater is one of the most valuable natural resources that society consumes for its domestic, industrial, and agricultural water supply. Its bulk and indiscriminate consumption affects the groundwater resource. Often, it has been found that the groundwater recharge rate is much lower than its demand. Thus, to maintain water and food security, it is necessary to monitor and management of groundwater storage. However, it is challenging to estimate groundwater storage (GWS) by making use of existing hydrological models. To overcome the difficulties, machine learning (ML) models are being introduced for the evaluation of groundwater level (GWL). Thus, the objective of this research work is to develop an ML-based model for the prediction of GWL. This objective has been realized through the development of an artificial neural network (ANN) model based on hydro-gravimetry. The model has been developed using training samples from field observations spread over 8 months. The developed model has been tested for the prediction of GWL in an observation well. The root means square error (RMSE) for the test samples has been found to be 0.390 meters. Thus, it can be concluded that the hydro-gravimetric-based ANN model can be used for the prediction of GWL. However, to improve the accuracy, more hydro-gravimetric parameter/s may be considered and tested in future.

Keywords: machine learning, hydro-gravimetry, ground water level, predictive model

Procedia PDF Downloads 111

Authors: Chularat Leewuttanakul, Khanitta Ruttarattanamongkol, Sasivimon Chittrakorn

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Rice during grain development stage is a rich source of many bioactive compounds. Dough stage rice contains high amounts of photochemical and can be used for rice milling industries. However, rice grain at dough stage had low milling quality due to high moisture content. Thermal processing can be applied to rice grain for improving milled rice yield. This experiment was conducted to study the chemical and physic properties of dough stage rice grain after roasting treatment. Rice were roasted with two different methods including traditional pan roasting at 140 °C for 60 minutes and using the electrical roasting machine at 140 °C for 30, 40, and 50 minutes. The chemical, physical properties, and bioactive compounds of brown rice and milled rice were evaluated. The result of this experiment showed that moisture content of brown and milled rice was less than 10 % and amylose contents were in the range of 26-28 %. Rice grains roasting for 30 min using electrical roasting machine had high head rice yield and length and breadth of grain after milling were close to traditional pan roasting (p > 0.05). The lightness (L*) of rice did not affect by roasting treatment (p > 0.05) and the a* indicated the yellowness of milled rice was lower than brown rice. The bioactive compounds of brown and milled rice significantly decreased with increasing of drying time. Brown rice roasted for 30 minutes had the highest of total phenolic content, antioxidant activity, α-tocopherol, and ɤ-oryzanol content. Volume expansion and elongation of cooked rice decreased as roasting time increased and quality of cooked rice roasted for 30 min was comparable to traditional pan roasting. Hardness of cooked rice as measured by texture analyzer increased with increasing roasting time. The results indicated that rice grains at dough stage, containing a high amount of bioactive compounds, have a great potential for rice milling industries and the electrical roasting machine can be used as an alternative to pan roasting which decreases processing time and labor costs.

Keywords: bioactive compounds, cooked rice, dough stage rice grain, grain development, roasting

Procedia PDF Downloads 146

Authors: Soheila Sadeghi

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In the dynamic landscape of project management, scope changes are an inevitable reality that can significantly impact project performance. These changes, whether initiated by stakeholders, external factors, or internal project dynamics, can lead to cost overruns and schedule delays. Accurately predicting the consequences of these changes is crucial for effective project control and informed decision-making. This study aims to develop predictive models to estimate the impact of scope changes on project cost and schedule using machine learning techniques. The research utilizes a comprehensive dataset containing detailed information on project tasks, including the Work Breakdown Structure (WBS), task type, productivity rate, estimated cost, actual cost, duration, task dependencies, scope change magnitude, and scope change timing. Multiple machine learning models are developed and evaluated to predict the impact of scope changes on project cost and schedule. These models include Linear Regression, Decision Tree, Ridge Regression, Random Forest, Gradient Boosting, and XGBoost. The dataset is split into training and testing sets, and the models are trained using the preprocessed data. Cross-validation techniques are employed to assess the robustness and generalization ability of the models. The performance of the models is evaluated using metrics such as Mean Squared Error (MSE) and R-squared. Residual plots are generated to assess the goodness of fit and identify any patterns or outliers. Hyperparameter tuning is performed to optimize the XGBoost model and improve its predictive accuracy. The feature importance analysis reveals the relative significance of different project attributes in predicting the impact on cost and schedule. Key factors such as productivity rate, scope change magnitude, task dependencies, estimated cost, actual cost, duration, and specific WBS elements are identified as influential predictors. The study highlights the importance of considering both cost and schedule implications when managing scope changes. The developed predictive models provide project managers with a data-driven tool to proactively assess the potential impact of scope changes on project cost and schedule. By leveraging these insights, project managers can make informed decisions, optimize resource allocation, and develop effective mitigation strategies. The findings of this research contribute to improved project planning, risk management, and overall project success.

Keywords: cost impact, machine learning, predictive modeling, schedule impact, scope changes

Procedia PDF Downloads 17

Authors: Marzieh Karimihaghighi, Carlos Castillo

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This work studies how Machine Learning (ML) may be used to increase the effectiveness of a criminal recidivism risk assessment tool, RisCanvi. The two key dimensions of this analysis are predictive accuracy and algorithmic fairness. ML-based prediction models obtained in this study are more accurate at predicting criminal recidivism than the manually-created formula used in RisCanvi, achieving an AUC of 0.76 and 0.73 in predicting violent and general recidivism respectively. However, the improvements are small, and it is noticed that algorithmic discrimination can easily be introduced between groups such as national vs foreigner, or young vs old. It is described how effectiveness and algorithmic fairness objectives can be balanced, applying a method in which a single error disparity in terms of generalized false positive rate is minimized, while calibration is maintained across groups. Obtained results show that this bias mitigation procedure can substantially reduce generalized false positive rate disparities across multiple groups. Based on these results, it is proposed that ML-based criminal recidivism risk prediction should not be introduced without applying algorithmic bias mitigation procedures.

Keywords: algorithmic fairness, criminal risk assessment, equalized odds, recidivism

Procedia PDF Downloads 136

Authors: Niyati Sharma, Om Anand, Sanjeev Kumar Prasad

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Assuming future sale value for any of the organizations is one of the major essential characteristics of tactical development. Walmart Sales Forecasting is the finest illustration to work with as a beginner; subsequently, it has the major retail data set. Walmart uses this sales estimate problem for hiring purposes also. We would like to analyzing how the internal and external effects of one of the largest companies in the US can walk out their Weekly Sales in the future. Demand forecasting is the planned prerequisite of products or services in the imminent on the basis of present and previous data and different stages of the market. Since all associations is facing the anonymous future and we do not distinguish in the future good demand. Hence, through exploring former statistics and recent market statistics, we envisage the forthcoming claim and building of individual goods, which are extra challenging in the near future. As a result of this, we are producing the required products in pursuance of the petition of the souk in advance. We will be using several machine learning models to test the exactness and then lastly, train the whole data by Using linear regression and fitting the training data into it. Accuracy is 8.88%. The extra trees regression model gives the best accuracy of 97.15%.

Keywords: random forest algorithm, linear regression algorithm, extra trees classifier, mean absolute error

Procedia PDF Downloads 129

Authors: Sigal Portnoy, Jason Friedman, Eitan Raveh

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Introduction: Sensory substitution is possible due to the capacity of our brain to adapt to information transmitted by a synthetic receptor via an alternative sensory system. Practical sensory substitution systems are being developed in order to increase the functionality of individuals with sensory loss, e.g. amputees. For upper limb prosthetic-users the loss of tactile feedback compels them to allocate visual attention to their prosthesis. The effect of adding vibrotactile feedback (VTF) to the applied force has been studied, however its effect on the allocation if visual attention during dual-tasking and the response during misleading visual feedback have not been studied. We hypothesized that VTF will improve the performance and reduce visual attention during dual-task assignments in healthy individuals using a robotic hand and improve the performance in a standardized functional test, despite the presence of misleading visual feedback. Methods: For the dual-task paradigm, twenty healthy subjects were instructed to toggle two keyboard arrow keys with the left hand to retain a moving virtual car on a road on a screen. During the game, instructions for various activities, e.g. mix the sugar in the glass with a spoon, appeared on the screen. The subject performed these tasks with a robotic hand, attached to the right hand. The robotic hand was controlled by the activity of the flexors and extensors of the right wrist, recorded using surface EMG electrodes. Pressure sensors were attached at the tips of the robotic hand and induced VTF using vibrotactile actuators attached to the right arm of the subject. An eye-tracking system tracked to visual attention of the subject during the trials. The trials were repeated twice, with and without the VTF. Additionally, the subjects performed the modified box and blocks, hidden from eyesight, in a motion laboratory. A virtual presentation of a misleading visual feedback was be presented on a screen so that twice during the trial, the virtual block fell while the physical block was still held by the subject. Results: This is an ongoing study, which current results are detailed below. We are continuing these trials with transradial myoelectric prosthesis-users. In the healthy group, the VTF did not reduce the visual attention or improve performance during dual-tasking for the tasks that were typed transfer-to-target, e.g. place the eraser on the shelf. An improvement was observed for other tasks. For example, the average±standard deviation of time to complete the sugar-mixing task was 13.7±17.2s and 19.3±9.1s with and without the VTF, respectively. Also, the number of gaze shifts from the screen to the hand during this task were 15.5±23.7 and 20.0±11.6, with and without the VTF, respectively. The response of the subjects to the misleading visual feedback did not differ between the two conditions, i.e. with and without VTF. Conclusions: Our interim results suggest that the performance of certain activities of daily living may be improved by VTF. The substitution of visual sensory input by tactile feedback might require a long training period so that brain plasticity can occur and allow adaptation to the new condition.

Keywords: prosthetics, rehabilitation, sensory substitution, upper limb amputation

Procedia PDF Downloads 326

Authors: Radhika Ranjan Roy

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Deployment of machine learning (ML)/deep learning (DL) algorithms for cyberattack detection in operational communications networks (wireless and/or wire-line) is being delayed because of low-performance parameters (e.g., recall, precision, and f₁-score). If datasets become imbalanced, which is the usual case for communications networks, the performance tends to become worse. Complexities in handling reducing dimensions of the feature sets for increasing performance are also a huge problem. Mahalanobis algorithms have been widely applied in scientific research because Mahalanobis distance metric learning is a successful framework. In this paper, we have investigated the Mahalanobis binary classifier algorithm for increasing cyberattack detection performance over communications networks as a proof of concept. We have also found that high-dimensional information in intermediate features that are not utilized as much for classification tasks in ML/DL algorithms are the main contributor to the state-of-the-art of improved performance of the Mahalanobis method, even for imbalanced and sparse datasets. With no feature reduction, MD offers uniform results for precision, recall, and f₁-score for unbalanced and sparse NSL-KDD datasets.

Keywords: Mahalanobis distance, machine learning, deep learning, NS-KDD, local intrinsic dimensionality, chi-square, positive semi-definite, area under the curve

Procedia PDF Downloads 60

Authors: Velimir "monty" Vesselinov, Trais Kliplhuis, Hope Jasperson

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Artificial intelligence (AI) is a powerful tool in the geothermal energy sector, aiding in both exploration and utilization. Identifying promising geothermal sites can be challenging due to limited surface indicators and the need for expensive drilling to confirm subsurface resources. Geothermal reservoirs can be located deep underground and exhibit complex geological structures, making traditional exploration methods time-consuming and imprecise. AI algorithms can analyze vast datasets of geological, geophysical, and remote sensing data, including satellite imagery, seismic surveys, geochemistry, geology, etc. Machine learning algorithms can identify subtle patterns and relationships within this data, potentially revealing hidden geothermal potential in areas previously overlooked. To address these challenges, a SIML (Science-Informed Machine Learning) technology has been developed. SIML methods are different from traditional ML techniques. In both cases, the ML models are trained to predict the spatial distribution of an output (e.g., pressure, temperature, heat flux) based on a series of inputs (e.g., permeability, porosity, etc.). The traditional ML (a) relies on deep and wide neural networks (NNs) based on simple algebraic mappings to represent complex processes. In contrast, the SIML neurons incorporate complex mappings (including constitutive relationships and physics/chemistry models). This results in ML models that have a physical meaning and satisfy physics laws and constraints. The prototype of the developed software, called GeoTGO, is accessible through the cloud. Our software prototype demonstrates how different data sources can be made available for processing, executed demonstrative SIML analyses, and presents the results in a table and graphic form.

Keywords: science-informed machine learning, artificial inteligence, exploration, utilization, hidden geothermal

Procedia PDF Downloads 26

Authors: Michael Stewart

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Mutation testing is a type of software testing proposed in the 1970s where program statements are deliberately changed to introduce simple errors so that test cases can be validated to determine if they can detect the errors. Test cases are executed against the mutant code to determine if one fails, detects the error and ensures the program is correct. One major issue with this type of testing was it became intensive computationally to generate and test all possible mutations for complex programs. This paper used reinforcement learning and parallel processing within the context of mutation testing for the selection of mutation operators and test cases that reduced the computational cost of testing and improved test suite effectiveness. Experiments were conducted using sample programs to determine how well the reinforcement learning-based algorithm performed with one live mutation, multiple live mutations and no live mutations. The experiments, measured by mutation score, were used to update the algorithm and improved accuracy for predictions. The performance was then evaluated on multiple processor computers. With reinforcement learning, the mutation operators utilized were reduced by 50 – 100%.

Keywords: automated-testing, machine learning, mutation testing, parallel processing, reinforcement learning, software engineering, software testing

Procedia PDF Downloads 178

Authors: Ibrahim Khan, Waqas Khalid

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The Healthcare sector is known to consume a higher proportion of total energy consumption in the HVAC market owing to an excessive cooling and heating requirement in maintaining human thermal comfort in indoor conditions, catering to patients undergoing treatment in hospital wards, rooms, and intensive care units. The indoor thermal comfort conditions in selected hospitals of Islamabad, Pakistan, were measured on a real-time basis with the collection of first-hand experimental data using calibrated sensors measuring Ambient Temperature, Wet Bulb Globe Temperature, Relative Humidity, Air Velocity, Light Intensity and CO2 levels. The Experimental data recorded was analyzed in conjunction with the Thermal Comfort Questionnaire Surveys, where the participants, including patients, doctors, nurses, and hospital staff, were assessed based on their thermal sensation, acceptability, preference, and comfort responses. The Recorded Dataset, including experimental and survey-based responses, was further analyzed in the development of a correlation between operative temperature, operative relative humidity, and other measured operative parameters with the predicted mean vote and adaptive predicted mean vote, with the adaptive temperature and adaptive relative humidity estimated using the seasonal data set gathered for both summer – hot and dry, and hot and humid as well as winter – cold and dry, and cold and humid climate conditions. The Machine Learning Logistic Regression Algorithm was incorporated to train the operative experimental data parameters and develop a correlation between patient sensations and the thermal environmental parameters for which a new ML-based adaptive thermal comfort model was proposed and developed in our study. Finally, the accuracy of our model was determined using the K-fold cross-validation.

Keywords: predicted mean vote, thermal comfort, energy management, logistic regression, machine learning

Procedia PDF Downloads 43

Authors: T. Praveenkumar, Kulpreet Singh, Divy Bhanpuriya, M. Saimurugan

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This study analysed the classification accuracy for gearbox faults using Machine Learning Techniques. Gearboxes are widely used for mechanical power transmission in rotating machines. Its rotating components such as bearings, gears, and shafts tend to wear due to prolonged usage, causing fluctuating vibrations. Increasing the dependability of mechanical components like a gearbox is hampered by their sealed design, which makes visual inspection difficult. One way of detecting impending failure is to detect a change in the vibration signature. The current study proposes various machine learning algorithms, with aid of these vibration signals for obtaining the fault classification accuracy of an automotive 4-Speed synchromesh gearbox. Experimental data in the form of vibration signals were acquired from a 4-Speed synchromesh gearbox using Data Acquisition System (DAQs). Statistical features were extracted from the acquired vibration signal under various operating conditions. Then the extracted features were given as input to the algorithms for fault classification. Supervised Machine Learning algorithms such as Support Vector Machines (SVM) and unsupervised algorithms such as Deep Feed Forward Neural Network (DFFNN), Deep Belief Networks (DBN) algorithms are used for fault classification. The fusion of DBN & DFFNN classifiers were architected to further enhance the classification accuracy and to reduce the computational complexity. The fault classification accuracy for each algorithm was thoroughly studied, tabulated, and graphically analysed for fused and individual algorithms. In conclusion, the fusion of DBN and DFFNN algorithm yielded the better classification accuracy and was selected for fault detection due to its faster computational processing and greater efficiency.

Keywords: deep belief networks, DBN, deep feed forward neural network, DFFNN, fault diagnosis, fusion of algorithm, vibration signal

Procedia PDF Downloads 98

Authors: Lucas Paganin, Viliam Makis

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With the advent of globalization, the market competition has become a major issue for most companies. One of the main strategies to overcome this situation is the quality improvement of the product at a lower cost to meet customers’ expectations. In order to achieve the desired quality of products, it is important to control the process to meet the specifications, and to implement the optimal maintenance policy for the machines and the production lines. Thus, the overall objective is to reduce process variation and the production and maintenance costs. In this paper, an integrated model involving Statistical Process Control (SPC) and maintenance is developed to achieve this goal. Therefore, the main focus of this paper is to develop the jointly optimal maintenance and statistical process control policy minimizing the total long run expected average cost per unit time. In our model, the production process can go out of control due to either the deterioration of equipment or other assignable causes. The equipment is also subject to failures in any of the operating states due to deterioration and aging. Hence, the process mean is controlled by an Xbar control chart using equidistant sampling epochs. We assume that the machine inspection epochs are the times when the control chart signals an out-of-control condition, considering both true and false alarms. At these times, the production process will be stopped, and an investigation will be conducted not only to determine whether it is a true or false alarm, but also to identify the causes of the true alarm, whether it was caused by the change in the machine setting, by other assignable causes, or by both. If the system is out of control, the proper actions will be taken to bring it back to the in-control state. At these epochs, a maintenance action can be taken, which can be no action, or preventive replacement of the unit. When the equipment is in the failure state, a corrective maintenance action is performed, which can be minimal repair or replacement of the machine and the process is brought to the in-control state. SMDP framework is used to formulate and solve the joint control problem. Numerical example is developed to demonstrate the effectiveness of the control policy.

Keywords: maintenance, semi-Markov decision process, statistical process control, Xbar control chart

Procedia PDF Downloads 76

Authors: Crysovaladou Kefaloudi, Kassiani Mellou, Eirini Saranti-Papasaranti, Athanasios Koustenis, Chrysoula Botsi, Agapios Terzidis

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Recent migration crisis led to a vast migrant – refugees movement to Greece which created an urgent need for hosting settlements. Taken into account the protection of public health from possible pathogens related to water and food supply as well as waste and sewage accumulation, a 'Living Conditions Recording Form' was created in the context of 'PHILOS' European Program funded by the Asylum Migration and Integration Fund (AMIF) of EU’s DG Migration and Home Affairs, in order to assess a number of environmental sanitation parameters, in refugees – migrants camps in mainland. The assessment will be completed until the end of July. From March to June 2017, mobile unit teams comprised of health inspectors of sub-action 2 of “PHILOS” proceeded with the assessment of living conditions in twenty-two out of thirty-one camps and 'Stata' was used for the statistical analysis of obtained information. Variables were grouped into the following categories: 1) Camp administration, 2) hosted population number, 3) accommodation, 4) heating installations, 5) personal hygiene, 6) sewage collection and disposal, 7) water supply, 8) waste collection and management, 9) pest control, 10) fire safety, 11) food handling and safety. Preliminary analysis of the results showed that camp administration was performed in 90% of the camps by a public authority with the coordination of various NGOs. The median number of hosted population was 222 ranging from 62 to 3200, and the median value of hosted population per accommodation type was 4 in 19 camps. Heating facilities were provided in 86.1% of camps. In 18.2 % of the camps, one personal hygiene facility was available per 6 people ranging in the rest of the camps from 1 per 3 to 1 per 20 hosted refugees-migrants. Waste and sewage collection was performed depending on populations demand in an adequate way in all recorded camps. In 90% of camps, water was supplied through the central water supply system. In 85% of camps quantity and quality of water supply inside camps was regularly monitored for microbial and chemical indices. Pest control was implemented in 86.4% of the camps as well as fire safety measures. Food was supplied by catering companies in 50% of the camps, and the quality and quantity food was monitored at a regular basis. In 77% of camps, food was prepared by the hosted population with the availability of proper storage conditions. Furthermore, in all camps, hosted population was provided with personal hygiene items and health sanitary educational programs were implemented in 77.3% of camps. In conclusion, in the majority of the camps, environmental sanitation parameters were satisfactory. However, waste and sewage accumulation, as well as inadequate pest control measures were recorded in some camps. The obtained data have led to a number of recommendations for the improvement of sanitary conditions, disseminated to all relevant stakeholders. Special emphasis was given to hygiene measures implementation during food handling by migrants – refugees, as well as to waste and sewage accumulation taking in to account the population’s cultural background.

Keywords: environmental sanitation parameters, food borne diseases risk assessment, refugee – migrants camps, water borne diseases risk assessment

Procedia PDF Downloads 210

Authors: Till Gramberg

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In the dynamic world of machine and plant engineering, stagnation in the growth of new product sales compels companies to reconsider their business models. The increasing shift toward service orientation, known as "servitization," along with challenges posed by digitalization and sustainability, necessitates an adaptation of product portfolio management (PPM). Against this backdrop, this study investigates the current challenges and requirements of PPM in this industrial context and develops a framework for the application of generative artificial intelligence (AI) to enhance agility and efficiency in PPM processes. The research approach of this study is based on a mixed-method design. Initially, qualitative interviews with industry experts were conducted to gain a deep understanding of the specific challenges and requirements in PPM. These interviews were analyzed using the Gioia method, painting a detailed picture of the existing issues and needs within the sector. This was complemented by a quantitative online survey. The combination of qualitative and quantitative research enabled a comprehensive understanding of the current challenges in the practical application of machine and plant engineering PPM. Based on these insights, a specific framework for the application of generative AI in PPM was developed. This framework aims to assist companies in implementing faster and more agile processes, systematically integrating dynamic requirements from trends such as digitalization and sustainability into their PPM process. Utilizing generative AI technologies, companies can more quickly identify and respond to trends and market changes, allowing for a more efficient and targeted adaptation of the product portfolio. The study emphasizes the importance of an agile and reactive approach to PPM in a rapidly changing environment. It demonstrates how generative AI can serve as a powerful tool to manage the complexity of a diversified and continually evolving product portfolio. The developed framework offers practical guidelines and strategies for companies to improve their PPM processes by leveraging the latest technological advancements while maintaining ecological and social responsibility. This paper significantly contributes to deepening the understanding of the application of generative AI in PPM and provides a framework for companies to manage their product portfolios more effectively and adapt to changing market conditions. The findings underscore the relevance of continuous adaptation and innovation in PPM strategies and demonstrate the potential of generative AI for proactive and future-oriented business management.

Keywords: servitization, product portfolio management, generative AI, disruptive innovation, machine and plant engineering

Procedia PDF Downloads 57

Authors: B. Remya Mohan, Kamal Bijlani, R. Jayakrishnan

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Visualization in an educational context provides the learner with visual means of information. Conceptualizing certain circumstances such as consequences of smoking can be done more effectively with the help of the technology, Augmented Reality (AR). It is a new methodology for effective learning. This paper proposes an approach on how AR based on Marker Technology simulates the harmful effects of smoking and its consequences using Unity 3D game engine. The study also illustrates the impact of AR technology on students for better learning. AR technology can be used as a method to improve learning.

Keywords: augmented reality, marker technology, multi-platform, virtual buttons

Procedia PDF Downloads 564

Authors: Stefanie J. Huber, Tobias Schmidt

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Cross-country differences in homeownership rates are large and very persistent over time, ranging between 35% in Switzerland to 80% in Spain. In this project, we test the hypothesis that these cross-country differences are driven by cultural tastes. To isolate the effect of culture from the effects of institutions and economic factors, we investigate the homeownership attitudes of second-generation immigrants in the United States. We find robust evidence that cross-country differences in cultural preferences are an important explanatory factor for the observed persistent differences in homeownership rates across countries.

Keywords: housing markets, homeownership rates, country heterogeneity, preferences, cultural transmission, migration

Procedia PDF Downloads 261

Authors: Yuntao Liu, Lei Wang, Haoran Xia

Abstract:

Machine learning has shown extensive applications in the development of classification models for autism spectrum disorder (ASD) using neural image data. This paper proposes a fusion multi-modal classification network based on a graph neural network. First, the brain is segmented into 116 regions of interest using a medical segmentation template (AAL, Anatomical Automatic Labeling). The image features of sMRI and the signal features of fMRI are extracted, which build the node and edge embedding representations of the brain map. Then, we construct a dynamically updated brain map neural network and propose a method based on a dynamic brain map adjacency matrix update mechanism and learnable graph to further improve the accuracy of autism diagnosis and recognition results. Based on the Autism Brain Imaging Data Exchange I dataset(ABIDE I), we reached a prediction accuracy of 74% between ASD and TD subjects. Besides, to study the biomarkers that can help doctors analyze diseases and interpretability, we used the features by extracting the top five maximum and minimum ROI weights. This work provides a meaningful way for brain disorder identification.

Keywords: autism spectrum disorder, brain map, supervised machine learning, graph network, multimodal data, model interpretability

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Authors: Weiwei Cui, Dejian Lin, Leigang Zhang, Yao Wang, Zheng Sun, Lianfeng Li

Abstract:

Built-in test (BIT) is an important technology in testability field, and it is widely used in state monitoring and fault diagnosis. With the improvement of modern equipment performance and complexity, the scope of BIT becomes larger, and it leads to the emergence of false alarm problem. The false alarm makes the health assessment unstable, and it reduces the effectiveness of BIT. The conventional false alarm suppression methods such as repeated test and majority voting cannot meet the requirement for a complicated system, and the intelligence algorithms such as artificial neural networks (ANN) are widely studied and used. However, false alarm has a very low frequency and small sample, yet a method based on ANN requires a large size of training sample. To recognize the false alarm, we propose a method based on multi-classification support vector machine (SVM) in this paper. Firstly, we divide the state of a system into three states: healthy, false-alarm, and faulty. Then we use multi-classification with '1 vs 1' policy to train and recognize the state of a system. Finally, an example of fault injection system is taken to verify the effectiveness of the proposed method by comparing ANN. The result shows that the method is reasonable and effective.

Keywords: false alarm, fault diagnosis, SVM, k-means, BIT

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Authors: Piotr Synaszko, Michał Sałaciński, Andrzej Leski

Abstract:

This paper describes the analysis of construction Mi-2 helicopter rotor blades in order to determine the causes of appearance disbonds. Authors describe construction of rotor blade with impact on bonded joins and areas of water migration. They also made analysis which determines possibility of disbond between critical parts of rotor blades based on more than one hundred non-destructive inspections results. They showed which parts of the blades most likely to damage. The main source of damage is water presence.

Keywords: disbonds, environmental effect, helicopter rotor blades, service life extension

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Authors: R. Ramesh, K. K. Shivaraman

Abstract:

The main aim of this paper is to handle the energy requirement in an efficient manner by merging the advanced digital communication and control technologies for smart grid applications. In order to reduce user home load during peak load hours, utility applies several incentives such as real-time pricing, time of use, demand response for residential customer through smart meter. However, this method provides inconvenience in the sense that user needs to respond manually to prices that vary in real time. To overcome these inconvenience, this paper proposes a convolutional neural network (CNN) with k-means clustering machine learning model which have ability to forecast energy requirement in short term, i.e., hour of the day or day of the week. By integrating our proposed technique with home energy management based on Bluetooth low energy provides predicted value to user for scheduling appliance in advanced. This paper describes detail about CNN configuration and k-means clustering algorithm for short-term energy forecasting.

Keywords: convolutional neural network, fuzzy logic, k-means clustering approach, smart home energy management

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Authors: M. H. Arslan, U. K. Sahin, H. Acikgoz-Tufan, I. Gocek, I. Erdem

Abstract:

Various exterior factors have perpetual effects on textile materials during wear, use and laundering in everyday life. In accordance with their frequency of use, textile materials are required to be laundered at certain intervals. The medium in which the laundering process takes place have inevitable detrimental physical and chemical effects on textile materials caused by the unique parameters of the process inherently existing. Connatural structures of various textile materials result in many different physical, chemical and mechanical characteristics. Because of their specific structures, these materials have different behaviors against several exterior factors. By modeling the behavior of commercial and test textiles as group-wise against laundering process, it is possible to disclose the relation in between these two groups of materials, which will lead to better understanding of their behaviors in terms of similarities and differences against the washing parameters of the laundering. Thus, the goal of the current research is to examine the behavior of two groups of textile materials as commercial textiles and as test textiles towards the main washing machine parameters during laundering process such as temperature, load quantity, mechanical action and level of water amount by concentrating on shrinkage, pilling, sewing defects, collar abrasion, the other defects other than sewing, whitening and overall properties of textiles. In this study, cotton fabrics were preferred as commercial textiles due to the fact that garments made of cotton are the most demanded products in the market by the textile consumers in daily life. Full factorial experimental set-up was used to design the experimental procedure. All profiles always including all of the commercial and the test textiles were laundered for 20 cycles by commercial home laundering machine to investigate the effects of the chosen parameters. For the laundering process, a modified version of ‘‘IEC 60456 Test Method’’ was utilized. The amount of detergent was altered as 0.5% gram per liter depending on varying load quantity levels. Datacolor 650®, EMPA Photographic Standards for Pilling Test and visual examination were utilized to test and characterize the textiles. Furthermore, in the current study the relation in between commercial and test textiles in terms of their performance was deeply investigated by the help of statistical analysis performed by MINITAB® package program modeling their behavior against the parameters of the laundering process. In the experimental work, the behaviors of both groups of textiles towards washing machine parameters were visually and quantitatively assessed in dry state.

Keywords: behavior against washing machine parameters, performance evaluation of textiles, statistical analysis, commercial and test textiles

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Authors: Haikal Achmad Thaha, Pujo Laksono, Dhamma Nibbana Putra

Abstract:

Customer service is one of the core interest in a service sector of a company, whether as the core business or as service part of the operation. Most of the time, the people and the previous research in service industry is focused on finding the best business model solution for the service sector, usually to decide between total in house customer service, outsourcing, or something in between. Conventionally, to take this decision is some important part of the management job, and this is a process that usually takes some time and staff effort, meanwhile market condition and overall company needs may change and cause loss of income and temporary disturbance in the companies operation . However, in this paper we have offer a new concept model to assist decision making process in service industry. This model will featured information platform as central tool to integrate service industry operation. The result is service information model which would ideally increase response time and effectivity of the decision making. it will also help service industry in switching the service solution system quickly through machine learning when the companies growth and the service solution needed are changing.

Keywords: service industry, customer service, machine learning, decision making, information platform

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Authors: Ayu Nabila Kusuma Pradana, Aprinaldi Jasa Mantau, Tomohiko Akahoshi

Abstract:

The incidence of Disseminated intravascular coagulation (DIC) following gastrointestinal surgery has a poor prognosis. Therefore, it is important to determine the factors that can predict the prognosis of DIC. This study will investigate the factors that may influence the outcome of DIC in patients after gastrointestinal surgery. Eighty-one patients were admitted to the intensive care unit after gastrointestinal surgery in Kyushu University Hospital from 2003 to 2021. Acute DIC scores were estimated using the new Japanese Association for Acute Medicine (JAAM) score from before and after surgery from day 1, day 3, and day 7. Acute DIC scores will be compared with The Sequential Organ Failure Assessment (SOFA) score, platelet count, lactate level, and a variety of biochemical parameters. This study applied machine learning algorithms to predict the prognosis of DIC after gastrointestinal surgery. The results of this study are expected to be used as an indicator for evaluating patient prognosis so that it can increase life expectancy and reduce mortality from cases of DIC patients after gastrointestinal surgery.

Keywords: the survival rate, gastrointestinal surgery, JAAM score, neural network, machine learning, disseminated intravascular coagulation (DIC)

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Authors: J. Congalton, K. Logan, B. Crump

Abstract:

Software is a critical aspect of modern life. However it is costly to develop and industry initiatives have focused on reducing costs and improving the productivity. Increasing, software is being developed in teams, and with greater globalization and migration, the teams are becoming more ethnically diverse. This study investigated whether diversity in terms of ethnicity impacted on the productivity of software development. Project managers of software development teams were interviewed. The study found that while some issues did exist due to language problems, when project managers created an environment of trust and friendliness, diversity made a positive contribution to productivity.

Keywords: diversity, project management, software development, team work

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Authors: Man Fung Ho, Lap So, Jiaqi Zhang, Yuheng Zhao, Huiyang Lu, Tat Shing Choi, K. Y. Michael Wong

Abstract:

Nowadays, a tremendous amount of data is available in the transportation system, enabling the development of various machine learning approaches to make short-term latency predictions. A natural question is then the choice of relevant information to enable accurate predictions. Using traffic data collected from the Taiwan Freeway System, we consider the prediction of short-term latency of a freeway segment with a length of 17 km covering 5 measurement points, each collecting vehicle-by-vehicle data through the electronic toll collection system. The processed data include the past latencies of the freeway segment with different time lags, the traffic conditions of the individual segments (the accumulations, the traffic fluxes, the entrance and exit rates), the total accumulations, and the weekday latency profiles obtained by Gaussian process regression of past data. We arrive at several important conclusions about how data should be refined to obtain accurate predictions, which have implications for future system-wide latency predictions. (1) We find that the prediction of median latency is much more accurate and meaningful than the prediction of average latency, as the latter is plagued by outliers. This is verified by machine-learning prediction using XGBoost that yields a 35% improvement in the mean square error of the 5-minute averaged latencies. (2) We find that the median latency of the segment 15 minutes ago is a very good baseline for performance comparison, and we have evidence that further improvement is achieved by machine learning approaches such as XGBoost and Long Short-Term Memory (LSTM). (3) By analyzing the feature importance score in XGBoost and calculating the mutual information between the inputs and the latencies to be predicted, we identify a sequence of inputs ranked in importance. It confirms that the past latencies are most informative of the predicted latencies, followed by the total accumulation, whereas inputs such as the entrance and exit rates are uninformative. It also confirms that the inputs are much less informative of the average latencies than the median latencies. (4) For predicting the latencies of segments composed of two or three sub-segments, summing up the predicted latencies of each sub-segment is more accurate than the one-step prediction of the whole segment, especially with the latency prediction of the downstream sub-segments trained to anticipate latencies several minutes ahead. The duration of the anticipation time is an increasing function of the traveling time of the upstream segment. The above findings have important implications to predicting the full set of latencies among the various locations in the freeway system.

Keywords: data refinement, machine learning, mutual information, short-term latency prediction

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Authors: Shoaib Rasool

Abstract:

Migration trends in rural areas of Gujrat are increasing day by day among illiterate people as they consider it as a source of attraction and charm of destination. It affects the life standard both positive and negative way to their families left behind in the context of poverty, socio-economic status and life standards. It also promotes material items and as well as social indicators of living, housing conditions, schooling of their children’s, health seeking behavior and to some extent their family environment. The nature of the present study is to analyze socio-economic conditions regarding life standard of emigrant families left behind in rural areas of Gujrat district, Pakistan. A survey design was used on 150 families selected from rural areas of Gujrat districts through purposive sampling technique. A well-structured questionnaire was administered by the researcher to explore the nature of the study and for further data collection process. The measurement tool was pretested on 20 families to check the workability and reliability before the actual data collection. Statistical tests were applied to draw results and conclusion. The preliminary findings of the study show that emigration has left deep social-economic impacts on life standards of rural families left behind in Gujrat. They improved their life status and living standard through remittances. Emigration is one of the major sources of development of economy of household and it also alleviate poverty at house household level as well as community and country level. The rationale behind migration varies individually and geographically. There are popular considered attractions in Pakistan includes securing high status, improvement in health condition, coping other, getting married then to acquire nationality, using the unfair means, opting educational visas etc. Emigrants are not only sending remittances but also returning with newly acquired skills and valuable knowledge to their country of origin because emigrants learn new methods of living and working. There are also women migrants who experience social downward mobility by engaging in jobs that are beneath their educational qualifications.

Keywords: emigration, life standard, families, left behind, rural area, Gujrat

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