Search results for: S/R machine

Authors: Faruk Ozdemir, Roy Kalawsky, Peter Hubbard

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Accurate estimation of RUL provides a basis for effective predictive maintenance, reducing unexpected downtime for industrial equipment. However, while models such as the Random Forest have effective predictive capabilities, they are the so-called ‘black box’ models, where interpretability is at a threshold to make critical diagnostic decisions involved in industries related to aviation. The purpose of this work is to present a prognostic framework that embeds Explainable Artificial Intelligence (XAI) techniques in order to provide essential transparency in Machine Learning methods' decision-making mechanisms based on sensor data, with the objective of procuring actionable insights for the aviation industry. Sensor readings have been gathered from critical equipment such as turbofan jet engine and landing gear, and the prediction of the RUL is done by a Random Forest model. It involves steps such as data gathering, feature engineering, model training, and evaluation. These critical components’ datasets are independently trained and evaluated by the models. While suitable predictions are served, their performance metrics are reasonably good; such complex models, however obscure reasoning for the predictions made by them and may even undermine the confidence of the decision-maker or the maintenance teams. This is followed by global explanations using SHAP and local explanations using LIME in the second phase to bridge the gap in reliability within industrial contexts. These tools analyze model decisions, highlighting feature importance and explaining how each input variable affects the output. This dual approach offers a general comprehension of the overall model behavior and detailed insight into specific predictions. The proposed framework, in its third component, incorporates the techniques of causal analysis in the form of Granger causality tests in order to move beyond correlation toward causation. This will not only allow the model to predict failures but also present reasons, from the key sensor features linked to possible failure mechanisms to relevant personnel. The causality between sensor behaviors and equipment failures creates much value for maintenance teams due to better root cause identification and effective preventive measures. This step contributes to the system being more explainable. Surrogate Several simple models, including Decision Trees and Linear Models, can be used in yet another stage to approximately represent the complex Random Forest model. These simpler models act as backups, replicating important jobs of the original model's behavior. If the feature explanations obtained from the surrogate model are cross-validated with the primary model, the insights derived would be more reliable and provide an intuitive sense of how the input variables affect the predictions. We then create an iterative explainable feedback loop, where the knowledge learned from the explainability methods feeds back into the training of the models. This feeds into a cycle of continuous improvement both in model accuracy and interpretability over time. By systematically integrating new findings, the model is expected to adapt to changed conditions and further develop its prognosis capability. These components are then presented to the decision-makers through the development of a fully transparent condition monitoring and alert system. The system provides a holistic tool for maintenance operations by leveraging RUL predictions, feature importance scores, persistent sensor threshold values, and autonomous alert mechanisms. Since the system will provide explanations for the predictions given, along with active alerts, the maintenance personnel can make informed decisions on their end regarding correct interventions to extend the life of the critical machinery.

Keywords: predictive maintenance, explainable artificial intelligence, prognostic, RUL, machine learning, turbofan engines, C-MAPSS dataset

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Authors: Stelios A. Mitilineos, Nicolas-Alexander Tatlas, Georgia Korompili, Lampros Kokkalas, Stelios M. Potirakis

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Obstructive Sleep Apnea Hypopnea Syndrome (OSAHS) is a widespread chronic disease that mostly remains undetected, mainly due to the fact that it is diagnosed via polysomnography which is a time and resource-intensive procedure. Screening the disease’s symptoms at home could be used as an alternative approach in order to alert individuals that potentially suffer from OSAHS without compromising their everyday routine. Since snoring is usually linked to OSAHS, developing a snore detector is appealing as an enabling technology for screening OSAHS at home using ubiquitous equipment like commodity microphones (included in, e.g., smartphones). In this context, this study developed a snore detection tool and herein present the approach and selection of specific sound features that discriminate snoring vs. environmental sounds, as well as the performance of the proposed tool. Furthermore, a Real-Time Snore Detector (RTSD) is built upon the snore detection tool and employed in whole-night sleep sound recordings resulting to a large dataset of snoring sound excerpts that are made freely available to the public. The RTSD may be used either as a stand-alone tool that offers insight to an individual’s sleep quality or as an independent component of OSAHS screening applications in future developments.

Keywords: obstructive sleep apnea hypopnea syndrome, apnea screening, snoring detection, machine learning, neural networks

Procedia PDF Downloads 208

Authors: M. Saiful Islam, M. Mohandes, S. Rehman, S. Badran

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Increasing necessity of wind power is directing us to have precise knowledge on wind resources. Methodical investigation of potential locations is required for wind power deployment. High penetration of wind energy to the grid is leading multi megawatt installations with huge investment cost. This fact appeals to determine appropriate places for wind farm operation. For accurate assessment, detailed examination of wind speed profile, relative humidity, temperature and other geological or atmospheric parameters are required. Among all of these uncertainty factors influencing wind power estimation, vertical extrapolation of wind speed is perhaps the most difficult and critical one. Different approaches have been used for the extrapolation of wind speed to hub height which are mainly based on Log law, Power law and various modifications of the two. This paper proposes a Artificial Neural Network (ANN) and Genetic Algorithm (GA) based hybrid model, namely GA-NN for vertical extrapolation of wind speed. This model is very simple in a sense that it does not require any parametric estimations like wind shear coefficient, roughness length or atmospheric stability and also reliable compared to other methods. This model uses available measured wind speeds at 10m, 20m and 30m heights to estimate wind speeds up to 100m. A good comparison is found between measured and estimated wind speeds at 30m and 40m with approximately 3% mean absolute percentage error. Comparisons with ANN and power law, further prove the feasibility of the proposed method.

Keywords: wind profile, vertical extrapolation of wind, genetic algorithm, artificial neural network, hybrid machine learning

Procedia PDF Downloads 490

Authors: Kamélia Bouderbala, Hichem Nouira, Etienne Videcoq, Manuel Girault, Daniel Petit

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Thermal drifts caused by the power dissipated by the mechanical guiding systems constitute the main limit to enhance the accuracy of an ultra-high precision cylindricity measuring machine. For this reason, a high precision compact prototype has been designed to simulate the behaviour of the instrument. It ensures in situ calibration of four capacitive displacement probes by comparison with four laser interferometers. The set-up includes three heating wires for simulating the powers dissipated by the mechanical guiding systems, four additional heating wires located between each laser interferometer head and its respective holder, 19 Platinum resistance thermometers (Pt100) to observe the temperature evolution inside the set-up and four Pt100 sensors to monitor the ambient temperature. Both a Reduced Model (RM), based on the Modal Identification Method (MIM) was developed and optimized by comparison with the experimental results. Thereafter, time dependent tests were performed under several conditions to measure the temperature variation at 19 fixed positions in the system and compared to the calculated RM results. The RM results show good agreement with experiment and reproduce as well the temperature variations, revealing the importance of the RM proposed for the evaluation of the thermal behaviour of the system.

Keywords: modal identification method (MIM), thermal behavior and drift, dimensional metrology, measurement

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Authors: Sushma Ghogale

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With a surge in user-generated content or feedback or reviews on the internet, it has become possible and important to know consumers' opinions about products and services. This data is important for both potential customers and businesses providing the services. Data from social media is attracting significant attention and has become the most prominent channel of expressing an unregulated opinion. Prospective customers look for reviews from experienced customers before deciding to buy a product or service. Several websites provide a platform for users to post their feedback for the provider and potential customers. However, the biggest challenge in analyzing such data is in extracting latent features and providing term-level analysis of the data. This paper proposes an approach to use topic modeling to classify the reviews into topics and conduct sentiment analysis to mine the opinions. This approach can analyse and classify latent topics mentioned by reviewers on business sites or review sites, or social media using topic modeling to identify the importance of each topic. It is followed by sentiment analysis to assess the satisfaction level of each topic. This approach provides a classification of hotel reviews using multiple machine learning techniques and comparing different classifiers to mine the opinions of user reviews through sentiment analysis. This experiment concludes that Multinomial Naïve Bayes classifier produces higher accuracy than other classifiers.

Keywords: latent Dirichlet allocation, topic modeling, text classification, sentiment analysis

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Authors: Mohammad Bakhshandeh

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Structural Health Monitoring (SHM) is a crucial and necessary practice that plays a vital role in ensuring the safety and integrity of critical structures, and in particular, bridges. The continuous monitoring of bridges for signs of damage or degradation through Bridge Health Monitoring (BHM) enables early detection of potential problems, allowing for prompt corrective action to be taken before significant damage occurs. Although all monitoring techniques aim to provide accurate and decisive information regarding the remaining useful life, safety, integrity, and serviceability of bridges, understanding the development and propagation of damage is vital for maintaining uninterrupted bridge operation. Over the years, extensive research has been conducted on BHM methods, and experts in the field have increasingly adopted new methodologies. In this article, we provide a comprehensive exploration of the various BHM approaches, including sensor-based, non-destructive testing (NDT), model-based, and artificial intelligence (AI)-based methods. We also discuss the challenges associated with BHM, including sensor placement and data acquisition, data analysis and interpretation, cost and complexity, and environmental effects, through an extensive review of relevant literature and research studies. Additionally, we examine potential solutions to these challenges and propose future research ideas to address critical gaps in BHM.

Keywords: structural health monitoring (SHM), bridge health monitoring (BHM), sensor-based methods, machine-learning algorithms, and model-based techniques, sensor placement, data acquisition, data analysis

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Authors: Vu H. N. Khue, Marian Pavelka, Georg Jocher, Jiří Dušek, Le T. Son, Bui T. An, Ho Q. Bang, Pham Q. Huong

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Agriculture is an important economic sector of Vietnam, the most popular of which is wet rice cultivation. These activities are also known as the main contributor to the national greenhouse gas. In order to understand more about greenhouse gas exchange in these activities and to investigate the factors influencing carbon cycling and sequestration in these types of ecosystems, since 2019, the first eddy covariance station has been installed in a paddy field in Long An province, Mekong Delta. The station was equipped with state-of-the-art equipment for CO₂ and CH₄ gas exchange and micrometeorology measurements. In this study, data from the station was processed following the ICOS recommendations (Integrated Carbon Observation System) standards for CO₂, while CH₄ was manually processed and gap-filled using a random forest model from methane-gapfill-ml, a machine learning package, as there is no standard method for CH₄ flux gap-filling yet. Finally, the carbon equivalent (Ce) balance based on CO₂ and CH₄ fluxes was estimated. The results show that in 2020, even though a new water management practice - alternate wetting and drying - was applied to reduce methane emissions, the paddy field released 928 g Cₑ.m⁻².yr⁻¹, and in 2021, it was reduced to 707 g Cₑ.m⁻².yr⁻¹. On a provincial level, rice cultivation activities in Long An, with a total area of 498,293 ha, released 4.6 million tons of Cₑ in 2020 and 3.5 million tons of Cₑ in 2021.

Keywords: eddy covariance, greenhouse gas, methane, rice cultivation, Mekong Delta

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Authors: A. Khalina, K. Shaharuddin, M. S. Wahab, M. P. Saiman, H. A. Aisyah

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This paper investigates the effect of alkali treatment and mechanical properties of kenaf (Hibiscus cannabinus) fibre for the development of yarn. Two different fibre sources are used for the yarn production. Kenaf fibres were treated with sodium hydroxide (NaOH) in the concentration of 3, 6, 9, and 12% prior to fibre opening process and tested for their tensile strength and Young’s modulus. Then, the selected fibres were introduced to fibre opener at three different opening processing parameters; namely, speed of roller feeder, small drum, and big drum. The diameter size, surface morphology, and fibre durability towards machine of the fibres were characterized. The results show that concentrations of NaOH used have greater effects on fibre mechanical properties. From this study, the tensile and modulus properties of the treated fibres for both types have improved significantly as compared to untreated fibres, especially at the optimum level of 6% NaOH. It is also interesting to highlight that 6% NaOH is the optimum concentration for the alkaline treatment. The untreated and treated fibres at 6% NaOH were then introduced to fibre opener, and it was found that the treated fibre produced higher fibre diameter with better surface morphology compared to the untreated fibre. Higher speed parameter during opening was found to produce higher yield of opened-kenaf fibres.

Keywords: alkaline treatment, kenaf fibre, tensile strength, yarn production

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Authors: Chongya Song, Kang Yen, Alexander Pons, Jin Liu

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In the field of machine learning, the ensemble has been employed as a common methodology to improve the performance upon multiple base classifiers. However, the true predictions are often canceled out by the false ones during consensus due to a phenomenon called “curse of correlation” which is represented as the strong interferences among the predictions produced by the base classifiers. In addition, the existing practices are still not able to effectively mitigate the problem of imbalanced classification. Based on the analysis on our experiment results, we conclude that the two problems are caused by some inherent deficiencies in the approach of consensus. Therefore, we create an enhanced ensemble algorithm which adopts a designed rank-based chain-mode consensus to overcome the two problems. In order to evaluate the proposed ensemble algorithm, we employ a well-known benchmark data set NSL-KDD (the improved version of dataset KDDCup99 produced by University of New Brunswick) to make comparisons between the proposed and 8 common ensemble algorithms. Particularly, each compared ensemble classifier uses the same 22 base classifiers, so that the differences in terms of the improvements toward the accuracy and reliability upon the base classifiers can be truly revealed. As a result, the proposed rank-based chain-mode consensus is proved to be a more effective ensemble solution than the traditional consensus approach, which outperforms the 8 ensemble algorithms by 20% on almost all compared metrices which include accuracy, precision, recall, F1-score and area under receiver operating characteristic curve.

Keywords: consensus, curse of correlation, imbalance classification, rank-based chain-mode ensemble

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Authors: Bagul Abhijeet

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Background: Client-Server model is the backbone of today’s internet communication. In which normal user can not have control over particular website or server? By using the same processing model one can have unauthorized access to particular server. In this paper, we discussed about application scenario of hacking for simple website or server consist of unauthorized way to access the server database. This application emerges to autonomously take direct access of simple website or server and retrieve all essential information maintain by administrator. In this system, IP address of server given as input to retrieve user-id and password of server. This leads to breaking administrative security of server and acquires the control of server database. Whereas virus helps to escape from server security by crashing the whole server. Objective: To control malicious attack and preventing all government website, and also find out illegal work to do hackers activity. Results: After implementing different hacking as well as non-hacking techniques, this system hacks simple web sites with normal security credentials. It provides access to server database and allow attacker to perform database operations from client machine. Above Figure shows the experimental result of this application upon different servers and provides satisfactory results as required. Conclusion: In this paper, we have presented a to view to hack the server which include some hacking as well as non-hacking methods. These algorithms and methods provide efficient way to hack server database. By breaking the network security allow to introduce new and better security framework. The terms “Hacking” not only consider for its illegal activities but also it should be use for strengthen our global network.

Keywords: Hacking, Vulnerabilities, Dummy request, Virus, Server monitoring

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Authors: Saliha Gachi, Mouloud Aissani, Fouad Boubenider

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Friction Stir Welding (FSW) is a solid-state welding technique that can join material without melting the plates to be welded. In this work, we are interested to demonstrate the potentiality of FSW for joining the heat-treatable aluminum alloy 2024-T3 which is reputed as difficult to be welded by fusion techniques. Thereafter, the FSW joint is compared with another one obtained from a conventional fusion process Tungsten Inert Gas (TIG). FSW welds are made up using an FSW tool mounted on a milling machine. Single pass welding was applied to fabricated TIG joint. The comparison between the two processes has been made on the temperature evolution, mechanical and microstructure behavior. The microstructural examination revealed that FSW weld is composed of four zones: Base metal (BM), Heat affected zone (HAZ), Thermo-mechanical affected zone (THAZ) and the nugget zone (NZ). The NZ exhibits a recrystallized equiaxed refined grains that induce better mechanical properties and good ductility compared to TIG joint where the grains have a larger size in the welded region compared with the BM due to the elevated heat input. The microhardness results show that, in FSW weld, the THAZ contains the lowest microhardness values and increase in the NZ; however, in TIG process, the lowest values are localized on the NZ.

Keywords: friction stir welding, tungsten inert gaz, aluminum, microstructure

Procedia PDF Downloads 277

Authors: P. Bharathiraja

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Computer Aided decision making system is used to enhance diagnosis and prognosis of diseases and also to assist clinicians and junior doctors in clinical decision making. Medical Data used for decision making should be definite and consistent. Data Mining and soft computing techniques are used for cleaning the data and for incorporating human reasoning in decision making systems. Fuzzy rule based inference technique can be used for classification in order to incorporate human reasoning in the decision making process. In this work, missing values are imputed using the mean or mode of the attribute. The data are normalized using min-ma normalization to improve the design and efficiency of the fuzzy inference system. The fuzzy inference system is used to handle the uncertainties that exist in the medical data. Equal-width-partitioning is used to partition the attribute values into appropriate fuzzy intervals. Fuzzy rules are generated using Class Based Associative rule mining algorithm. The system is trained and tested using heart disease data set from the University of California at Irvine (UCI) Machine Learning Repository. The data was split using a hold out approach into training and testing data. From the experimental results it can be inferred that classification using fuzzy inference system performs better than trivial IF-THEN rule based classification approaches. Furthermore it is observed that the use of fuzzy logic and fuzzy inference mechanism handles uncertainty and also resembles human decision making. The system can be used in the absence of a clinical expert to assist junior doctors and clinicians in clinical decision making.

Keywords: decision making, data mining, normalization, fuzzy rule, classification

Procedia PDF Downloads 518

Authors: M. Devaki, K. B. Jayanthi

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The identification of water bodies from satellite images has recently received a great deal of attention. Different methods have been developed to distinguish water bodies from various satellite images that vary in terms of time and space. Urban water identification issues body manifests in numerous applications with a great deal of certainty. There has been a sharp rise in the usage of satellite images to map natural resources, including urban water bodies and forests, during the past several years. This is because water and forest resources depend on each other so heavily that ongoing monitoring of both is essential to their sustainable management. The relevant elements from satellite pictures have been chosen using a variety of techniques, including machine learning. Then, a convolution neural network (CNN) architecture is created that can identify a superpixel as either one of two classes, one that includes water or doesn't from input data in a complex metropolitan scene. The deep learning technique, CNN, has advanced tremendously in a variety of visual-related tasks. CNN can improve classification performance by reducing the spectral-spatial regularities of the input data and extracting deep features hierarchically from raw pictures. Calculate the water body using the satellite image's resolution. Experimental results demonstrate that the suggested method outperformed conventional approaches in terms of water extraction accuracy from remote-sensing images, with an average overall accuracy of 97%.

Keywords: water body, Deep learning, satellite images, convolution neural network

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Authors: Jianshu Yang, Delphine Sordes, Marek Kolmer, Christian Joachim

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Nanoelectronics, for example the calculating circuits integrating at molecule scale logic gates, atomic scale circuits, has been constructed and investigated recently. A major challenge is their functional properties characterization because of the connecting problem from atomic scale to micrometer scale. New experimental instruments and new processes have been proposed therefore. To satisfy a precisely measurement at atomic scale and then connecting micrometer scale electrical integration controller, the technique improvement is kept on going. Our new machine, a low temperature high vacuum four scanning tunneling microscope, as a customer required instrument constructed by Omicron GmbH, is expected to be scaling down to atomic scale characterization. Here, we will present our first testified results about the performance of this new instrument. The sample we selected is Au(111) surface. The measurements have been taken at 4.2 K. The atomic resolution surface structure was observed with each of four scanners with noise level better than 3 pm. With a tip-sample distance calibration by I-z spectra, the sample conductance has been derived from its atomic locally I-V spectra. Furthermore, the surface conductance measurement has been performed using two methods, (1) by landing two STM tips on the surface with sample floating; and (2) by sample floating and one of the landed tips turned to be grounding. In addition, single atom manipulation has been achieved with a modified tip design, which is comparable to a conventional LT-STM.

Keywords: low temperature ultra-high vacuum four scanning tunneling microscope, nanoelectronics, point contact, single atom manipulation, tunneling resistance

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Authors: Bijit Kalita, K. V. N. Surendra

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Rotating disk is one of the most indispensable parts of a rotating machine. Rotating disk has found many applications in the diverging field of science and technology. In this paper, we have taken into consideration the problem of a heavy spinning disk mounted on a rotor system acted upon by boundary traction. Finite element modelling is used at various loading condition to determine the mixed mode stress intensity factors. The effect of combined shear and normal traction on the boundary is incorporated in the analysis under the action of gravity. The variation near the crack tip is characterized in terms of the stress intensity factor (SIF) with an aim to find the SIF for a wide range of parameters. The results of the finite element analyses carried out on the compressed disk of a belt pulley arrangement using fracture mechanics concepts are shown. A total of hundred cases of the problem are solved for each of the variations in loading arc parameter and crack orientation using finite element models of the disc under compression. All models were prepared and analyzed for the uncracked disk, disk with a single crack at different orientation emanating from shaft hole as well as for a disc with pair of cracks emerging from the same center hole. Curves are plotted for various loading conditions. Finally, crack propagation paths are determined using kink angle concepts.

Keywords: crack-tip deformations, static loading, stress concentration, stress intensity factor

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Authors: Junior Nomani

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This paper presents a novel approach to modelling the metal cutting of duplex stainless steels, a two-phase alloy regarded as a difficult-to-machine material. Calculation and control of shear strain and stresses during cutting are essential to achievement of ideal cutting conditions. Too low or too high leads to higher required cutting force or excessive heat generation causing premature tool wear failure. A 2D finite element cutting model was created based on electron backscatter diffraction (EBSD) data imagery of duplex microstructure. A mesh was generated using ‘object-oriented’ software OOF2 version V2.1.11, converting microstructural images to quadrilateral elements. A virtual workpiece was created on ABAQUS modelling software where a rigid body toolpiece advanced towards workpiece simulating chip formation, generating serrated edge chip formation cutting. Model results found calculated stress strain contour plots correlated well with similar finite element models tied with austenite stainless steel alloys. Virtual chip form profile is also similar compared experimental frozen machining chip samples. The output model data provides new insight description of strain behavior of two phase material on how it transitions from workpiece into the chip.

Keywords: Duplex stainless steel, ABAQUS, OOF2, Chip formation

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Authors: Saad M. Darwish, Mohamed A. El-Iskandarani, Guitar M. Shawkat

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Nowadays, the amount of available multimedia data is continuously on the rise. The need to find a required image for an ordinary user is a challenging task. Content based image retrieval (CBIR) computes relevance based on the visual similarity of low-level image features such as color, textures, etc. However, there is a gap between low-level visual features and semantic meanings required by applications. The typical method of bridging the semantic gap is through the automatic image annotation (AIA) that extracts semantic features using machine learning techniques. In this paper, a multi-label image annotation system guided by Firefly and Bayesian method is proposed. Firstly, images are segmented using the maximum variance intra cluster and Firefly algorithm, which is a swarm-based approach with high convergence speed, less computation rate and search for the optimal multiple threshold. Feature extraction techniques based on color features and region properties are applied to obtain the representative features. After that, the images are annotated using translation model based on the Net Bayes system, which is efficient for multi-label learning with high precision and less complexity. Experiments are performed using Corel Database. The results show that the proposed system is better than traditional ones for automatic image annotation and retrieval.

Keywords: feature extraction, feature selection, image annotation, classification

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Authors: Tochukwu Timothy Okoli, Devi Datt Tewari

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The high rate of Fintech adoption has not transmitted to greater financial inclusion and development in Africa. This problem is attributed to poor Fintech diversification and usefulness in the continent. This concept is referred to as the Fintech depth of penetration in this study. The study, therefore, assessed its determinants and growth process in a panel of three emergings, twenty-four frontiers and five fragile African economies disaggregated with dummies over the period 2004-2018 to allow for heterogeneity between groups. The System Generalized Method of Moments (GMM) technique reveals that the average depth of Mobile banking and automated teller machine (ATM) is a dynamic heterogeneity process. Moreover, users' previous experiences/compatibility, trial-ability/income, and financial development were the major factors that raise its usefulness, whereas perceived risk, financial openness, and inflation rate significantly limit its usefulness. The growth rate of Mobile banking, ATM, and Internet banking in 2018 is, on average 41.82, 0.4, and 20.8 per cent respectively greater than its average rates in 2004. These greater averages after the 2009 financial crisis suggest that countries resort to Fintech as a risk-mitigating tool. This study, therefore, recommends greater Fintech diversification through improved literacy, institutional development, financial liberalization, and continuous innovation.

Keywords: depth of fintech, emerging Africa, financial technology, internet banking, mobile banking

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Authors: Aderet M. Pantierer, Shmuel Pantierer, Raphael Cordina, Yougashwar Budhoo

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Stress concentration occurs when there is an abrupt change in geometry, a mechanical part under loading. These changes in geometry can include holes, notches, or cracks within the component. The modifications create larger stress within the part. This maximum stress is difficult to determine, as it is directly at the point of the minimum area. Strain gauges have yet to be developed to analyze stresses at such minute areas. Therefore, a stress concentration factor must be utilized. The stress concentration factor is a dimensionless parameter calculated solely on the geometry of a part. The factor is multiplied by the nominal, or average, stress of the component, which can be found analytically or experimentally. Stress concentration graphs exist for common loading conditions and geometrical configurations to aid in the determination of the maximum stress a part can withstand. These graphs were developed from historical data yielded from experimentation. This project seeks to verify a stress concentration graph for combined loading conditions. The aforementioned graph was developed using CATIA Finite Element Analysis software. The results of this analysis will be validated through further testing. The 3D modeled parts will be subjected to further finite element analysis using Patran-Nastran software. The finite element models will then be verified by testing physical specimen using a tensile testing machine. Once the data is validated, the unique stress concentration graph will be submitted for publication so it can aid engineers in future projects.

Keywords: stress concentration, finite element analysis, finite element models, combined loading

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Authors: Leonard D. Agana, Wendell Ace Dela Cruz, Arjan C. Lingaya, Bonifacio T. Doma Jr.

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The purpose of this paper is to study the predict the fuel performance parameters, which include drivability index (DI), vapor lock index (VLI), and vapor lock potential using distillation curve and Reid vapor pressure (RVP) of dual alcohol-gasoline fuel blends. Distillation curve and Reid vapor pressure were predicted using artificial neural networks (ANN) with macroscopic properties such as boiling points, RVP, and molecular weights as the input layers. The ANN consists of 5 hidden layers and was trained using Bayesian regularization. The training mean square error (MSE) and R-value for the ANN of RVP are 91.4113 and 0.9151, respectively, while the training MSE and R-value for the distillation curve are 33.4867 and 0.9927. Fuel performance analysis of the dual alcohol–gasoline blends indicated that highly volatile gasoline blended with dual alcohols results in non-compliant fuel blends with D4814 standard. Mixtures of low-volatile gasoline and 10% methanol or 10% ethanol can still be blended with up to 10% C3 and C4 alcohols. Intermediate volatile gasoline containing 10% methanol or 10% ethanol can still be blended with C3 and C4 alcohols that have low RVPs, such as 1-propanol, 1-butanol, 2-butanol, and i-butanol. Biography: Graduate School of Chemical, Biological, and Materials Engineering and Sciences, Mapua University, Muralla St., Intramuros, Manila, 1002, Philippines

Keywords: dual alcohol-gasoline blends, distillation curve, machine learning, reid vapor pressure

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Authors: Rezvan Ravanfar Haghighi, S. Chatterjee, Pratik Kumar, V. C. Vani, Priya Jagia, Sanjiv Sharma, Susama Rani Mandal, R. Lakshmy

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The dual energy computed tomography (DECT) aims at noting the HU(V) values for the sample at two different voltages V=V1, V2 and thus obtain the electron densities (ρe) and effective atomic number (Zeff) of the substance. In the present paper, we aim to obtain a numerical algorithm by which (ρe, Zeff) can be obtained from the HU(100) and HU(140) data, where V=100, 140 kVp. The idea is to use this inversion method to characterize and distinguish between the lipid and fibrous coronary artery plaques.With the idea to develop the inversion algorithm for low Zeff materials, as is the case with non calcified coronary artery plaque, we prepare aqueous samples whose calculated values of (ρe, Zeff) lie in the range (2.65×1023≤ ρe≤ 3.64×1023 per cc ) and (6.80≤ Zeff ≤ 8.90). We fill the phantom with these known samples and experimentally determine HU(100) and HU(140) for the same pixels. Knowing that the HU(V) values are related to the attenuation coefficient of the system, we present an algorithm by which the (ρe, Zeff) is calibrated with respect to (HU(100), HU(140)). The calibration is done with a known set of 20 samples; its accuracy is checked with a different set of 23 known samples. We find that the calibration gives the ρe with an accuracy of ± 4% while Zeff is found within ±1% of the actual value, the confidence being 95%.In this inversion method (ρe, Zeff) of the scanned sample can be found by eliminating the effects of the CT machine and also by ensuring that the determination of the two unknowns (ρe, Zeff) does not interfere with each other. It is found that this algorithm can be used for prediction of chemical characteristic (ρe, Zeff) of unknown scanned materials with 95% confidence level, by inversion of the DECT data.

Keywords: chemical composition, dual-energy computed tomography, inversion algorithm

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Authors: Keerthana E., Lohithya S., Harshavardhini K. S., Saranya G., Suganthi S.

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In an epoch of expanding human-machine interaction, the development of innovative interfaces that bridge the gap between physical gestures and digital control has gained significant momentum. This study introduces a distinct solution that leverages a combination of MEMS (Micro-Electro-Mechanical Systems) sensors, an Arduino Mega microcontroller, and a PC to create a hand gesture interface for PC control and SMS notification. The core of the system is an ADXL335 MEMS accelerometer sensor integrated with an Arduino Mega, which communicates with a PC via a USB cable. The ADXL335 provides real-time acceleration data, which is processed by the Arduino to detect specific hand gestures. These gestures, such as left, right, up, down, or custom patterns, are interpreted by the Arduino, and corresponding actions are triggered. In the context of SMS notifications, when a gesture indicative of a new SMS is recognized, the Arduino relays this information to the PC through the serial connection. The PC application, designed to monitor the Arduino's serial port, displays these SMS notifications in the serial monitor. This study offers an engaging and interactive means of interfacing with a PC by translating hand gestures into meaningful actions, opening up opportunities for intuitive computer control. Furthermore, the integration of SMS notifications adds a practical dimension to the system, notifying users of incoming messages as they interact with their computers. The use of MEMS sensors, Arduino, and serial communication serves as a promising foundation for expanding the capabilities of gesture-based control systems.

Keywords: hand gestures, multiple cables, serial communication, sms notification

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Authors: Maha Saliba Foster

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Speech perception has been recognized as a bi-sensory process involving the auditory and visual channels. Compared to the auditory modality, the contribution of the visual signal to speech perception is not very well understood. Studying how the visual modality affects speech recognition can have pedagogical implications in second language learning, as well as clinical application in speech therapy. The current investigation explores the potential effect of speech visual cues on the perception of Arabic emphatics (AEs). The corpus consists of 36 minimal pairs each containing two contrasting consonants, an AE versus a non-emphatic (NE). Movies of four Lebanese speakers were edited to allow perceivers to have partial view of facial regions: lips only, lips-cheeks, lips-chin, lips-cheeks-chin, lips-cheeks-chin-neck. In the absence of any auditory information and relying solely on visual speech, perceivers were above chance at correctly identifying AEs or NEs across vowel contexts; moreover, the models were able to predict the probability of perceivers’ accuracy in identifying some of the COIs produced by certain speakers; additionally, results showed an overlap between the measurements selected by the computer and those selected by human perceivers. The lack of significant face effect on the perception of AEs seems to point to the lips, present in all of the videos, as the most important and often sufficient facial feature for emphasis recognition. Future investigations will aim at refining the analyses of visual cues used by perceivers by using Principal Component Analysis and including time evolution of facial feature measurements.

Keywords: Arabic emphatics, machine learning, speech perception, visual speech perception

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Authors: Dionisio Andujar, Juan lópez-Correa, Hugo Moreno, Angela Ri

Abstract:

The increased number of weeds in tomato crops highly lower yields. Weed identification with the aim of machine learning is important to carry out site-specific control. The last advances in computer vision are a powerful tool to face the problem. The analysis of RGB (Red, Green, Blue) images through Artificial Neural Networks had been rapidly developed in the past few years, providing new methods for weed classification. The development of the algorithms for crop and weed species classification looks for a real-time classification system using Object Detection algorithms based on Convolutional Neural Networks. The site study was located in commercial corn fields. The classification system has been tested. The procedure can detect and classify weed seedlings in tomato fields. The input to the Neural Network was a set of 10,000 RGB images with a natural infestation of Cyperus rotundus l., Echinochloa crus galli L., Setaria italica L., Portulaca oeracea L., and Solanum nigrum L. The validation process was done with a random selection of RGB images containing the aforementioned species. The mean average precision (mAP) was established as the metric for object detection. The results showed agreements higher than 95 %. The system will provide the input for an online spraying system. Thus, this work plays an important role in Site Specific Weed Management by reducing herbicide use in a single step.

Keywords: deep learning, object detection, cnn, tomato, weeds

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Authors: Hatim Laalej, Salvador Sumohano-Verdeja, Thomas McLeay

Abstract:

Monitoring of tool wear in milling operations is essential for achieving the desired dimensional accuracy and surface finish of a machined workpiece. Although there are numerous statistical models and artificial intelligence techniques available for monitoring the wear of cutting tools, these techniques cannot pin point which cutting edge of the tool, or which insert in the case of indexable tooling, is worn or broken. Currently, the task of monitoring the wear on the tool cutting edges is carried out by the operator who performs a manual inspection, causing undesirable stoppages of machine tools and consequently resulting in costs incurred from lost productivity. The present study is concerned with the development of a flute tracking system to segment signals related to each physical flute of a cutter with three flutes used in an end milling operation. The purpose of the system is to monitor the cutting condition for individual flutes separately in order to determine their progressive wear rates and to predict imminent tool failure. The results of this study clearly show that signals associated with each flute can be effectively segmented using the proposed flute tracking system. Furthermore, the results illustrate that by segmenting the sensor signal by flutes it is possible to investigate the wear in each physical cutting edge of the cutting tool. These findings are significant in that they facilitate the online condition monitoring of a cutting tool for each specific flute without the need for operators/engineers to perform manual inspections of the tool.

Keywords: machining, milling operation, tool condition monitoring, tool wear prediction

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Authors: Edward K. Boahen, Brunel E. Bouya-Moko, Changda Wang

Abstract:

The major threat to online social network (OSN) users is account compromisation. Spammers now spread malicious messages by exploiting the trust relationship established between account owners and their friends. The challenge in detecting a compromised account by service providers is validating the trusted relationship established between the account owners, their friends, and the spammers. Another challenge is the increase in required human interaction with the feature selection. Research available on supervised learning (machine learning) has limitations with the feature selection and accounts that cannot be profiled, like application programming interface (API). Therefore, this paper discusses the various behaviours of the OSN users and the current approaches in detecting a compromised OSN account, emphasizing its limitations and challenges. We propose a deep learning approach that addresses and resolve the constraints faced by the previous schemes. We detailed our proposed optimized nonsymmetric deep auto-encoder (OPT_NDAE) for unsupervised feature learning, which reduces the required human interaction levels in the selection and extraction of features. We evaluated our proposed classifier using the NSL-KDD and KDDCUP'99 datasets in a graphical user interface enabled Weka application. The results obtained indicate that our proposed approach outperformed most of the traditional schemes in OSN compromised account detection with an accuracy rate of 99.86%.

Keywords: computer security, network security, online social network, account compromisation

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Authors: Chaiyawat Musikapan, Ratchatin Chancharoen, Saknan Bongsebandhu-Phubhakdi

Abstract:

For many recent years, soft robotic fingers were used for supporting the patients who had survived the neurological diseases that resulted in muscular disorders and neural network damages, such as stroke and Parkinson’s disease, and inflammatory symptoms such as De Quervain and trigger finger. Generally, the major hand function is significant to manipulate objects in activities of daily living (ADL). In this work, we proposed the model of soft actuator that manufactured by full 3D printing without the molding process and one material for use. Furthermore, we designed the model with a technique of multi cavitation cells (MC-Cells). Then, we demonstrated the curvature bending, fluidic pressure and force that generated to the model for assistive finger flexor and hand grasping. Also, the soft actuators were characterized in mathematics solving by the length of chord and arc length. In addition, we used an adaptive push-button switch machine to measure the force in our experiment. Consequently, we evaluated biomechanics efficiency by the range of motion (ROM) that affected to metacarpophalangeal joint (MCP), proximal interphalangeal joint (PIP) and distal interphalangeal joint (DIP). Finally, the model achieved to exhibit the corresponding fluidic pressure with force and ROM to assist the finger flexor and hand grasping.

Keywords: biomechanics efficiency, curvature bending, hand functional assistance, multi cavitation cells (MC-Cells), range of motion (ROM)

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Authors: Vertika Goswami, Gargi Sharma

Abstract:

The rapid evolution of fintech has led to the rise of robo-advisor platforms that utilize artificial intelligence (AI) and machine learning to offer personalized investment solutions efficiently and cost-effectively. This research paper conducts a comprehensive sentiment analysis of investor experiences with these platforms, employing natural language processing (NLP) and sentiment classification techniques. The study investigates investor perceptions, engagement, and satisfaction, identifying key drivers of positive sentiment such as clear communication, low fees, consistent returns, and robust security. Conversely, negative sentiment is linked to issues like inconsistent performance, hidden fees, poor customer support, and a lack of transparency. The analysis reveals that addressing these pain points—through improved transparency, enhanced customer service, and ongoing technological advancements—can significantly boost investor trust and satisfaction. This paper contributes valuable insights into the fields of behavioral finance and fintech innovation, offering actionable recommendations for stakeholders, practitioners, and policymakers. Future research should explore the long-term impact of these factors on investor loyalty, the role of emerging technologies, and the effects of ethical investment choices and regulatory compliance on investor sentiment.

Keywords: artificial intelligence in finance, automated investment, financial technology, investor satisfaction, investor sentiment, robo-advisors, sentimental analysis

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Authors: Michel Han, Christophe Besson, Alain Savary, Yvan Becher

Abstract:

The variable flux permanent magnet synchronous motor (VF-PMSM), also called "Memory Motor", is a new generation of motor capable of modifying the magnetization state with short pulses of current during operation or standstill. The impact of such operation is the expansion of the operating range in the torque-speed characteristic and an improvement in energy efficiency at high-speed in comparison to conventional permanent magnet synchronous machines (PMSMs). This paper reviews the operating principle and the unique features of the proposed memory motor. The benefits of this concept are highlighted by comparing the performance of the rotor of the VF-PMSM to that of two PM rotors that are typically found in the industry. The investigation emphasizes the properties of the variable magnetization and presents the comparison of the torque-speed characteristic with the capability of loss reduction in a VF-PMSM by means of experimental results, especially when tests are conducted under identical conditions for each rotor (same stator, same inverter and same experimental setup). The experimental results demonstrated that the VF-PMSM gives an additional degree of freedom to optimize the efficiency over a wide speed range. Thus, with a design easy to manufacture and with the possibility of controlling the magnetization and the demagnetization of the magnets during operations, the VF-PMSM can be interesting for various applications.

Keywords: efficiency, magnetization state, memory motors, performances, permanent-magnet, synchronous machine, variable-flux, variable magnetization, wide speed application

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Authors: Jigg Pelayo, Ricardo Villar

Abstract:

Leaning on the development of aerial laser scanning in the Philippine geospatial industry, researches about remote sensing and machine vision technology became a trend. Object detection via template matching is one of its application which characterized to be fast and in real time. The paper purposely attempts to provide application for robust pattern matching algorithm based on the normalized cross correlation (NCC) criterion function subjected in Object-based image analysis (OBIA) utilizing high-resolution aerial imagery and low density LiDAR data. The height information from laser scanning provides effective partitioning order, thus improving the hierarchal class feature pattern which allows to skip unnecessary calculation. Since detection is executed in the object-oriented platform, mathematical morphology and multi-level filter algorithms were established to effectively avoid the influence of noise, small distortion and fluctuating image saturation that affect the rate of recognition of features. Furthermore, the scheme is evaluated to recognized the performance in different situations and inspect the computational complexities of the algorithms. Its effectiveness is demonstrated in areas of Misamis Oriental province, achieving an overall accuracy of 91% above. Also, the garnered results portray the potential and efficiency of the implemented algorithm under different lighting conditions.

Keywords: algorithm, LiDAR, object recognition, OBIA

Procedia PDF Downloads 246