Search results for: metal processing

Authors: A. Degale Desta, Cheng Jian

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Deep learning application in computer vision is rapidly advancing, giving it the ability to monitor the public and quickly identify potentially anomalous behaviour from crowd scenes. Therefore, the purpose of the current work is to improve the performance of safety of people in crowd events from panic behaviour through introducing the innovative idea of Aggregation of Ensembles (AOE), which makes use of the pre-trained ConvNets and a pool of classifiers to find anomalies in video data with packed scenes. According to the theory of algorithms that applied K-means, KNN, CNN, SVD, and Faster-CNN, YOLOv5 architectures learn different levels of semantic representation from crowd videos; the proposed approach leverages an ensemble of various fine-tuned convolutional neural networks (CNN), allowing for the extraction of enriched feature sets. In addition to the above algorithms, a long short-term memory neural network to forecast future feature values and a handmade feature that takes into consideration the peculiarities of the crowd to understand human behavior. On well-known datasets of panic situations, experiments are run to assess the effectiveness and precision of the suggested method. Results reveal that, compared to state-of-the-art methodologies, the system produces better and more promising results in terms of accuracy and processing speed.

Keywords: action recognition, computer vision, crowd detecting and tracking, deep learning

Procedia PDF Downloads 139

Authors: Isha Saxena, Behzad Kazemtabrizi, Matthias C. M. Troffaes, Christopher Crabtree

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The calculations done at the beginning of the life of a wind farm are rarely reliable, which makes it important to conduct research and study the failure and repair rates of the wind turbines under various conditions. This miscalculation happens because the current models make a simplifying assumption that the failure/repair rate remains constant over time. This means that the reliability function is exponential in nature. This research aims to create a more accurate model using sensory data and a data-driven approach. The data cleaning and data processing is done by comparing the Power Curve data of the wind turbines with SCADA data. This is then converted to times to repair and times to failure timeseries data. Several different mathematical functions are fitted to the times to failure and times to repair data of the wind turbine components using Maximum Likelihood Estimation and the Posterior expectation method for Bayesian Parameter Estimation. Initial results indicate that two parameter Weibull function and exponential function produce almost identical results. Further analysis is being done using the complex system analysis considering the failures of each electrical and mechanical component of the wind turbine. The aim of this project is to perform a more accurate reliability analysis that can be helpful for the engineers to schedule maintenance and repairs to decrease the downtime of the turbine.

Keywords: reliability, bayesian parameter inference, maximum likelihood estimation, weibull function, SCADA data

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Authors: Walter Bernhofer, Peter Haber, Tobias Mayer, Manfred Mayr, Markus Ziegler

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Current preventive maintenance measures are cost intensive and not efficient. With the available sensor data of state of the art internet of things devices new possibilities of automated data processing emerge. Current advances in data science and in machine learning enable new, so called predictive maintenance technologies, which empower data scientists to forecast possible system failures. The goal of this approach is to cut expenses in preventive maintenance by automating the detection of possible failures and to improve efficiency and quality of maintenance measures. Additionally, a centralization of the sensor data monitoring can be achieved by using this approach. This paper describes the approach of three students to define a reference architecture for a predictive maintenance solution in the internet of things domain with a connected smartphone app for service technicians. The reference architecture is validated by a case study. The case study is implemented with current Microsoft Azure cloud technologies. The results of the case study show that the reference architecture is valid and can be used to achieve a system for predictive maintenance execution with the cloud components of Microsoft Azure. The used concepts are technology platform agnostic and can be reused in many different cloud platforms. The reference architecture is valid and can be used in many use cases, like gas station maintenance, elevator maintenance and many more.

Keywords: case study, internet of things, predictive maintenance, reference architecture

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Authors: Amr Sadek, Abdelrahaman Al-Qahtany, Turkey Salem Al-Qahtany

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In this study, a backpropagation artificial neural network (ANN) model has been used to predict the cooling and heating capacities of air conditioners (AC) under different conditions. Sufficiently large measurement results were obtained from the national energy-efficiency laboratories in Saudi Arabia and were used for the learning process of the ANN model. The parameters affecting the performance of the AC, including temperature, humidity level, specific heat enthalpy indoors and outdoors, and the air volume flow rate of indoor units, have been considered. These parameters were used as inputs for the ANN model, while the cooling and heating capacity values were set as the targets. A backpropagation ANN model with two hidden layers and one output layer could successfully correlate the input parameters with the targets. The characteristics of the ANN model including the input-processing, transfer, neurons-distance, topology, and training functions have been discussed. The performance of the ANN model was monitored over the training epochs and assessed using the mean squared error function. The model was then used to predict the performance of the AC under conditions that were not included in the measurement results. The optimum performance of the AC was also predicted under the different environmental conditions in Saudi Arabia. The uncertainty of the ANN model predictions has been evaluated taking into account the randomness of the data and lack of learning.

Keywords: artificial neural network, uncertainty of model predictions, efficiency of air conditioners, cooling and heating capacities

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Authors: Judith Mwakalonge, Geophrey Mbatta, Saidi Siuhi, Gurcan Comert, Cuthbert Ruseruka

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Transportation agencies worldwide continuously monitor their roads' conditions to minimize road maintenance costs and maintain public safety and rideability quality. Existing methods for carrying out road condition surveys involve manual observations of roads using standard survey forms done by qualified road condition surveyors or engineers either on foot or by vehicle. Automated road condition survey vehicles exist; however, they are very expensive since they require special vehicles equipped with sensors for data collection together with data processing and computing devices. The manual methods are expensive, time-consuming, infrequent, and can hardly provide real-time information for road conditions. This study contributes to this arena by utilizing built-in vehicle technologies, drones, and deep learning to automate road condition surveys while using low-cost technology. A single model is trained to capture flexible pavement distresses (Potholes, Rutting, Cracking, and raveling), thereby providing a more cost-effective and efficient road condition monitoring approach that can also provide real-time road conditions. Additionally, data fusion is employed to enhance the road condition assessment with data from vehicles and drones.

Keywords: road conditions, built-in vehicle technology, deep learning, drones

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Authors: Khamdan Cahyari, Pratikno Hidayat

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Indonesia has produced more than 1.8 million ton pineapple fruit in 2013 of which turned into waste due to industrial processing, deterioration and low qualities. It was estimated that this waste accounted for more than 40 percent of harvested fruits. In addition, pineapple leaves were one of biomass waste from pineapple farming land, which contributed even higher percentages. Most of the waste was only dumped into landfill area without proper pretreatment causing severe environmental problem. This research was meant to valorize the pineapple waste for producing renewable energy source of biogas through mesophilic (30℃) anaerobic digestion process. Especially, it was aimed to investigate effect of substrate concentration of pineapple fruit waste i.e. peel, core as well as effect of microwave pretreatment of pineapple leaves waste. The concentration of substrate was set at value 12, 24 and 36 g VS/liter culture whereas 800-Watt microwave pretreatment conducted at 2 and 5 minutes. It was noticed that optimum biogas production obtained at concentration 24 g VS/l with biogas yield 0.649 liter/g VS (45%v CH4) whereas microwave pretreatment at 2 minutes duration performed better compare to 5 minutes due to shorter exposure of microwave heat. This results suggested that valorization of pineapple waste could be carried out through biogas production at the aforementioned process condition. Application of this method is able to both reduce the environmental problem of the waste and produce renewable energy source of biogas to fulfill local energy demand of pineapple farming areas.

Keywords: pineapple waste, substrate concentration, microwave pretreatment, biogas, anaerobic digestion

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Authors: Robson da C. Santos, Carlos Henrique de A. S. P. Coutinho, Lucas Moreira Dias, Gerson Gomes Cunha

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The article refers to the development of an augmented reality 3D display, through the control of servo motors and projection of image with aid of video projector on the model. Augmented Reality is a branch that explores multiple approaches to increase real-world view by viewing additional information along with the real scene. The article presents the broad use of electrical, electronic, mechanical and industrial automation for geospatial visualizations, applications in mathematical models with the visualization of functions and 3D surface graphics and volumetric rendering that are currently seen in 2D layers. Application as a 3D display for representation and visualization of Digital Terrain Model (DTM) and Digital Surface Models (DSM), where it can be applied in the identification of canyons in the marine area of the Campos Basin, Rio de Janeiro, Brazil. The same can execute visualization of regions subject to landslides, as in Serra do Mar - Agra dos Reis and Serranas cities both in the State of Rio de Janeiro. From the foregoing, loss of human life and leakage of oil from pipelines buried in these regions may be anticipated in advance. The physical design consists of a table consisting of a 9 x 16 matrix of servo motors, totalizing 144 servos, a mesh is used on the servo motors for visualization of the models projected by a retro projector. Each model for by an image pre-processing, is sent to a server to be converted and viewed from a software developed in C # Programming Language.

Keywords: visualization, 3D models, servo motors, C# programming language

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Authors: Rhea Kapoor

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Fractal dimension provides a way to characterize non-geometric shapes like those found in nature. The purpose of this research is to estimate Minkowski fractal dimension of human lung images for early detection of lung cancer. Lung cancer is the leading cause of death among all types of cancer and an early histopathological analysis will help reduce deaths primarily due to late diagnosis. A Python application program, PathoPy2.0, was developed for analyzing medical images in pixelated format and estimating Minkowski fractal dimension using a new box-counting algorithm that allows windowing of images for more accurate calculation in the suspected areas of cancerous growth. Benchmark geometric fractals were used to validate the accuracy of the program and changes in fractal dimension of lung images to indicate the presence of issues in the lung. The accuracy of the program for the benchmark examples was between 93-99% of known values of the fractal dimensions. Fractal dimension values were then calculated for lung images, from National Cancer Institute, taken over time to correctly detect the presence of cancerous growth. For example, as the fractal dimension for a given lung increased from 1.19 to 1.27 due to cancerous growth, it represents a significant change in fractal dimension which lies between 1 and 2 for 2-D images. Based on the results obtained on many lung test cases, it was concluded that fractal dimension of human lungs can be used to diagnose lung cancer early. The ideas behind PathoPy2.0 can also be applied to study patterns in the electrical activity of the human brain and DNA matching.

Keywords: fractals, histopathological analysis, image processing, lung cancer, Minkowski dimension

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Authors: Zhou Luchen, Wu Yubing, Burra Venkata Durga Kumar

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As the scale of the network becomes larger and more complex than before, the density of user devices is also increasing. The development of Unmanned Aerial Vehicle (UAV) networks is able to collect and transform data in an efficient way by using software-defined networks (SDN) technology. This paper proposed a three-layer distributed and dynamic cluster architecture to manage UAVs by using an AI-based resource allocation calculation algorithm to address the overloading network problem. Through separating services of each UAV, the UAV hierarchical cluster system performs the main function of reducing the network load and transferring user requests, with three sub-tasks including data collection, communication channel organization, and data relaying. In this cluster, a head node and a vice head node UAV are selected considering the Central Processing Unit (CPU), operational (RAM), and permanent (ROM) memory of devices, battery charge, and capacity. The vice head node acts as a backup that stores all the data in the head node. The k-means clustering algorithm is used in order to detect high load regions and form the UAV layered clusters. The whole process of detecting high load areas, forming and selecting UAV clusters, and moving the selected UAV cluster to that area is proposed as offloading traffic algorithm.

Keywords: k-means, resource allocation, SDN, UAV network, unmanned aerial vehicles

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Authors: Jianwei Ma, Diriba Gemechu

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In seismic data processing, attenuation of random noise is the basic step to improve quality of data for further application of seismic data in exploration and development in different gas and oil industries. The signal-to-noise ratio of the data also highly determines quality of seismic data. This factor affects the reliability as well as the accuracy of seismic signal during interpretation for different purposes in different companies. To use seismic data for further application and interpretation, we need to improve the signal-to-noise ration while attenuating random noise effectively. To improve the signal-to-noise ration and attenuating seismic random noise by preserving important features and information about seismic signals, we introduce the concept of anisotropic total fractional order denoising algorithm. The anisotropic total fractional order variation model defined in fractional order bounded variation is proposed as a regularization in seismic denoising. The split Bregman algorithm is employed to solve the minimization problem of the anisotropic total fractional order variation model and the corresponding denoising algorithm for the proposed method is derived. We test the effectiveness of theproposed method for synthetic and real seismic data sets and the denoised result is compared with F-X deconvolution and non-local means denoising algorithm.

Keywords: anisotropic total fractional order variation, fractional order bounded variation, seismic random noise attenuation, split Bregman algorithm

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Authors: Joan O. Ogundele, Aladesanmi A. Oshodi, Adekunle I. Amoo

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Abstract In-vitro multi-enzyme protein digestibility (IVMPD) and some anti-nutritional factors (ANF) of five melon (egusi) seed flours (MSF) and their protein isolates (PI) were carried out. Their PI have potentials comparable to that of soya beans. It is important to know the IVMPD and ANF of these protein sources as to ensure their safety when adapted for use as alternate protein sources to substitute for cow milk, which is relatively expensive in Nigeria. Standard methods were used to produce PI of Citrullus colocynthis, Citrullus vulgaris, African Wine Kettle gourd (Lageneria siceraria I), Basket Ball gourd (Lagenaria siceraria II) and Bushel Giant Gourd (Lageneria siceraria III) seeds and to determine the ANF and IVMPD of the MSF and PI unheated and at 37oC. Multi-enzymes used were trypsin, chymotrypsin and peptidase. IVMPD of MSF ranged from (70.67±0.70) % (C. vulgaris) to (72.07± 1.79) % (L.siceraria I) while for their PI ranged from 74.33% (C.vulgaris) to 77.55% (L.siceraria III). IVMPD of the PI were higher than those of MSF. Heating increased IVMPD of MSF with average value of 79.40% and those of PI with average of 84.14%. ANF average in MSF are tannin (0.11mg/g), phytate (0.23%). Differences in IVMPD of MSF and their PI at different temperatures may arise from processing conditions that alter the release of amino acids from proteins by enzymatic processes. ANF in MSF were relatively low, but were found to be lower in the PI, therefor making the PI safer for human consumption as an alternate source of protein.

Keywords: Anti-nutrients, Enzymatic protein digestibility, Melon (egusi)., Protein Isolates.

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Authors: Seval Yilmaz, Filiz Bayrakci Karel, Ali Savas Koparal

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Textile products produced by leather have been indispensable for human consumption. Various chemicals are used to enhance the durability of end-products in the processing of leather products. The wastewaters from the leather industry which contain these chemicals exhibit toxic effects on the receiving environment and threaten the natural ecosystem. In this study, leather industry wastewater (LIW), which has high loads of contaminants, was treated using advanced treatment techniques instead of conventional methods. During the experiments, the performance of electrochemical methods was investigated. During the electrochemical experiments, the performance of batch electrooxidation (EO) using boron-doped diamond (BDD) electrodes with monopolar configuration for removal of chemical oxygen demand (COD) from LIW were investigated. The influences of electrolysis time, current density (which varies as 5 mA/cm², 10 mA/cm², 20 mA/cm², 30 mA/cm², 50 mA/cm²) and initial pH (which varies as 3,80 (natural pH of LIW), 7, 9) on removal efficiency were investigated in a batch stirred cell to determine the best treatment conditions. The current density applied to the electrochemical reactors is directly proportional to the consumption of electric energy, so electrical energy consumption was monitored during the experiment. The best experimental conditions obtained in electrochemical studies were as follows: electrolysis time = 60 min, current density = 30.0 mA/cm², pH 7. Using these parameters, 53.59% COD removal rates for LIW was achieved and total energy consumption was obtained as 13.03 kWh/m³. It is concluded that electrooxidation process constitutes a plausible and developable method for the treatment of LIW.

Keywords: BDD electrodes, COD removal, electrochemical treatment, leather industry wastewater

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Authors: Ardita Malaj, Ahmad R. Alsaber, Bedour Alboloushi, Anwaar Alkandari

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Organizations worldwide, particularly in Kuwait, are concerned with implementing a progressive workplace culture and fostering social innovation behaviours. The main aim of this research is to examine and establish a thorough comprehension of the relationship between an inventive organizational culture, employee intrapreneurial behaviour, authentic leadership, employee job satisfaction, and employee job commitment in the manufacturing sector of Kuwait, which is a developed economy. Literature reviews analyse the core concepts and their related areas by scrutinizing their definitions, dimensions, and importance to uncover any deficiencies in existing research. The examination of relevant research uncovered major gaps in understanding. This study examines the reliability and validity of a newly developed questionnaire designed to identify the appropriate applications for a large-scale investigation. A preliminary investigation was carried out, determining a sample size of 36 respondents selected randomly from a pool of 223 samples. SPSS was utilized to calculate the percentages of the demographic characteristics for the participants, assess the credibility of the measurements, evaluate the internal consistency, validate all agreements, and determine Pearson's correlation. The study's results indicated that the majority of participants were male (66.7%), aged between 35 and 44 (38.9%), and possessed a bachelor's degree (58.3%). Approximately 94.4% of the participants were employed full-time. 72.2% of the participants are employed in the electrical, computer, and ICT sector, whilst 8.3% work in the metal industry. Out of all the departments, the human resource department had the highest level of engagement, making up 13.9% of the total. Most participants (36.1%) possessed intermediate or advanced levels of experience, whilst 21% were classified as entry-level. Furthermore, 8.3% of individuals were categorized as first-level management, 22.2% were categorized as middle management, and 16.7% were categorized as executive or senior management. Around 19.4% of the participants have over a decade of professional experience. The Pearson's correlation coefficient for all 5 components varies between 0.4009 to 0.7183. The results indicate that all elements of the questionnaire were effectively verified, with a Cronbach alpha factor predominantly exceeding 0.6, which is the criterion commonly accepted by researchers. Therefore, the work on the larger scope of testing and analysis could continue.

Keywords: pilot study, ESIB, innovative organizational culture, Kuwait, validation

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Authors: Mohamed Benaicha, Mahdi Allam

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A new two stage electrochemical process as a safe, large area and low processing cost technique for the production of semi-conducting CuInSe2 (CIS) thin films is studied. CuIn precursors were first potentiostatically electrodeposited onto molybdenum substrates from an acidic thiocyanate electrolyte. In a second stage, the prepared metallic CuIn layers were used as substrate in the selenium electrochemical deposition system and subjected to a thermal treatment in vacuum atmosphere, to eliminate binary phase formation by reaction of the Cu2-x Se and InxSey selenides, leading to the formation of CuInSe2 thin film. Electrochemical selenization from aqueous electrolyte is introduced as an alternative to toxic and hazardous H2Se or Se vapor phase selenization used in physical techniques. In this study, the influence of film deposition parameters such as bath composition, temperature and potential on film properties was studied. The electrochemical, morphological, structural and compositional properties of electrodeposited thin films were characterized using various techniques. Results of Cyclic and Stripping-Cyclic Voltammetry (CV, SCV), Scanning Electron Microscopy (SEM) and Energy Dispersive X-Ray microanalysis (EDX) investigations revealed good reproducibility and homogeneity of the film composition. Thereby optimal technological parameters for the electrochemical production of CuIn, Se as precursors for CuInSe2 thin layers are determined.

Keywords: photovoltaic, CIGS, copper alloys, electrodeposition, thin films

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Authors: Umm-E-Hani, Faiza Babar, Hanif Durad

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Malware-detecting approaches suffer many limitations, due to which all anti-malware solutions have failed to be reliable enough for detecting zero-day malware. Signature-based solutions depend upon the signatures that can be generated only when malware surfaces at least once in the cyber world. Another approach that works by detecting the anomalies caused in the environment can easily be defeated by diligently and intelligently written malware. Solutions that have been trained to observe the behavior for detecting malicious files have failed to cater to the malware capable of detecting the sandboxed or protected environment. Machine learning and deep learning-based approaches greatly suffer in training their models with either an imbalanced dataset or an inadequate number of samples. AI-based anti-malware solutions that have been trained with enough samples targeted a selected feature vector, thus ignoring the input of leftover features in the maliciousness of malware just to cope with the lack of underlying hardware processing power. Our research focuses on producing an anti-malware solution for detecting malicious PE files by circumventing the earlier-mentioned shortcomings. Our proposed framework, which is based on automated feature engineering through autoencoders, trains the model over a fairly large dataset. It focuses on the visual patterns of malware samples to automatically extract the meaningful part of the visual pattern. Our experiment has successfully produced a state-of-the-art accuracy of 99.54 % over test data.

Keywords: malware, auto encoders, automated feature engineering, classification

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Authors: Sarekha Woranuch, Rangrong Yoksan

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Active packaging has been developed based on the incorporation of certain additives, in particular antimicrobial and antioxidant agents, into packaging systems to maintain or extend product quality and shelf-life. Ferulic acid is one of the most effective natural phenolic antioxidants, which has been used in food, pharmaceutical and active packaging film applications. However, most phenolic compounds are sensitive to oxygen, light and heat; its activities are thus lost during product formulation and processing. Grafting ferulic acid onto polymer is an alternative to reduce its loss under thermal processes. Therefore, the objectives of the present research were to study the thermal stability of ferulic acid after grafting onto chitosan, and to investigate the possibility of using ferulic acid-grafted chitosan (FA-g-CTS) as an antioxidant for active biodegradable packaging film. FA-g-CTS was incorporated into biodegradable film via a two-step process, i.e. compounding extrusion at temperature up to 150 °C followed by blown film extrusion at temperature up to 175 °C. Although incorporating FA-g-CTS with a content of 0.02–0.16% (w/w) caused decreased water vapor barrier property and reduced extensibility, the films showed improved oxygen barrier property and antioxidant activity. Radical scavenging activity and reducing power of the film containing FA-g-CTS with a content of 0.04% (w/w) were higher than that of the naked film about 254% and 94%, respectively. Tensile strength and rigidity of the films were not significantly affected by adding FA-g-CTS with a content of 0.02–0.08% (w/w). The results indicated that FA-g-CTS could be potentially used as an antioxidant for active packaging film.

Keywords: active packaging film, antioxidant activity, chitosan, ferulic acid

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Authors: Krishna Mohan Bathula, Fatou Bintou Loucoubar, FNU Kaleemunnisa, Christelle Scharff, Mark Anthony De Castro

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Voice recognition algorithms such as automatic speech recognition and text-to-speech systems with African languages can play an important role in bridging the digital divide of Artificial Intelligence in Africa, contributing to the establishment of a fully inclusive information society. This paper proposes a Deep Learning model that can classify the user responses as inputs for an interactive voice response system. A dataset with Wolof language words ‘yes’ and ‘no’ is collected as audio recordings. A two stage Data Augmentation approach is adopted for enhancing the dataset size required by the deep neural network. Data preprocessing and feature engineering with Mel-Frequency Cepstral Coefficients are implemented. Convolutional Neural Networks (CNNs) have proven to be very powerful in image classification and are promising for audio processing when sounds are transformed into spectra. For performing voice response classification, the recordings are transformed into sound frequency feature spectra and then applied image classification methodology using a deep CNN model. The inference model of this trained and reusable Wolof voice response recognition system can be integrated with many applications associated with both web and mobile platforms.

Keywords: automatic speech recognition, interactive voice response, voice response recognition, wolof word classification

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Authors: Mohammad Talei, Neda Ranjbar Nosheri, Reza Kazemi Gorzadini

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Today municipalities are searching for the new tools for increasing the public participation in different levels of urban planning. This approach of urban planning involves the community in planning process using participatory approaches instead of the long traditional top-down planning methods. These tools can be used to obtain the particular problems of urban furniture form the residents’ point of view. One of the tools that is designed with this goal is public participation GIS (PPGIS) that enables citizen to record and following up their feeling and spatial knowledge regarding main problems of the city, specifically urban furniture, in the form of maps. However, despite the good intentions of PPGIS, its practical implementation in developing countries faces many problems including the lack of basic supporting infrastructure and services and unavailability of sophisticated public participatory models. In this research we develop a PPGIS using of Web 2 to collect voluntary geodataand to perform spatial analysis based on Spatial OnLine Analytical Processing (SOLAP) and Spatial Data Mining (SDM). These tools provide urban planners with proper informationregarding the type, spatial distribution and the clusters of reported problems. This system is implemented in a case study area in Tehran, Iran and the challenges to make it applicable and its potential for real urban planning have been evaluated. It helps decision makers to better understand, plan and allocate scarce resources for providing most requested urban furniture.

Keywords: PPGIS, spatial information, urbanizing futures, urban planning

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Authors: Nabil Hameed Al-Farsi

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This study’s main goal is to develop a model and a maintenance strategy for a cement factory called Arabian Cement Company, Rabigh Plant. The proposed work here depends on Reliability centric maintenance approach to develop a strategy and maintenance schedule that ensures increasing the reliability of the production system components, thus ensuring continuous productivity. The cost-effective maintenance of the plant’s dependability performance is the key goal of durability-based maintenance is. The cement plant consists of 7 important steps, so, developing a maintenance plan based on Reliability centric maintenance (RCM) method is made up of 10 steps accordingly starting from selecting units and data until performing and updating the model. The processing unit chosen for the analysis of this case is the calcinatory unit regarding model’s validation and the Travancore Titanium Products Ltd (TTP) using the claimed data history acquired from the maintenance department maintenance from the mentioned company. After applying the proposed model, the results of the maintenance simulation justified the plant's existing scheduled maintenance policy being reconsidered. Results represent the need for preventive maintenance for all Class A criticality equipment instead of the planned maintenance and the breakdown one for all other equipment depends on its criticality and an FMEA report. Consequently, the additional cost of preventive maintenance would be offset by the cost savings from breakdown maintenance for the remaining equipment.

Keywords: engineering, reliability, strategy, maintenance, failure modes, effects and criticality analysis (FMEA)

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Authors: Josiel Delgadillo, Johnson Kinyua, Charles Mutigwe

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The purpose of sentiment analysis is to determine the sentiment strength (e.g., positive, negative, neutral) from a textual source for good decision-making. Natural language processing in domains such as financial markets requires knowledge of domain ontology, and pre-trained language models, such as BERT, have made significant breakthroughs in various NLP tasks by training on large-scale un-labeled generic corpora such as Wikipedia. However, sentiment analysis is a strong domain-dependent task. The rapid growth of social media has given users a platform to share their experiences and views about products, services, and processes, including financial markets. StockTwits and Twitter are social networks that allow the public to express their sentiments in real time. Hence, leveraging the success of unsupervised pre-training and a large amount of financial text available on social media platforms could potentially benefit a wide range of financial applications. This work is focused on sentiment analysis using social media text on platforms such as StockTwits and Twitter. To meet this need, SkyBERT, a domain-specific language model pre-trained and fine-tuned on financial corpora, has been developed. The results show that SkyBERT outperforms current state-of-the-art models in financial sentiment analysis. Extensive experimental results demonstrate the effectiveness and robustness of SkyBERT.

Keywords: BERT, financial markets, Twitter, sentiment analysis

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Authors: Ahmed Negm, George Aggidis, Xiandong Ma

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With the diverse nature of urban cities, customer demand patterns, landscape topologies or even seasonal weather trends; managing our water distribution networks (WDNs) has proved a complex task. These unpredictable circumstances manifest as pipe failures, intermittent supply and burst events thus adding to water loss, energy waste and increased carbon emissions. Whilst these events are unavoidable, advanced pressure management has proved an effective tool to control and mitigate them. Henceforth, water utilities have struggled with developing a real-time control method that is resilient when confronting the challenges of water distribution. In this paper we use deep reinforcement learning (DRL) algorithms as a novel pressure control strategy to minimise pressure violations and leakage under both burst and background leakage conditions. Agents based on asynchronous actor critic (A2C) and recurrent proximal policy optimisation (Recurrent PPO) were trained and compared to benchmarked optimisation algorithms (differential evolution, particle swarm optimisation. A2C manages to minimise leakage by 32.48% under burst conditions and 67.17% under background conditions which was the highest performance in the DRL algorithms. A2C and Recurrent PPO performed well in comparison to the benchmarks with higher processing speed and lower computational effort.

Keywords: deep reinforcement learning, pressure management, water distribution networks, leakage management

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Authors: Ming-Yang Guo, Cheng-Xian Wu, Wei-Xiang Chen, Chun-Yuan Lin, Yen-Jen Lin, Ann-Shyn Chiang

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With more and more Drosophila Driver and Neuron images, it is an important work to find the similarity relationships among them as the functional inference. There is a general problem that how to find a Drosophila Driver image, which can cover a set of Drosophila Driver/Neuron images. In order to solve this problem, the intersection/union region for a set of images should be computed at first, then a comparison work is used to calculate the similarities between the region and other images. In this paper, three encoding schemes, namely Integer, Boolean, Decimal, are proposed to encode each image as a one-dimensional structure. Then, the intersection/union region from these images can be computed by using the compare operations, Boolean operators and lookup table method. Finally, the comparison work is done as the union region computation, and the similarity score can be calculated by the definition of Tanimoto coefficient. The above methods for the region computation are also implemented in the multi-core CPUs environment with the OpenMP. From the experimental results, in the encoding phase, the performance by the Boolean scheme is the best than that by others; in the region computation phase, the performance by Decimal is the best when the number of images is large. The speedup ratio can achieve 12 based on 16 CPUs. This work was supported by the Ministry of Science and Technology under the grant MOST 106-2221-E-182-070.

Keywords: Drosophila driver image, Drosophila neuron images, intersection/union computation, parallel processing, OpenMP

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Authors: Priyadharshini Ahrumugam

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Cooperative groups through much research have been recognized to churn remarkable achievements instead of solitary or individualistic efforts. Based on Johnson and Johnson’s model of cooperative learning, the five key components of cooperation are positive interdependence, face-to-face promotive interaction, individual accountability, social skills and group processing. In 2011, the Malaysian Ministry of Higher Education (MOHE) introduced the Holistic Student Development policy with the aim to develop morally sound individuals equipped with lifelong learning skills. The Community Service project was included in the improvement initiative. The purpose of this study is to assess the relationship of team-based learning in facilitating particularly students’ positive interdependence and face-to-face promotive interaction. The research methods involve in-depth interviews with the team leaders and selected team members, and a content analysis of the undergraduate students’ reflective journals. A significant positive relationship was found between students’ progressive outlook towards teamwork and the highlighted two components. The key findings show that students have gained in their individual learning and work results through teamwork and interaction with other students. The inclusion of Community Service as a MOHE subject resonates with cooperative learning methods that enhances supportive relationships and develops students’ social skills together with their professional skills.

Keywords: community service, cooperative learning, positive interdependence, teamwork

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Authors: Yuri Kitahara

Abstract:

Background: Cognitive reappraisal refers to an emotion regulation strategy in which one changes the interpretation of emotion-eliciting events. Numerous studies show that cognitive reappraisal is associated with mental health and better social functioning. However the examination of the predictive factors of adaptive emotion regulation remains as an issue. The present study examined the factors contributing to the habitual use of cognitive reappraisal, with a focus on emotional awareness and working memory. Methods: Data was collected from 30 junior high school students, using a Japanese version of the Emotion Regulation Questionnaire (ERQ), the Levels of Emotional Awareness Scale for Children (LEAS-C), and N-back task. Results: A positive correlation between emotional awareness and cognitive reappraisal was observed in the high-working-memory group (r = .54, p < .05), whereas no significant relationship was found in the low-working-memory group. In addition, the results of the analysis of variance (ANOVA) showed a significant interaction between emotional awareness and working memory capacity (F(1, 26) = 7.74, p < .05). Subsequent analysis of simple main effects confirmed that high working memory capacity significantly increases the use of cognitive reappraisal for high-emotional-awareness subjects, and significantly decreases the use of cognitive reappraisal for low-emotional-awareness subjects. Discussion: These results indicate that under the condition when one has an adequate ability for simultaneous processing of information, explicit understanding of emotion would contribute to adaptive cognitive emotion regulation. The findings are discussed along with neuroscientific claims.

Keywords: cognitive reappraisal, emotional awareness, emotion regulation, working memory

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Authors: Saron Adisu Gezahegn

Abstract:

Commercial cosmetic has been affecting human health due to their contents and dosage composition. Chemical base cosmetics exposes users to unnecessary health problems and financial cost. Some of the cosmetics' interaction with the environment has negative impacts on health such as burning, cracking, coloring, and so on. The users are looking for a temporary service without evaluating the side effects of cosmetics that contain chemical compositions that result in irritation, burning, allergies, cracking, and the nature of the face. Every cosmetic contains a heavy metal such as lead, zinc, cadmium, silicon, and other heavy cosmetics materials. The users may expose at the end of the day to untreatable diseases like cancer. The objective of the research is to study the effects of indigenous biological face treatment without any additives like chemicals. In ancient times this thought was highly tremendous in the world but things were changing bit by bit and reached chemical base cosmetics to maintain the beauty of hair, skin, and faces. The side effects of the treatment on the face were minimum and the side effects with the interaction of the environment were almost nil. But this thought is changed and replaces the indigenous substances with chemical substances by adding additives like heavy chemical lead and cadmium in the sense of preservation, pigments, dye, and shining. Various studies indicated that cosmetics have dangerous side effects that expose users to health problems and expensive financial loss. This study focuses on a local indigenous plant called Kulkual. Kulkual is available everywhere in a study area and sustainable products can harvest to use as indigenous face treatment materials.25 men and 25 women were selected as a sample population randomly to conduct the study effectively.The plant is harvested from the guard in the productive season. The plant was exposed to the sun dry for a week. Then the peel was removed from the plant fruit and the peels were taken to a bath filled with water to soak for three days. Then the flesh of the peel was avoided from the fruit and ready to use as a face treatment. The fleshy peel was smeared on each sample for almost a week and continued for a week. The result indicated that the effects of the treatment were a positive response with minimum cost and minimum side effects due to the environment. The beauty shines, smoothness, and color are better than chemical base cosmetics. Finally, the study is recommended that all users prefer a biological method of treatment with minimum cost and minimums side effects on health with the interaction of the environment.

Keywords: cosmetic, indigneous, heavymetals, toxic

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Authors: E. E. S. Uy, T. Noda, K. Nakai, J. R. Dungca

Abstract:

Cyclic threshold shear strain is the cyclic shear strain amplitude that serves as the indicator of the development of pore water pressure. The parameter can be obtained by performing either cyclic triaxial test, shaking table test, cyclic simple shear or resonant column. In a cyclic triaxial test, other researchers install measuring devices in close proximity of the soil to measure the parameter. In this study, an attempt was made to estimate the cyclic threshold shear strain parameter using full-field measurement technique. The technique uses a camera to monitor and measure the movement of the soil. For this study, the technique was incorporated in a strain-controlled consolidated undrained cyclic triaxial test. Calibration of the camera was first performed to ensure that the camera can properly measure the deformation under cyclic loading. Its capacity to measure deformation was also investigated using a cylindrical rubber dummy. Two-dimensional image processing was implemented. Lucas and Kanade optical flow algorithm was applied to track the movement of the soil particles. Results from the full-field measurement technique were compared with the results from the linear variable displacement transducer. A range of values was determined from the estimation. This was due to the nonhomogeneous deformation of the soil observed during the cyclic loading. The minimum values were in the order of 10-2% in some areas of the specimen.

Keywords: cyclic loading, cyclic threshold shear strain, full-field measurement, optical flow

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Authors: Neha Sharma, Kanthi K. Kondepudi, Naveen Gupta

Abstract:

Phytases are phytate specific phosphatases catalyzing the step-wise dephosphorylation of phytate, which acts as an anti-nutritional factor in food due to its strong binding capacity to minerals. In recent years microbial phytases have been explored for improving nutritional quality of food. But the major limitation is acceptability of phytases from these microorganisms. Therefore, efforts are being made to isolate organisms which are generally regarded as safe for human consumption such as Lactic Acid Bacteria (LAB). Phytases from these organisms will have an edge over other phytase sources due to its probiotic attributes. Only few LAB have been reported to give phytase activity that too is generally seen as intracellular. LAB producing extracellular phytase will be more useful as it can degrade phytate more effectively. Moreover, enzyme from such isolate will have application in food processing also. Only few species of Lactobacillus producing extracellular phytase have been reported so far. This study reports the isolation of a probiotic strain of Lactobacillus fermentum spp KA1 which produces extracellular phytase. Conditions for the optimal production of phytase have been optimized and the enzyme production resulted in an approximately 13-fold increase in yield. The phytate degradation potential of extracellular phytase in rice bran has been explored and conditions for optimal degradation were optimized. Under optimal conditions, there was 43.26% release of inorganic phosphate and 6.45% decrease of phytate content.

Keywords: Lactobacillus, phytase, phytate reduction, rice bran

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Authors: Aniket S. Joshi, Aditya R. Mane, Arjun Tukaram

Abstract:

In the 21st century, in the era of e-world, people are continuously getting updated by daily information such as weather conditions, news, stock exchange market updates, new projects, cricket updates, sports and other such applications. In the busy situation, they want this information on the little use of keyboard, time. Today in order to get such information user have to repeat same mouse and keyboard actions which includes time and inconvenience. In India due to rural background many people are not much familiar about the use of computer and internet also. Also in small clinics, small offices, and hotels and in the airport there should be a system which retrieves daily information with the minimum use of keyboard and mouse actions. We plan to design application based project that can easily retrieve information with minimum use of keyboard and mouse actions and make our task more convenient and easier. This can be possible with an image processing application which takes real time hand gestures which will get matched by system and retrieve information. Once selected the functions with hand gestures, the system will report action information to user. In this project we use real time hand gesture movements to select required option which is stored on the screen in the form of RSS Feeds. Gesture will select the required option and the information will be popped and we got the information. A real time hand gesture makes the application handier and easier to use.

Keywords: hand detection, hand tracking, hand gesture recognition, HSV color model, Blob detection

Procedia PDF Downloads 266

Authors: Daifeng Hao, Guang Feng, Jingfeng Zhao, Tao Li, Xiaoye Tuo

Abstract:

Objective: To retrospectively analyze the clinical characteristics of incision infection with sternal osteomyelitis sinus tract after cardiac surgery and the operation method and therapeutic effect of filling and repairing with activated PRP gel. Methods: From March 2011 to October 2022, 142 cases of incision infection after cardiac surgery with sternal osteomyelitis sinus were retrospectively analyzed, and the causes of poor wound healing after surgery, wound characteristics, perioperative wound management were summarized. Treatment during operation, collection and storage process of autologous PRP before debridement surgery, PRP filling repair and activation method after debridement surgery, effect of anticoagulant drugs on surgery, postoperative complications and average wound healing time, etc.. Results: Among the cases in this group, 53.3% underwent coronary artery bypass grafting, 36.8% underwent artificial heart valve replacement, 8.2% underwent aortic artificial vessel replacement, and 1.7% underwent allogeneic heart transplantation. The main causes of poor incision healing were suture reaction, fat liquefaction, osteoporosis, diabetes, and metal allergy in sequence. The wound is characterized by an infected sinus tract. Before the operation, 100-150ml of PRP with 4 times the physiological concentration was collected separately with a blood component separation device. After sinus debridement, PRP was perfused to fill the bony defect in the middle of the sternum, activated with thrombin freeze-dried powder and calcium gluconate injection to form a gel, and the outer skin and subcutaneous tissue were sutured freely. 62.9% of patients discontinued warfarin during the perioperative period, and 37.1% of patients maintained warfarin treatment. There was no significant difference in the incidence of postoperative wound hematoma. The average postoperative wound healing time was 12.9±4.7 days, and there was no obvious postoperative complication. Conclusions: Application of activated PRP gel to fill incision infection with sternal osteomyelitis sinus after cardiac surgery has a less surgical injury and satisfactory and stable curative effect. It can completely replace the previously used pectoralis major muscle flap transplantation operation scheme.

Keywords: platelet-rich plasma, negative-pressure wound therapy, sternal osteomyelitis, cardiac surgery

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Authors: Magdalena Diaka, Paweł Mazierski, Joanna Żebrowska, Michał Winiarski, Tomasz Klimczuk, Adriana Zaleska-Medynska

Abstract:

During the last years, TiO2 nanotubes have been widely studied due to their unique highly ordered array structure, unidirectional charge transfer and higher specific surface area compared to conventional TiO2 powder. These photoactive materials, in the form of thin layer, can be activated by low powered and low cost irradiation sources (such as LEDs) to remove VOCs, microorganism and to deodorize air streams. This is possible due to their directly growth on a support material and high surface area, which guarantee enhanced photon absorption together with an extensive adsorption of reactant molecules on the photocatalyst surface. TiO2 nanotubes exhibit also lots of other attractive properties, such as potential enhancement of electron percolation pathways, light conversion, and ion diffusion at the semiconductor-electrolyte interface. Pure TiO2 nanotubes were previously used to remove organic compounds from the gas phase as well as in water splitting reaction. The major factors limiting the use of TiO2 nanotubes, which have not been fully overcome, are their relatively large band gap (3-3,2 eV) and high recombination rate of photogenerated electron–hole pairs. Many different strategies were proposed to solve this problem, however titania nanostructures containing incorporated metal oxides like Ag2O shows very promising, new optical and photocatalytic properties. Unfortunately, there is still very limited number of reports regarding application of TiO2/MxOy nanostructures. In the present work, we prepared TiO2/Ag2O nanotubes obtained by anodization of Ti-Ag alloys containing 5, 10 and 15 wt. % Ag. Photocatalysts prepared in this way were characterized by X-ray diffraction spectroscopy (XRD), scanning electron microscopy (SEM), luminescence spectroscopy and UV-Vis spectroscopy. The activities of new TiO2/Ag2O were examined by photocatalytic degradation of toluene in gas phase reaction and phenol in aqueous phase using 1000 W Xenon lamp (Oriel) and light emitting diodes (LED) as a irradiation sources. Additionally efficiency of bacteria (Pseudomonas aeruginosa) removal from the gas phase was estimated. The number of surviving bacteria was determined by the serial twofold dilution microtiter plate method, in Tryptic Soy Broth medium (TSB, GibcoBRL).

Keywords: photocatalysis, antibacterial properties, titania nanotubes, new TiO2/MxOy nanostructures

Procedia PDF Downloads 279