Search results for: labeling automation

Authors: Ankur Dixit, Hiroaki Wagatsuma

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The crack detection on a concrete bridge is an important and constant task in civil engineering. Chronically, humans are checking the bridge for inspection of cracks to maintain the quality and reliability of bridge. But this process is very long and costly. To overcome such limitations, we have used a drone with a digital camera, which took some images of bridge deck and these images are processed by morphological component analysis (MCA). MCA technique is a very strong application of sparse coding and it explores the possibility of separation of images. In this paper, MCA has been used to decompose the image into coarse and fine components with the effectiveness of two dictionaries namely anisotropic diffusion and wavelet transform. An anisotropic diffusion is an adaptive smoothing process used to adjust diffusion coefficient by finding gray level and gradient as features. These cracks in image are enhanced by subtracting the diffused coarse image into the original image and the results are treated by Sobel edge detector and binary filtering to exhibit the cracks in a fine way. Our results demonstrated that proposed MCA framework using anisotropic diffusion followed by Sobel operator and binary filtering may contribute to an automation of crack detection even in open field sever conditions such as bridge decks.

Keywords: anisotropic diffusion, coarse component, fine component, MCA, Sobel edge detector and wavelet transform

Procedia PDF Downloads 153

Authors: Nadia Yaghoobi, Seyed Majid Esmaeilzadeh

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Due to advancements in wireless communication, new sensor capabilities have been created. In addition to the automation industry, the Internet of Things (IoT) has been used in environmental issues and has provided the possibility of communication between different devices for data collection and exchange. Water quality depends on many factors which are essential for maintaining the minimum sustainability of water. Regarding the great dependence of fishes on the quality of the aquatic environment, water quality can directly affect their activity. Therefore, monitoring water quality is an important issue to consider, especially in the fish farming industry. The conventional method of water quality testing is to collect water samples manually and send them to a laboratory for testing and analysis. This time-consuming method is a waste of manpower and is not cost-effective. The water quality measurement system implemented in this project monitors water quality in real-time through various sensors (parameters: water temperature, water level, dissolved oxygen, humidity and ambient temperature, water turbidity, PH). The Wi-Fi module, ESP8266, transmits data collected by sensors wirelessly to ThingSpeak and the smartphone app. Also, with the help of these instantaneous data, water temperature and water level can be controlled by using a heater and a water pump, respectively. This system can have a detailed study of the pollution and condition of water resources and can provide an environment for safe fish farming.

Keywords: dissolved oxygen, IoT, monitoring, ThingSpeak, water level, water quality, WiFi module

Procedia PDF Downloads 163

Authors: Iqra Yaseen

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One of the well-known areas of computer science and engineering, image processing in the context of computer vision has been essential to automation. In remote sensing, medical science, and many other fields, it has made it easier to uncover previously undiscovered facts. Grading of diverse items is now possible because of neural network algorithms, categorization, and digital image processing. Its use in the classification of agricultural products, particularly in the grading of seeds or grains and their cultivars, is widely recognized. A grading and sorting system enables the preservation of time, consistency, and uniformity. Global population growth has led to an increase in demand for food staples, biofuel, and other agricultural products. To meet this demand, available resources must be used and managed more effectively. Image processing is rapidly growing in the field of agriculture. Many applications have been developed using this approach for crop identification and classification, land and disease detection and for measuring other parameters of crop. Vegetation localization is the base of performing these task. Vegetation helps to identify the area where the crop is present. The productivity of the agriculture industry can be increased via image processing that is based upon Unmanned Aerial Vehicle photography and satellite. In this paper we use the machine learning techniques like Convolutional Neural Network, deep learning, image processing, classification, You Only Live Once to UAV imaging dataset to divide the crop into distinct groups and choose the best way to use it.

Keywords: image processing, UAV, YOLO, CNN, deep learning, classification

Procedia PDF Downloads 71

Authors: Marrone Silverio Melo Dantas Pedro Henrique Dreyer, Gabriel Fonseca Reis de Souza, Daniel Bezerra, Ricardo Souza, Silvia Lins, Judith Kelner, Djamel Fawzi Hadj Sadok

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The creation of a dataset is time-consuming and often discourages researchers from pursuing their goals. To overcome this problem, we present and discuss two solutions adopted for the automation of this process. Both optimize valuable user time and resources and support video object segmentation with object tracking and 3D projection. In our scenario, we acquire images from a moving robotic arm and, for each approach, generate distinct annotated datasets. We evaluated the precision of the annotations by comparing these with a manually annotated dataset, as well as the efficiency in the context of detection and classification problems. For detection support, we used YOLO and obtained for the projection dataset an F1-Score, accuracy, and mAP values of 0.846, 0.924, and 0.875, respectively. Concerning the tracking dataset, we achieved an F1-Score of 0.861, an accuracy of 0.932, whereas mAP reached 0.894. In order to evaluate the quality of the annotated images used for classification problems, we employed deep learning architectures. We adopted metrics accuracy and F1-Score, for VGG, DenseNet, MobileNet, Inception, and ResNet. The VGG architecture outperformed the others for both projection and tracking datasets. It reached an accuracy and F1-score of 0.997 and 0.993, respectively. Similarly, for the tracking dataset, it achieved an accuracy of 0.991 and an F1-Score of 0.981.

Keywords: RJ45, automatic annotation, object tracking, 3D projection

Procedia PDF Downloads 137

Authors: Alper Taner, Yesim Benal Oztekin, Huseyin Duran

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In this study, an Artificial Neural Network (ANN) was developed in order to classify barley varieties. For this purpose, physical properties of barley varieties were determined and ANN techniques were used. The physical properties of 8 barley varieties grown in Turkey, namely thousand kernel weight, geometric mean diameter, sphericity, kernel volume, surface area, bulk density, true density, porosity and colour parameters of grain, were determined and it was found that these properties were statistically significant with respect to varieties. As ANN model, three models, N-l, N-2 and N-3 were constructed. The performances of these models were compared. It was determined that the best-fit model was N-1. In the N-1 model, the structure of the model was designed to be 11 input layers, 2 hidden layers and 1 output layer. Thousand kernel weight, geometric mean diameter, sphericity, kernel volume, surface area, bulk density, true density, porosity and colour parameters of grain were used as input parameter; and varieties as output parameter. R2, Root Mean Square Error and Mean Error for the N-l model were found as 99.99%, 0.00074 and 0.009%, respectively. All results obtained by the N-l model were observed to have been quite consistent with real data. By this model, it would be possible to construct automation systems for classification and cleaning in flourmills.

Keywords: physical properties, artificial neural networks, barley, classification

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Authors: Jie Zhao, Meng Su

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Image recognition, as one of the most critical technologies in computer vision, works to help machine-like robotics understand a scene, that is, if deployed appropriately, will trigger the revolution in remote sensing and industry automation. With the developments of AI technologies, there are many prevailing and sophisticated neural networks as technologies developed for image recognition. However, computer vision platforms as hardware, supporting neural networks for image recognition, as crucial as the neural network technologies, need to be more congruently addressed as the research subjects. In contrast, different computer vision platforms are deterministic to leverage the performance of different neural networks for recognition. In this paper, three different computer vision platforms – Jetson Nano(with 4GB), a standalone laptop(with RTX 3000s, using CUDA), and Google Colab (web-based, using GPU) are explored and four prominent neural network architectures (including AlexNet, VGG(16/19), GoogleNet, and ResNet(18/34/50)), are investigated. In the context of pairwise usage between different computer vision platforms and distinctive neural networks, with the merits of recognition accuracy and time efficiency, the performances are evaluated. In the case study using public imageNets, our findings provide a nuanced perspective on optimizing image recognition tasks across Edge-AI platforms, offering guidance on selecting appropriate neural network structures to maximize performance under hardware constraints.

Keywords: alexNet, VGG, googleNet, resNet, Jetson nano, CUDA, COCO-NET, cifar10, imageNet large scale visual recognition challenge (ILSVRC), google colab

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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: Jin Ho Kim, Yujeong Shin, Seong-Jin Cho, Dong-Jin Kim, U-Syn Ha

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Due to the development of the information industry and technologies, cellular phones have must not only function to communicate, but also have functions such as the Internet, e-banking, entertainment, etc. These phones are called smartphones. The performance of smartphones has improved, because of the various functions of smartphones, and the capacity of the battery has been increased gradually. Recently, linear generators have been embedded in smartphones in order to recharge the smartphone's battery. In this study, optimization is performed and an array change of permanent magnets is examined in order to increase efficiency. We propose an optimal design using design of experiments (DOE) to maximize the generated induced voltage. The thickness of the poleshoe and permanent magnet (PM), the height of the poleshoe and PM, and the thickness of the coil are determined to be design variables. We made 25 sampling points using an orthogonal array according to four design variables. We performed electromagnetic finite element analysis to predict the generated induced voltage using the commercial electromagnetic analysis software ANSYS Maxwell. Then, we made an approximate model using the Kriging algorithm, and derived optimal values of the design variables using an evolutionary algorithm. The commercial optimization software PIAnO (Process Integration, Automation, and Optimization) was used with these algorithms. The result of the optimization shows that the generated induced voltage is improved.

Keywords: smartphone, linear generator, design of experiment, approximate model, optimal design

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Authors: Meng Wang

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Historically, young members (children) in the society have been oppressed by adults through direct violent acts. Direct violence was evident in rampant child labor and child maltreatment cases. After acknowledging the rights of children from the United Nations, it is believed in public that children have been protected against direct physical violence. Nevertheless, at present, this paper argues from Foucauldian and disability study standpoints that similar to the old times, children are oppressed objects in the context of child play, which is constructed by adults to substitute direct violence in regulating children. Particularly, this paper suggests that on the one hand, preschool play is a new way that adults adopt to oppress preschoolers and regulate the society as a whole; on the other hand, preschoolers are taught how to play as an acquired skill and master self-regulation through play. There is a line of contemporary research that centers on child play from social constructivism perspective. Yet, current teaching practices pertaining to child play including guided child play and free play, in fact, serve the interest of adults and society at large. By acknowledging and deconstructing the prevalence of 'evidence-based best practice' in early childhood education field within western society, reconstruction of child-adult power relation could be achieved and alternative truth could be found in early childhood education. To support the argument of this paper, an on-going observational case study is conducted in a preschool setting in the United States. Age range of children is 2.5 to 4 years old. Approximately 10 children (5 boys) are participating in this case study. Observation is conducted throughout the weekdays as children follow through the classroom routine with a lead and an assistant teacher. Classroom teachers are interviewed pertaining to their classroom management strategies. Preliminary research finding of this case study suggested that preschool teachers tended to utilize scenarios from preschoolers’ dramatic play to impart core cultural values to young children. These values were pre-determined by adults. In addition, if young children have failed to follow teachers' guidance in terms of playing in a correct way, children ran the risk of being excluded from the play scenario by peers and adults. Furthermore, this study tended to indicate that through child play, preschoolers are obliged to develop an internal violence system, that is self-regulation skill to regulate their own behavior; and if this internal system is unestablished based on various assessments by adults, then potentially there will be consequences of negative labeling and disabling toward young children intended by adults. In conclusion, this paper applies Foucauldian analysis into the context of child play. At present, within preschool, child play is not free as it seems to be. Young children are expected to perform cultural tasks through their play activities designed by adults. Adults utilize child play as technologies of governmentality to further predict and regulate future society at large.

Keywords: child play, developmentally appropriate practice, DAP, poststructuralism, technologies of governmentality

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Authors: Berhane Gebreslassie

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Conventional commericial buildings are among the highest unwisely consumes enormous amount of energy and as consequence produce significant amount Carbon Dioxide (CO2). Traditional/conventional buildings have been built for years without consideration being given to their impact on the global warming issues as well as their CO2 contributions. Since 1973, simulation of Green Building (GB) for Energy Efficiency started and many countries in particular the US showed a positive response to minimize the usage of energy in respect to reducing the CO2 emission. As a consequence many software companies developed their own unique building energy efficiency simulation software, interfacing interoperability with Building Information Modeling (BIM). The last decade has witnessed very rapid growing number of researches on GB energy efficiency system. However, the study also indicates that the results of current GB simulation are not yet satisfactory to meet the objectives of GB. In addition most of these previous studies are unlikely excluded the studies of ultimate building energy efficiencies simulation. The aim of this project is to meet the objectives of GB by design, modeling and simulation of building ultimate energy efficiencies system. This research project presents multi-level, L-shape office building in which every particular part of the building materials has been tested for energy efficiency. An overall of 78.62% energy is saved, approaching to NetZero energy saving. Furthermore, the building is implements with distributed energy resources like renewable energies and integrating with Smart Building Automation System (SBAS) for controlling and monitoring energy usage.

Keywords: ultimate energy saving, optimum energy saving, green building, sustainable materials and renewable energy

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Authors: Angelo Corallo, Maria Elena Latino, Marta Menegoli

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The link between man and food plays, in the social and economic system, a central role where cultural and multidisciplinary aspects intertwine: food is not only nutrition, but also communication, culture, politics, environment, science, ethics, fashion. This multi-dimensionality has many implications in the food economy. In recent years, the consumer became more conscious about his food choices, involving a consistent change in consumption models. This change concerns several aspects: awareness of food system issues, employment of socially and environmentally conscious decision-making, food choices based on different characteristics than nutritional ones i.e. origin of food, how it’s produced, and who’s producing it. In this frame the ‘consumption choices’ and the ‘interests of the citizen’ become one part of the others. The figure of the ‘Citizen Consumer’ is born, a responsible and ethically motivated individual to change his lifestyle, achieving the goal of sustainable consumption. Simultaneously the branding, that before was guarantee of the product quality, today is questioned. In order to meet these needs, Agri-Food companies are developing specific product lines that follow two main philosophies: ‘Back to basics’ and ‘Less is more’. However, the issue of ethical behavior does not seem to find an adequate on market offer. Most likely due to a lack of attention on the communication strategy used, very often based on market logic and rarely on ethical one. The label in its classic concept of ‘clean labeling’ can no longer be the only instrument through which to convey product information and its evolution towards a concept of ‘clear label’ is necessary to embrace ethical and transparent concepts in progress the process of democratization of the Food System. The implementation of a voluntary traceability path, relying on the technological models of the Internet of Things or Industry 4.0, would enable the Agri-Food Supply Chain to collect data that, if properly treated, could satisfy the information need of consumers. A change of approach is therefore proposed towards Agri-Food traceability that is no longer intended as a tool to be used to respond to the legislator, but rather as a promotional tool useful to tell the company in a transparent manner and then reach the slice of the market of food citizens. The use of mobile technology can also facilitate this information transfer. However, in order to guarantee maximum efficiency, an appropriate communication model based on the ethical communication principles should be used, which aims to overcome the pipeline communication model, to offer the listener a new way of telling the food product, based on real data collected through processes traceability. The Citizen Consumer is therefore placed at the center of the new model of communication in which he has the opportunity to choose what to know and how. The new label creates a virtual access point capable of telling the product according to different point of views, following the personal interests and offering the possibility to give several content modalities to support different situations and usability.

Keywords: agri food traceability, agri-food transparency, clear label, food system, internet of things

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Authors: Alessandra Marino

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This paper thoroughly investigates the design characteristics necessary for intergenerational living and evaluates their applicability within the Italian social panorama in order to identify a model that can serve as a reference for subsequent regulatory adjustments of a new building typology. The applied methodology involves the collaboration of people with various background and architects, all representing the three main ages of life - childhood or youth, adulthood, seniority - through questionnaires aimed at researching the peculiar characteristics that contemporary intergenerational housing should include; the questionnaires are then compared with each other in order to identify any recurring patterns by age group and/or influenced by the specialist knowledge on the subject of the architects compared to the rest of the user sample. The results indicate that among specialist users in the field of architecture, young students identify home automation as the key to the inclusion of the weakest groups within the building, adult architects believe that the identification of intergenerational/community services within the building is the cornerstone, and senior architects focus on widespread spatial accessibility. At the same time, the results among non-specialist users do not identify a significantly diversified model by age group but are generally in agreement in the importance of separation between private environments and collective spaces. The interpretation of the results obtained leads to a compositional study of a new building typology with the future objective of channeling the subsequent outcomes within the regulatory adjustments of the sector.

Keywords: intergenerational living, social sustainability, health, lifestyle, well-being

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Authors: Tahmina Sultana, Yasser Hassan

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Vehicle technologies rapidly evolving due to their multifaceted advantages. Adapted different vehicle technologies like connectivity and automation on the same roads with conventional vehicles controlled by human drivers may increase speed disparity in mixed vehicle technologies. Identifying relationships between speed distribution measures of different vehicles and road geometry can be an indicator of speed disparity in mixed technologies. Previous studies proved that speed disparity measures and traffic accidents are inextricably related. Horizontal curves from three geographic areas were selected based on relevant criteria, and speed data were collected at the midpoint of the preceding tangent and starting, ending, and middle point of the curve. Multiple linear mixed effect models (LME) were developed using the instantaneous speed measures representing the speed of vehicles at different points of horizontal curves to recognize relationships between speed variance (standard deviation) and road geometry. A simulation-based framework (Monte Carlo) was introduced to check the speed disparity on horizontal curves in mixed vehicle technologies when consideration is given to the interactions among connected vehicles (CVs), autonomous vehicles (AVs), and non-connected vehicles (NCVs) on horizontal curves. The Monte Carlo method was used in the simulation to randomly sample values for the various parameters from their respective distributions. Theresults show that NCVs had higher speed variation than CVs and AVs. In addition, AVs and CVs contributed to reduce speed disparity in the mixed vehicle technologies in any penetration rates.

Keywords: autonomous vehicles, connected vehicles, non-connected vehicles, speed variance

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Authors: Jia Li, Xing Wei, Yuchen Hong, Yang Lu

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Detecting vehicle behavior has always been the focus of intelligent transportation, but with the explosive growth of the number of vehicles and the complexity of the road environment, the vehicle behavior videos captured by traditional surveillance have been unable to satisfy the study of vehicle behavior. The traditional method of manually labeling vehicle behavior is too time-consuming and labor-intensive, but the existing object detection and tracking algorithms have poor practicability and low behavioral location detection rate. This paper proposes a vehicle behavior detection algorithm based on the dual-stream convolution network and the multi-dimensional video dynamic detection network. In the videos, the straight-line behavior of the vehicle will default to the background behavior. The Changing lanes, turning and turning around are set as target behaviors. The purpose of this model is to automatically mark the target behavior of the vehicle from the untrimmed videos. First, the target behavior proposals in the long video are extracted through the dual-stream convolution network. The model uses a dual-stream convolutional network to generate a one-dimensional action score waveform, and then extract segments with scores above a given threshold M into preliminary vehicle behavior proposals. Second, the preliminary proposals are pruned and identified using the multi-dimensional video dynamic detection network. Referring to the hierarchical reinforcement learning, the multi-dimensional network includes a Timer module and a Spacer module, where the Timer module mines time information in the video stream and the Spacer module extracts spatial information in the video frame. The Timer and Spacer module are implemented by Long Short-Term Memory (LSTM) and start from an all-zero hidden state. The Timer module uses the Transformer mechanism to extract timing information from the video stream and extract features by linear mapping and other methods. Finally, the model fuses time information and spatial information and obtains the location and category of the behavior through the softmax layer. This paper uses recall and precision to measure the performance of the model. Extensive experiments show that based on the dataset of this paper, the proposed model has obvious advantages compared with the existing state-of-the-art behavior detection algorithms. When the Time Intersection over Union (TIoU) threshold is 0.5, the Average-Precision (MP) reaches 36.3% (the MP of baselines is 21.5%). In summary, this paper proposes a vehicle behavior detection model based on multi-dimensional dynamic detection network. This paper introduces spatial information and temporal information to extract vehicle behaviors in long videos. Experiments show that the proposed algorithm is advanced and accurate in-vehicle timing behavior detection. In the future, the focus will be on simultaneously detecting the timing behavior of multiple vehicles in complex traffic scenes (such as a busy street) while ensuring accuracy.

Keywords: vehicle behavior detection, convolutional neural network, long short-term memory, deep learning

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Authors: Hasan Demiroğlu, Uğur Avcıbaşı, Serhan Sakarya, Perihan Ünak

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Implanted devices are progressively practiced in innovative medicine to relieve pain or improve a compromised function. Implant-associated infections represent an emerging complication, caused by organisms which adhere to the implant surface and grow embedded in a protective extracellular polymeric matrix, known as a biofilm. In addition, the microorganisms within biofilms enter a stationary growth phase and become phenotypically resistant to most antimicrobials, frequently causing treatment failure. In such cases, surgical removal of the implant is often required, causing high morbidity and substantial healthcare costs. Staphylococcus aureus is the most common pathogen causing implant-associated infections. Successful treatment of these infections includes early surgical intervention and antimicrobial treatment with bactericidal drugs that also act on the surface-adhering microorganisms. Linezolid is a promising anti-microbial with ant-staphylococcal activity, used for the treatment of MRSA infections. Linezolid is a synthetic antimicrobial and member of oxazolidinoni group, with a bacteriostatic or bactericidal dose-dependent antimicrobial mechanism against gram-positive bacteria. Intensive use of antibiotics, have emerged multi-resistant organisms over the years and major problems have begun to be experienced in the treatment of infections occurred with them. While new drugs have been developed worldwide, on the other hand infections formed with microorganisms which gained resistance against these drugs were reported and the scale of the problem increases gradually. Scientific studies about the production of bacterial biofilm increased in recent years. For this purpose, we investigated the activity of Lin, Lin radiolabeled with 131I (131I-Lin) and cold iodinated Lin (127I-Lin) against clinical strains of Staphylococcus aureus DSM 4910 in biofilm. In the first stage, radio and cold labeling studies were performed. Quality-control studies of Lin and iodo (radio and cold) Lin derivatives were carried out by using TLC (Thin Layer Radiochromatography) and HPLC (High Pressure Liquid Chromatography). In this context, it was found that the binding yield was obtained to be about 86±2 % for 131I-Lin. The minimal inhibitory concentration (MIC) of Lin, 127I-Lin and 131I-Lin for Staphylococcus aureus DSM 4910 strain were found to be 1µg/mL. In time-kill studies of Lin, 127I-Lin and 131I-Lin were producing ≥ 3 log10 decreases in viable counts (cfu/ml) within 6 h at 2 and 4 fold of MIC respectively. No viable bacteria were observed within the 24 h of the experiments. Biofilm eradication of S. aureus started with 64 µg/mL of Lin, 127I-Lin and 131I-Lin, and OD630 was 0.507±0.0.092, 0.589±0.058 and 0.266±0.047, respectively. The media control of biofilm producing Staphylococcus was 1.675±0,01 (OD630). 131I and 127I did not have any effects on biofilms. Lin and 127I-Lin were found less effectively than 131I-Lin at killing cells in biofilm and biofilm eradication. Our results demonstrate that the 131I-Lin have potent anti-biofilm activity against S. aureus compare to Lin, 127I-Lin and media control. This is suggested that, 131I may have harmful effect on biofilm structure.

Keywords: iodine-131, linezolid, radiolabeling, slime layer, Staphylococcus

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Authors: Ahmet Taşan, Süha Tirkeş, Yavuz Öztürk, Zafer Bingül

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Friction stir welding (FSW) is a joining process in the solid state, which eliminates problems associated with the material melting and solidification, such as cracks, residual stresses and distortions generated during conventional welding. Among the most important advantages of FSW are; easy automation, less distortion, lower residual stress and good mechanical properties in the joining region. FSW is a recent approach to metal joining and although originally intended for aluminum alloys, it is investigated in a variety of metallic materials. The basic concept of FSW is a rotating tool, made of non-consumable material, specially designed with a geometry consisting of a pin and a recess (shoulder). This tool is inserted as spinning on its axis at the adjoining edges of two sheets or plates to be joined and then it travels along the joining path line. The tool rotation axis defines an angle of inclination with which the components to be welded. This angle is used for receiving the material to be processed at the tool base and to promote the gradual forge effect imposed by the shoulder during the passage of the tool. This prevents the material plastic flow at the tool lateral, ensuring weld closure on the back of the pin. In this study, two 4 mm Kevlar^® plates which were produced with the Kevlar^® fabrics, are analyzed with COMSOL Multiphysics in order to investigate the weldability via FSW. Thereafter, some experimental investigation is done with an appropriate workbench in order to compare them with the analysis results.

Keywords: analytical modeling, composite materials welding, friction stir welding, heat generation

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Authors: Ravi Vishwanath, Saumya Kumaar, S. N. Omkar

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Brain-computer interfacing (BCI) is a technology that is almost four decades old, and it was developed solely for the purpose of developing and enhancing the impact of neuroprosthetics. However, in the recent times, with the commercialization of non-invasive electroencephalogram (EEG) headsets, the technology has seen a wide variety of applications like home automation, wheelchair control, vehicle steering, etc. One of the latest developed applications is the mind-controlled quadrotor unmanned aerial vehicle. These applications, however, do not require a very high-speed response and give satisfactory results when standard classification methods like Support Vector Machine (SVM) and Multi-Layer Perceptron (MLPC). Issues are faced when there is a requirement for high-speed control in the case of fixed-wing unmanned aerial vehicles where such methods are rendered unreliable due to the low speed of classification. Such an application requires the system to classify data at high speeds in order to retain the controllability of the vehicle. This paper proposes a novel method of classification which uses a combination of Common Spatial Paradigm and Linear Discriminant Analysis that provides an improved classification accuracy in real time. A non-linear SVM based classification technique has also been discussed. Further, this paper discusses the implementation of the proposed method on a fixed-wing and VTOL unmanned aerial vehicles.

Keywords: brain-computer interface, classification, machine learning, unmanned aerial vehicles

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Authors: Mahawish Masud, Muhammad Iqbal, M. U. Khan, Farooque Azam

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Manual writing of test cases from functional requirements is a time-consuming task. Such test cases are not only difficult to write but are also challenging to maintain. Test cases can be drawn from the functional requirements that are expressed in natural language. However, manual test case generation is inefficient and subject to errors.  In this paper, we have presented a systematic procedure that could automatically derive test cases from user stories. The user stories are specified in a restricted natural language using a well-defined template.  We have also presented a detailed methodology for writing our test ready user stories. Our tool “Test-o-Matic” automatically generates the test cases by processing the restricted user stories. The generated test cases are executed by using open source Selenium IDE.  We evaluate our approach on a case study, which is an open source web based application. Effectiveness of our approach is evaluated by seeding faults in the open source case study using known mutation operators.  Results show that the test case generation from restricted user stories is a viable approach for automated testing of web applications.

Keywords: automated testing, natural language, restricted user story modeling, software engineering, software testing, test case specification, transformation and automation, user story, web application testing

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Authors: Sarmin, S., Ologuin, R.S.

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Background: Rapid population growth and land constraints in Singapore results in a possible situation in which we face a higher number of casualties and lack of operational space in healthcare facilities during disasters and HAZMAT events, collectively known as Civil Emergencies. This creates a need for available working space within hospital grounds to be amphibious or multi-functional, to ensure the institution’s capability to respond efficiently to Civil Emergencies. The Emergency Department (ED) mitigates this issue by converting the Ambulance Assembly Area used during peacetime into an Open Decontamination Facility (ODF) during Civil Emergency Response, for decontamination of casualties before they proceed to treatment areas into Ambulance Assembly Area used during peacetime. Aims: To effectively operationalize the Open Decontamination Facility (ODF) through the reduction of manual handling. Methods: From past experiences on Civil Emergency exercises, it was labor-intensive for staff to set up the Open Decontamination Facility (ODF). Manual handling to set up the Decontamination lanes by bringing down the curtains and supply of water was required to be turned on. Conclusion: The effectiveness of the design construction of an Open Decontamination Facility (ODF) is based on the use of automation of bringing down the curtains on the various lanes. The use of control panels for water supply to decontaminate patients. Safety within the ODF was considered with the installation of panic buttons, intercom for staff communication, and perimeter curtains were installed with stability arm to manage the condition with high wind velocity.

Keywords: civil emergencies, disaster, emergency department, Hazmat

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Authors: H. M. Côrtes, J. I. Da Silva Filho, M. F. Blos, B. S. Zanon

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In a modern society the factor corresponding to the increase in the level of quality in industrial production demand new techniques of control and machinery automation. In this context, this work presents the implementation of a Paraconsistent-Fuzzy Digital PID controller. The controller is based on the treatment of inconsistencies both in the Paraconsistent Logic and in the Fuzzy Logic. Paraconsistent analysis is performed on the signals applied to the system inputs using concepts from the Paraconsistent Annotated Logic with annotation of two values (PAL2v). The signals resulting from the paraconsistent analysis are two values defined as Dc - Degree of Certainty and Dct - Degree of Contradiction, which receive a treatment according to the Fuzzy Logic theory, and the resulting output of the logic actions is a single value called the crisp value, which is used to control dynamic system. Through an example, it was demonstrated the application of the proposed model. Initially, the Paraconsistent-Fuzzy Digital PID controller was built and tested in an isolated MATLAB environment and then compared to the equivalent Digital PID function of this software for standard step excitation. After this step, a level control plant was modeled to execute the controller function on a physical model, making the tests closer to the actual. For this, the control parameters (proportional, integral and derivative) were determined for the configuration of the conventional Digital PID controller and of the Paraconsistent-Fuzzy Digital PID, and the control meshes in MATLAB were assembled with the respective transfer function of the plant. Finally, the results of the comparison of the level control process between the Paraconsistent-Fuzzy Digital PID controller and the conventional Digital PID controller were presented.

Keywords: fuzzy logic, paraconsistent annotated logic, level control, digital PID

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Authors: Sezen Gurdag, Ayse Ebru Akin

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There is a growing concern all over the world on the health effect of the formaldehyde emission coming from the adhesive used in the MDF production. In this research, we investigated the effect of nanoparticle addition such as nanoclay and halloysite into urea-formadehyde resin on the total emitted formaldehyde from MDF plates produced using the resin modified as such. First, the curing behavior of the resin was studied by monitoring the pH, curing time, solid content, density and viscosity of the modified resin in comparison to the reference resin with no added nanoparticle. The dosing of the nanoparticle in the dry resin was kept at 1wt%, 3wt% or 5wt%. Consecutively, the resin was used in the production of 50X50 cm MDF samples using laboratory scale press line with full automation system. Modulus of elasticity, bending strength, internal bonding strength, water absorption were also measured in addition to the main interested parameter formaldehyde emission levels which is determined via spectrometric technique following an extraction procedure. Threshold values for nanoparticle dosing levels were determined to be 5wt% for both nanoparticles. However, the reinforcing behavior was observed to be occurring at different levels in comparison to the reference plates with each nanoparticle such that the level of reinforcement with nanoclay was shown to be more favorable than the addition of halloysite due to higher surface area available with the former. In relation, formaldehyde emission levels were observed to be following a similar trend where addition of 5wt% nanoclay into the urea-formaldehyde adhesive helped decrease the formaldehyde emission up to 40% whereas addition of halloysite at its threshold level demonstrated as the same level, i.e., 5wt%, produced an improvement of 18% only.

Keywords: halloysite, nanoclay, fiberboard, urea-formaldehyde adhesive

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Authors: J. C. Cabanillas-Noris, M. I. Martínez-Hernández, I. León-Monzón

Abstract:

The selection of diffractive events in the ALICE experiment during the first data taking period (RUN-I) of the Large Hadron Collider (LHC) was limited by the range over which rapidity gaps occur. It would be possible to achieve better measurements by expanding the range in which the production of particles can be detected. For this purpose, the ALICE Diffractive (AD0) detector has been installed and commissioned for the second phase (RUN-II). Any new detector should be able to take the data synchronously with all other detectors and be operated through the ALICE central systems. One of the key elements that must be developed for the AD0 detector is the Detector Control System (DCS). The DCS must be designed to operate safely and correctly this detector. Furthermore, the DCS must also provide optimum operating conditions for the acquisition and storage of physics data and ensure these are of the highest quality. The operation of AD0 implies the configuration of about 200 parameters, from electronics settings and power supply levels to the archiving of operating conditions data and the generation of safety alerts. It also includes the automation of procedures to get the AD0 detector ready for taking data in the appropriate conditions for the different run types in ALICE. The performance of AD0 detector depends on a certain number of parameters such as the nominal voltages for each photomultiplier tube (PMT), their threshold levels to accept or reject the incoming pulses, the definition of triggers, etc. All these parameters define the efficiency of AD0 and they have to be monitored and controlled through AD0 DCS. Finally, AD0 DCS provides the operator with multiple interfaces to execute these tasks. They are realized as operating panels and scripts running in the background. These features are implemented on a SCADA software platform as a distributed control system which integrates to the global control system of the ALICE experiment.

Keywords: AD0, ALICE, DCS, LHC

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Authors: Abu Zafor Sadek

Abstract:

Pharmaceuticals were one of the least impacted business sectors during the corona pandemic as they are the center point of Covid-19 fight. Emergency use authorization, unproven indication of some commonly used drugs, self-medication, research and production capacity of an individual country, capacity of producing vaccine by many countries, Active Pharmaceutical Ingredients (APIs) related uncertainty, information gap among manufacturer, practitioners and user, export restriction, duration of lock-down, lack of harmony in transportation, disruption in the regulatory approval process, sudden increased demand of hospital items and protective equipment, panic buying, difficulties in in-person product promotion, e-prescription, geo-politics and associated issues added a new dimension to this industry. Although the industry maintains a reasonable growth throughout Covid-19 days; however, it has been characterized by both long- and short-term effects. Short-term effects have already been visible to so many countries, especially those who are import-dependent and have limited research capacity. On the other hand, it will take a few more time to see the long-term effects. Nevertheless, supply chain disruption, changes in strategic planning, new communication model, squeezing of job opportunity, rapid digitalization are the major short-term effects, whereas long-term effects include a shift towards self-sufficiency, growth pattern changes of certain products, special attention towards clinical studies, automation in operations, the increased arena of ethical issues etc. Therefore, this qualitative and exploratory study identifies the post-covid-19 landscape of the global pharmaceutical industry.

Keywords: covid-19, pharmaceutical, businees, landscape

Procedia PDF Downloads 75

Authors: John Michael Russo

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Honeybees are important to our food system and continue to suffer from high rates of colony loss. Precision feed has brought many benefits to livestock cultivation and these should transfer to apiculture. However, apiculture has unique challenges. The objective of this research is to understand how principles of precision agriculture, applied to apiculture and feed specifically, might effectively improve state-of-the-art cultivation. The methodology surveys apicultural practice to build a model for assessment. First, a review of apicultural motivators is made. Feed method is then evaluated. Finally, precision feed methods are examined as accelerants with potential to advance the effectiveness of feed practice. Six important motivators emerge: colony loss, disease, climate change, site variance, operational costs, and competition. Feed practice itself is used to compensate for environmental variables. The research finds that the current state-of-the-art in apiculture feed focuses on critical challenges in the management of feed schedules which satisfy requirements of the bees, preserve potency, optimize environmental variables, and manage costs. Many of the challenges are most acute when feed is used to dispense medication. Technology such as RNA treatments have even more rigorous demands. Precision feed solutions focus on strategies which accommodate specific needs of individual livestock. A major component is data; they integrate precise data with methods that respond to individual needs. There is enormous opportunity for precision feed to improve apiculture through the integration of precision data with policies to translate data into optimized action in the apiary, particularly through automation.

Keywords: precision agriculture, precision feed, apiculture, honeybees

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Authors: Nadhifah A. Nugraha, Dida D. Damayanti, Widia Juliani

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Airplanes manufacturing is growing along with the increasing demand from consumers. The helicopter's tail called Tailboom is a product of the helicopter division at XYZ Corp, where the Tailboom assembly line is a pull system. Based on observations of existing conditions that occur at XYZ Corp, production is still unable to meet the demands of consumers; lead time occurs greater than the plan agreed upon by the consumers. In the assembly process, each work station experiences a lack of parts and components needed to assemble components. This happens because of the delay in getting the required part information, and there is no warning about the availability of parts needed, it makes some parts unavailable in assembly warehouse. The lack of parts and components from the previous work station causes the assembly process to stop, and the assembly line also stops at the next station. In its completion, the production time was late and not on the schedule. In resolving these problems, the controlling process is needed, which is controlling the assembly line to get all components and subassembly in the right amount and at the right time. This study applies one of Just In Time tools, namely Kanban and automation, should be added as efficiently and effectively communication line becomes electronic Kanban. The problem can be solved by reducing non-value added time, such as waiting time and idle time. The proposed results of controlling the assembly line of Tailboom result in a smooth assembly line without waiting, reduced lead time, and achieving production time according to the schedule agreed with the consumers.

Keywords: kanban, e-Kanban, lead time, pull system

Procedia PDF Downloads 85

Authors: Nathaniel Carpenter

Abstract:

Web-enabled systems for supporting youth mental health management in Australia are pioneering in their field; however, with their success, these systems are experiencing exponential growth in demand which is straining an already stretched service. Supporting youth mental is critical as the lack of support is associated with significant and lasting negative consequences. To meet this growing demand, and provide critical support, investigations are needed on evaluating and improving existing online support services. Improvements should focus on developing frameworks capable of augmenting and scaling service provisions. There are few investigations informing best-practice frameworks when implementing e-mental health support systems for youth mental health; there are fewer which implement machine learning or artificially intelligent systems to facilitate the delivering of services. This investigation will use a case study methodology to highlight the design features which are important for systems to enable young people to self-manage their mental health. The investigation will also highlight the current information system challenges, to include challenges associated with service quality, provisioning, and scaling. This work will propose methods of meeting these challenges through improved design, service augmentation and automation, service quality, and through artificially intelligent inspired solutions. The results of this study will inform a framework for supporting youth mental health with intelligent and scalable web-enabled technologies to support an ever-growing user base.

Keywords: artificial intelligence, information systems, machine learning, youth mental health

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Authors: Iqraq Kamal, Akmal Razif, Sivadas Chandra Sekaran, Ahmad Syazwan Hisaburi

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Painting plays a crucial role in the aerospace manufacturing industry, serving both protective and cosmetic purposes for components. However, the traditional manual painting method is time-consuming and labor-intensive, posing challenges for the sector in achieving higher efficiency. Additionally, the current automated robot path planning has been a bottleneck for spray painting processes, as typical manual teaching methods are time-consuming, error-prone, and skill-dependent. Therefore, it is essential to develop automated tool path planning methods to replace manual ones, reducing costs and improving product quality. Focusing on flat panel painting in aerospace manufacturing, this study aims to address issues related to unreliable part identification techniques caused by the high-mixture, low-volume nature of the industry. The proposed solution involves using a spray gun and a UR10 robotic arm with a vision system that utilizes one-shot object detection (OS2D) to identify parts accurately. Additionally, the research optimizes path planning by concentrating on the region of interest—specifically, the identified part, rather than uniformly covering the entire painting tray.

Keywords: aerospace manufacturing, one-shot object detection, automated spray painting, vision-based path optimization, deep learning, automation, robotic arm

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Authors: Sagar Jitendra Mahendrakar

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Exploratory testing is a dynamic and adaptable method of software quality assurance that is frequently praised for its ability to find hidden flaws and improve the overall quality of the product. Instead of using preset test cases, exploratory testing allows testers to explore the software application dynamically. This is in contrast to scripted testing methodologies, which primarily rely on tester intuition, creativity, and adaptability. There are several tools and techniques that can aid testers in the exploratory testing process which we will be discussing in this talk.Tests of this kind are able to find bugs of this kind that are harder to find during structured testing or that other testing methods may have overlooked.The purpose of this abstract is to examine the nature and importance of exploratory testing in modern software development methods. It explores the fundamental ideas of exploratory testing, highlighting the value of domain knowledge and tester experience in spotting possible problems that may escape the notice of traditional testing methodologies. Throughout the software development lifecycle, exploratory testing promotes quick feedback loops and continuous improvement by giving testers the ability to make decisions in real time based on their observations. This abstract also clarifies the unique features of exploratory testing, like its non-linearity and capacity to replicate user behavior in real-world settings. Testers can find intricate bugs, usability problems, and edge cases in software through impromptu exploration that might go undetected. Exploratory testing's flexible and iterative structure fits in well with agile and DevOps processes, allowing for a quicker time to market without sacrificing the quality of the final product.

Keywords: exploratory, testing, automation, quality

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Authors: Muhammad Umair, Yuri Nikolaev, Denis Artemov, Ighor Uzhinsky

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A flexible manufacturing system is an integral part of a smart factory of industry 4.0 in which every machine is interconnected and works autonomously. Robots are in the process of replacing humans in every industrial sector. As the cyber-physical-system (CPS) and artificial intelligence (AI) are advancing, the manufacturing industry is getting more dependent on computers than human brains. This modernization has boosted the production with high quality and accuracy and shifted from classic production to smart manufacturing systems. However, material handling for such automated productions is a challenge and needs to be addressed with the best possible solution. Conventional clamping systems are designed for manual work and not suitable for highly automated production systems. Researchers and engineers are trying to find the most economical solution for loading/unloading and transportation workpieces from a warehouse to a machine shop for machining operations and back to the warehouse without human involvement. This work aims to propose an advanced multi-shape tooling solution for highly automated manufacturing systems. The currently obtained result shows that it could function well with automated guided vehicles (AGVs) and modern conveyor belts. The proposed solution is following requirements to be automation-friendly, universal for different part geometry and production operations. We used a bottom-up approach in this work, starting with studying different case scenarios and their limitations and finishing with the general solution.

Keywords: artificial intelligence, cyber physics system, Industry 4.0, material handling, smart factory, flexible manufacturing system

Procedia PDF Downloads 112

Authors: Yan Zhang

Abstract:

Predictive maintenance is a technique to predict when an in-service machine will fail so that maintenance can be planned in advance. Analytics-driven predictive maintenance is gaining increasing attention in many industries such as manufacturing, utilities, aerospace, etc., along with the emerging demand of Internet of Things (IoT) applications and the maturity of technologies that support Big Data storage and processing. This study aims to build an end-to-end analytics solution that includes both real-time machine condition monitoring and machine learning based predictive analytics capabilities. The goal is to showcase a general predictive maintenance solution architecture, which suggests how the data generated from field machines can be collected, transmitted, stored, and analyzed. We use a publicly available aircraft engine run-to-failure dataset to illustrate the streaming analytics component and the batch failure prediction component. We outline the contributions of this study from four aspects. First, we compare the predictive maintenance problems from the view of the traditional reliability centered maintenance field, and from the view of the IoT applications. When evolving to the IoT era, predictive maintenance has shifted its focus from ensuring reliable machine operations to improve production/maintenance efficiency via any maintenance related tasks. It covers a variety of topics, including but not limited to: failure prediction, fault forecasting, failure detection and diagnosis, and recommendation of maintenance actions after failure. Second, we review the state-of-art technologies that enable a machine/device to transmit data all the way through the Cloud for storage and advanced analytics. These technologies vary drastically mainly based on the power source and functionality of the devices. For example, a consumer machine such as an elevator uses completely different data transmission protocols comparing to the sensor units in an environmental sensor network. The former may transfer data into the Cloud via WiFi directly. The latter usually uses radio communication inherent the network, and the data is stored in a staging data node before it can be transmitted into the Cloud when necessary. Third, we illustrate show to formulate a machine learning problem to predict machine fault/failures. By showing a step-by-step process of data labeling, feature engineering, model construction and evaluation, we share following experiences: (1) what are the specific data quality issues that have crucial impact on predictive maintenance use cases; (2) how to train and evaluate a model when training data contains inter-dependent records. Four, we review the tools available to build such a data pipeline that digests the data and produce insights. We show the tools we use including data injection, streaming data processing, machine learning model training, and the tool that coordinates/schedules different jobs. In addition, we show the visualization tool that creates rich data visualizations for both real-time insights and prediction results. To conclude, there are two key takeaways from this study. (1) It summarizes the landscape and challenges of predictive maintenance applications. (2) It takes an example in aerospace with publicly available data to illustrate each component in the proposed data pipeline and showcases how the solution can be deployed as a live demo.

Keywords: Internet of Things, machine learning, predictive maintenance, streaming data

Procedia PDF Downloads 361