Search results for: trained graphic designers

Authors: Belaynesh Chekol, Numan Çelebi

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The main purpose of this work is to recognize individual characters extracted from natural scene images using scale invariant feature transform (SIFT) features as an input to K-nearest neighbor (KNN); a classification learner algorithm. For this task, 1,068 and 78 images of English alphabet characters taken from Chars74k data set is used to train and test the classifier respectively. For each character image, We have generated describing features by using SIFT algorithm. This set of features is fed to the learner so that it can recognize and label new images of English characters. Two types of KNN (fine KNN and weighted KNN) were trained and the resulted classification accuracy is 56.9% and 56.5% respectively. The training time taken was the same for both fine and weighted KNN.

Keywords: character recognition, KNN, natural scene image, SIFT

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Authors: Malgorzata Schwab, Ashis Kumer Biswas

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In this concept paper, we explore the topic of Reversibility in Neural Networks leveraged for Network Integrity Verification and crafted the term ''Trusted Neural Network'' (TNN), paired with the API abstraction around it, to embrace the idea formally. This newly proposed high-level generalizable TNN model builds upon the Invertible Neural Network architecture, trained simultaneously in both forward and reverse directions. This allows for the original system inputs to be compared with the ones reconstructed from the outputs in the reversed flow to assess the integrity of the end-to-end inference flow. The outcome of that assessment is captured as an Integrity Score. Concrete implementation reflecting the needs of specific problem domains can be derived from this general approach and is demonstrated in the experiments. The model aspires to become a useful practice in drafting high-level systems architectures which incorporate AI capabilities.

Keywords: trusted, neural, invertible, API

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Authors: Prudvi Paresi, Fatemeh Javidan

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In recent years, greenhouse gas, or in particular, carbon emissions, have received special attention from environmentalists and designers due to the fact that they significantly contribute to the temperature rise. The building industry is one of the top seven major industries contributing to embodied carbon emission. Signage systems are an integral part of the building industry and bring completeness to the space-building by providing the required information and guidance. A significant amount of building materials, such as steel, aluminium, acrylic, LED, etc., are utilized in these systems, but very limited information is available on their sustainability and carbon footprint. Therefore, there is an urgent need to assess the emissions associated with the signage industry and for controlling these by adopting different mitigation techniques without sacrificing the efficiency of the project. The present paper investigates the embodied carbon of two case studies in the Australian signage industry within the cradle – gate (A1-A3) and gate–site (A4-A5) stages. A material source-based database is considered to achieve more accuracy. The study identified that aluminium is the major contributor to embodied carbon in the signage industry compared to other constituents. Finally, an attempt is made to suggest strategies for mitigating embodied carbon in this industry.

Keywords: carbon accounting, small-scale construction, signage industry, construction materials

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Authors: Lakshmidevi Sreeramareddy, Komalpreet Kaur, Nane Pothier

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Graphical-based passwords have existed for decades. Their major advantage is that they are easier to remember than an alphanumeric password. However, their disadvantage (especially recognition-based passwords) is the smaller password space, making them more vulnerable to brute force attacks. Graphical passwords are also highly susceptible to the shoulder-surfing effect. The gesture-based password method that we developed is a grid-free, template-free method. In this study, we evaluated the gesture-based passwords for usability and vulnerability. The results of the study are significant. We developed a gesture-based password application for data collection. Two modes of data collection were used: Creation mode and Replication mode. In creation mode (Session 1), users were asked to create six different passwords and reenter each password five times. In replication mode, users saw a password image created by some other user for a fixed duration of time. Three different duration timers, such as 5 seconds (Session 2), 10 seconds (Session 3), and 15 seconds (Session 4), were used to mimic the shoulder-surfing attack. After the timer expired, the password image was removed, and users were asked to replicate the password. There were 74, 57, 50, and 44 users participated in Session 1, Session 2, Session 3, and Session 4 respectfully. In this study, the machine learning algorithms have been applied to determine whether the person is a genuine user or an imposter based on the password entered. Five different machine learning algorithms were deployed to compare the performance in user authentication: namely, Decision Trees, Linear Discriminant Analysis, Naive Bayes Classifier, Support Vector Machines (SVMs) with Gaussian Radial Basis Kernel function, and K-Nearest Neighbor. Gesture-based password features vary from one entry to the next. It is difficult to distinguish between a creator and an intruder for authentication. For each password entered by the user, four features were extracted: password score, password length, password speed, and password size. All four features were normalized before being fed to a classifier. Three different classifiers were trained using data from all four sessions. Classifiers A, B, and C were trained and tested using data from the password creation session and the password replication with a timer of 5 seconds, 10 seconds, and 15 seconds, respectively. The classification accuracies for Classifier A using five ML algorithms are 72.5%, 71.3%, 71.9%, 74.4%, and 72.9%, respectively. The classification accuracies for Classifier B using five ML algorithms are 69.7%, 67.9%, 70.2%, 73.8%, and 71.2%, respectively. The classification accuracies for Classifier C using five ML algorithms are 68.1%, 64.9%, 68.4%, 71.5%, and 69.8%, respectively. SVMs with Gaussian Radial Basis Kernel outperform other ML algorithms for gesture-based password authentication. Results confirm that the shorter the duration of the shoulder-surfing attack, the higher the authentication accuracy. In conclusion, behavioral features extracted from the gesture-based passwords lead to less vulnerable user authentication.

Keywords: authentication, gesture-based passwords, machine learning algorithms, shoulder-surfing attacks, usability

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Authors: M. Caramihai, Irina Severin

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This paper deals with the design of an intelligent control structure for a bioprocess of Hansenula polymorpha yeast cultivation. The objective of the process control is to produce biomass in a desired physiological state. The work demonstrates that the designed Hybrid Control Techniques (HCT) are able to recognize specific evolution bioprocess trajectories using neural networks trained specifically for this purpose, in order to estimate the model parameters and to adjust the overall bioprocess evolution through an expert system and a fuzzy structure. The design of the control algorithm as well as its tuning through realistic simulations is presented. Taking into consideration the synergism of different paradigms like fuzzy logic, neural network, and symbolic artificial intelligence (AI), in this paper we present a real and fulfilled intelligent control architecture with application in bioprocess control.

Keywords: bioprocess, intelligent control, neural nets, fuzzy structure, hybrid techniques

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Authors: Judson P. LaGrone

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Simulation-based education in curricula was once a luxurious component of nursing programs but now serves as a vital element of an individual’s learning experience. A debriefing occurs after the simulation scenario or clinical experience is completed to allow the instructor(s) or trained professional(s) to act as a debriefer to guide a reflection with a purpose of acknowledging, assessing, and synthesizing the thought process, decision-making process, and actions/behaviors performed during the scenario or clinical experience. Debriefing is a vital component of the simulation process and educational experience to allow the learner(s) to progressively build upon past experiences and current scenarios within a safe and welcoming environment with a guided dialog to enhance future practice. The aim of this integrative review was to assess current practices of debriefing models in simulation-based education for health care professionals and students. The following databases were utilized for the search: CINAHL Plus, Cochrane Database of Systemic Reviews, EBSCO (ERIC), PsycINFO (Ovid), and Google Scholar. The advanced search option was useful to narrow down the search of articles (full text, Boolean operators, English language, peer-reviewed, published in the past five years). Key terms included debrief, debriefing, debriefing model, debriefing intervention, psychological debriefing, simulation, simulation-based education, simulation pedagogy, health care professional, nursing student, and learning process. Included studies focus on debriefing after clinical scenarios of nursing students, medical students, and interprofessional teams conducted between 2015 and 2020. Common themes were identified after the analysis of articles matching the search criteria. Several debriefing models are addressed in the literature with similarities of effectiveness for participants in clinical simulation-based pedagogy. Themes identified included (a) importance of debriefing in simulation-based pedagogy, (b) environment for which debriefing takes place is an important consideration, (c) individuals who should conduct the debrief, (d) length of debrief, and (e) methodology of the debrief. Debriefing models supported by theoretical frameworks and facilitated by trained staff are vital for a successful debriefing experience. Models differed from self-debriefing, facilitator-led debriefing, video-assisted debriefing, rapid cycle deliberate practice, and reflective debriefing. A reoccurring finding was centered around the emphasis of continued research for systematic tool development and analysis of the validity and effectiveness of current debriefing practices. There is a lack of consistency of debriefing models among nursing curriculum with an increasing rate of ill-prepared faculty to facilitate the debriefing phase of the simulation.

Keywords: debriefing model, debriefing intervention, health care professional, simulation-based education

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Authors: Alina Parincu, Giuseppe Empoli, Alexandru Capatina

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The purpose of this paper is to identify the most influential predictors of emotional intelligence skills, in the case of 20 business innovation coaches, on the co-creation of knowledge through coaching services delivered to innovative start-ups from Europe, funded through Horizon 2020 – SME Instrument. We considered the emotional intelligence skills (self-awareness, self-regulation, motivation, empathy and social skills) as antecedent conditions of the outcome: the quality of coaching services, perceived by the entrepreneurs who received funding within SME instrument, using fuzzy-sets qualitative comparative analysis (fsQCA) approach. The findings reveal that emotional intelligence skills, trained with neuro-management techniques, were associated with increased goal-focused business coaching skills.

Keywords: neuro-management, innovative start-ups, business coaching, fsQCA

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Authors: Hamid Falaki, Roya Kelkanlo, Mojtaba Tabatabaei

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The present study aimed at distinguishing the effects of work type on hand dimensions and investigating the relationship between anthropometric dimensions and occupation. The anthropometric data used in study were collected on 12 hand anthropometric dimensions. The participants included 91 males in two groups, namely manual labor job and office workers. All the data were analyzed using SPSS version 16. All measurements were significantly greater than those of office jobs except for the grip diameter obtained from the manual workers. The hand perimeter was the greatest value among the 12 measured dimensions, while the thickness of the side little finger was the smallest one. In four dimensions, namely width of four fingers together from the central hinge; diameter of thumb to face; diameter of index finger to face; hand thickness from index finger revealed the availability of a significant difference between manual labor jobs and office workers. Moreover, no significant relation was observed between weight and stature with hand dimension, which represents the correlation between occupation and the four dimensions. The results of this study showed that the difference between the two occupational groups was significant in terms of the four dimensions. Therefore, it is suggested that tool designers should consider this finding in their designing process.

Keywords: hand dimensions, occupation, tool design, anthropometry

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Authors: Seba Quqandi

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This study investigates the bond that people form using their assistive devices and the tactics applied during the product design process to help improve the user experience leading to a long-term product relationship. The aim is to develop a conceptual framework with which to describe and analyze the bond people form with their assistive devices and to integrate human emotions as a factor during the development of the product design process. The focus will be on the assistive technology market, namely, the Aid-For-Daily-Living market for situational impairments, to increase the quality of wellbeing. Findings will help us better understand the real issues of the product experience concerning people’s interaction throughout the product performance, establish awareness of the emotional effects in the daily interaction that fosters the product attachment, and help product developers and future designers create a connection between users and their assistive devices. The research concludes by discussing the implications of these findings for professionals and academics in the form of experiments in order to identify new areas that can stimulate new /or developed design directions.

Keywords: experience design, interaction design, emotion, design psychology, assistive tools, customization, userentred design

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Authors: Tharini N. de Silva, Xiao Zhibo, Zhao Rui, Mao Kezhi

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Causal relation identification is a crucial task in information extraction and knowledge discovery. In this work, we present two approaches to causal relation identification. The first is a classification model trained on a set of knowledge-based features. The second is a deep learning based approach training a model using convolutional neural networks to classify causal relations. We experiment with several different convolutional neural networks (CNN) models based on previous work on relation extraction as well as our own research. Our models are able to identify both explicit and implicit causal relations as well as the direction of the causal relation. The results of our experiments show a higher accuracy than previously achieved for causal relation identification tasks.

Keywords: causal realtion extraction, relation extracton, convolutional neural network, text representation

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Authors: Büşra Elibol, İsmail Sait Soyer, Hamid Farrokh Ghatte

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The continuity of the structural and non-structural elements within the envelope of the buildings is one of the fundamental factors in buildings during seismic actions. This investigation aims to make a comparison between Van and İzmir earthquakes in terms of damage assessment of the various facades. A strong earthquake (magnitude 7.2) struck the city of Van in the east of Turkey on 23 October 2011, and similarly, another strong earthquake struck the city of İzmir (magnitude 6.9) in Turkey on 30 October 2020. This paper presents the damage assessment of the current facade systems from multi-story buildings in Van and İzmir, Turkey. This investigation covers the buildings greater than three stories in height, excluding most unreinforced masonry facades. Regarding a building that can have more than one facade system, any of the facade systems are considered individually. Observation of different kinds of damages in the facade is discussed and represented in terms of its performance level throughout the seismic actions. Furthermore, presenting the standard design guidelines (i.e., Turkish seismic design code) is required not only for designers but also for installers of facade systems.

Keywords: damage, earthquake, facade, structural element, seismic action

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Authors: Cesar Ferreira Amorim, Tamotsu Hirata, Runer Augusto Marson

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The purpose of this study was to determine the effect of strength ramp isometric contraction on changes in surface electromyography (sEMG) signal characteristics of the hamstrings muscles. All measurements were obtained from 20 healthy well trained healthy adults (age 19.5 ± 0.8 yrs, body mass 63.4 ± 1.5 kg, height: 1.65 ± 0.05 m). Subjects had to perform isometric ramp contractions in knee flexion with the force gradually increasing from 0 to 40% of the maximal voluntary contraction (MVC) in a 20s period. The root mean square (RMS) amplitude of sEMG signals obtained from the biceps femoris (caput longum) were calculated at four different strength levels (10, 20, 30, and 40% MVC) from the ramp isometric contractions (5s during the 20s task %MVC). The main results were a more pronounced increase non-linear in sEMG-RMS amplitude for the muscles. The protocol described here may provide a useful index for measuring of strength neuromuscular fatigue.

Keywords: biosignal, surface electromyography, ramp contractions, strength

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Authors: Tajudeen Yusuf

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Arabic language and its instruction occupy a prominent status in the contemporary world, especially in academic and research institutions. Arabic, like other international languages, consolidates understanding among people of different nations and societies. It is a promising medium of sharing thoughts and feelings. As a means of communication and interaction, the language has gained its outstanding status since ancient times, especially because of the relationship it maintains with Islam and its heritage. Adding to its importance is the rapid growth and advancement of Science and Technology in the contemporary Era which has eventually made communication between human societies all over the world inevitable. Despite, the Arabic language still experiences many challenges especially in some area such as irrelevant textbooks and other teaching materials, old versions of teaching methods and inadequate teachers who professionally trained. Eventually, these have resulted in difficulties in the teaching and learning of the language. Therefore, urgent and necessary measures to enhance the teaching and learning of Arabic language within and outside Arab countries are therefore needed to be taken.

Keywords: Arabic, language, challenges, modern era

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Authors: Hanafizan Hussain

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This study shows that several projects will be conducted at the workshop in which using the conceptual model for knowledge sharing approach to create an idea for mobile application. The sharing idea has been done through the collaborative activity in which a group of different field sought to define the mobile application which will lead to new media approach of using social media platform. The collaborative activity will be provided and implemented in the form of one day workshop to determine the approach towards the theme given. The activity later will be continued for four weeks for the participant to prepare for the pitch day workshop. This paper shows the pitch of idea including the interface and prototype for the said products. The collaboration between the members with different field of study shows that social media influenced the knowledge sharing model and its creation or innovations. One of the projects supported a collaborative activity in which a group of young designers sought to define the knowledge sharing model of their ability in creating idea for mobile applications.

Keywords: mobile application, collaborative activity, conceptual knowledge sharing model, social media platform

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Authors: André Augusto Ceballos Melo

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Design to facilitate the recognition of congruent prosthetic movements, context-to-motion translations guided by image, verbal prompt, users nonverbal communication such as facial expressions, gestures, paralinguistics, scene context, and object recognition contributes to this process though it can also be applied to other tasks, such as walking, Prosthetic limbs as assistive technology through gestures, sound codes, signs, facial, body expressions, and scene context The context-to-motion model is a machine learning approach that is designed to improve the control and dexterity of prosthetic limbs. It works by using sensory input from the prosthetic limb to learn about the dynamics of the environment and then using this information to generate smooth, stable movements. This can help to improve the performance of the prosthetic limb and make it easier for the user to perform a wide range of tasks. There are several key benefits to using the context-to-motion model for prosthetic limb control. First, it can help to improve the naturalness and smoothness of prosthetic limb movements, which can make them more comfortable and easier to use for the user. Second, it can help to improve the accuracy and precision of prosthetic limb movements, which can be particularly useful for tasks that require fine motor control. Finally, the context-to-motion model can be trained using a variety of different sensory inputs, which makes it adaptable to a wide range of prosthetic limb designs and environments. Stable diffusion is a machine learning method that can be used to improve the control and stability of movements in robotic and prosthetic systems. It works by using sensory feedback to learn about the dynamics of the environment and then using this information to generate smooth, stable movements. One key aspect of stable diffusion is that it is designed to be robust to noise and uncertainty in the sensory feedback. This means that it can continue to produce stable, smooth movements even when the sensory data is noisy or unreliable. To implement stable diffusion in a robotic or prosthetic system, it is typically necessary to first collect a dataset of examples of the desired movements. This dataset can then be used to train a machine learning model to predict the appropriate control inputs for a given set of sensory observations. Once the model has been trained, it can be used to control the robotic or prosthetic system in real-time. The model receives sensory input from the system and uses it to generate control signals that drive the motors or actuators responsible for moving the system. Overall, the use of the context-to-motion model has the potential to significantly improve the dexterity and performance of prosthetic limbs, making them more useful and effective for a wide range of users Hand Gesture Body Language Influence Communication to social interaction, offering a possibility for users to maximize their quality of life, social interaction, and gesture communication.

Keywords: stable diffusion, neural interface, smart prosthetic, augmenting

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Authors: Mohamed Boubekri, Jaewook Lee

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The assessment of the daylighting performance of a design solution is a complex task due to the changing nature of daylight. A few quantitative metrics are available to designers to assess such a performance, among them are the mean hourly illuminance (MHI), the daylight factor (DF), the daylight autonomy (DA) and the useful daylight illuminance (UDI). Each of these metrics has criteria and limitations that affect the outcome of the evaluation. When to use one metric instead of another depends largely on the design goals to be achieved. Using Design Iterate Validate Adapt (DIVA) daylighting simulation program we set out to examine the performance behavior of these four metrics with the changing dimensions of three shading devices: a horizontal overhang, a horizontal louver system, and a vertical louver system, and compare their performance behavior as the orientation of the window changes. The context is a classroom of a prototypical elementary school in South Korea. Our results indicate that not all four metrics behave similarly as we vary the size of each shading device and as orientations changes. The UDI is the metric that leads to outcome most different than the other three metrics. Our conclusion is that not all daylighting metrics lead to the same conclusions and that it is important to use the metric that corresponds to the specific goals and objectives of the daylighting solution.

Keywords: daylight factor, hourly daylight illuminance, daylight autonomy, useful daylight illuminance

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Authors: Ying Yu, Wen-Chi Kuo, Tung-Jung Sung

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The Product-Service System (PSS) has received widespread attention due to the increasing global competition in manufacturing and service markets. Today’s smart products and services are driven by Internet of things (IoT) technologies which will promote the transformation from traditional PSS to smart PSS. Although the smart PSS has some of technological achievements in businesses, it often ignores the real demands of target users when using products and services. Therefore, designers should know and learn the User Experience (UX) of smart products, services and systems. However, both of academia and industry still lack relevant development experience of smart PSS since it is an emerging field. In doing so, this is a case study of Xiaomi’s Chunmi, the largest IoT platform in the world, and addresses the two major issues: (1) why Chunmi should develop smart PSS strategies with UX; and (2) how Chunmi could successfully implement the strategic objectives of smart PSS through the design. The case study results indicated that: (1) the smart PSS can distinguish competitors by their unique UX which is difficult to duplicate; (2) early user engagement is crucial for the success of smart PSS; and (3) interaction, expectation, and enjoyment can be treated as a three-dimensional evaluation of UX design for smart PSS innovation. In conclusion, the smart PSS can gain competitive advantages through good UX design in the market.

Keywords: design, smart PSS, user experience, user engagement

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Authors: Marilei Amadeu Sabino

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The aim of this study is to establish differences between the terms ‘false cognates’, ‘false friends’ and ‘deceptive cognates’, usually considered to be synonyms. It will be shown they are not synonyms, since they do not designate the same linguistic process or phenomenon. Despite their differences in meaning, many pairs of formally similar words in two (or more) different languages are true cognates, although they are usually known as ‘false’ cognates – such as, for instance, the English and Italian lexical items ‘assist x assistere’; ‘attend x attendere’; ‘argument x argomento’; ‘apology x apologia’; ‘camera x camera’; ‘cucumber x cocomero’; ‘fabric x fabbrica’; ‘factory x fattoria’; ‘firm x firma’; ‘journal x giornale’; ‘library x libreria’; ‘magazine x magazzino’; ‘parent x parente’; ‘preservative x preservativo’; ‘pretend x pretendere’; ‘vacancy x vacanza’, to name but a few examples. Thus, one of the theoretical objectives of this paper is firstly to elaborate definitions establishing a distinction between the words that are definitely ‘false cognates’ (derived from different etyma) and those that are just ‘deceptive cognates’ (derived from the same etymon). Secondly, based on Set Theory and on the concepts of equal sets, subsets, intersection of sets and disjoint sets, this study is intended to elaborate some theoretical and practical questions that will be useful in identifying more precisely similarities and differences between cognate words of different languages, and according to graphic interpretation of sets it will be possible to classify them and provide discernment about the processes of semantic changes. Therefore, these issues might be helpful not only to the Learning of Second and Foreign Languages, but they could also give insights into Foreign and Second Language Teaching Methodology. Acknowledgements: FAPESP – São Paulo State Research Support Foundation – the financial support offered (proc. n° 2017/02064-7).

Keywords: deceptive cognates, false cognates, foreign language learning, teaching methodology

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Authors: Pierre Kancir, Jean-Philippe Diguet, Marc Sevaux

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One of the main difficulties in developing multi-robot systems (MRS) is related to the simulation and testing tools available. Indeed, if the differences between simulations and real robots are too significant, the transition from the simulation to the robot won’t be possible without another long development phase and won’t permit to validate the simulation. Moreover, the testing of different algorithmic solutions or modifications of robots requires a strong knowledge of current tools and a significant development time. Therefore, the availability of tools for MRS, mainly with flying drones, is crucial to enable the industrial emergence of these systems. This research aims to present the most commonly used tools for MRS simulations and their main shortcomings and presents complementary tools to improve the productivity of designers in the development of multi-vehicle solutions focused on a fast learning curve and rapid transition from simulations to real usage. The proposed contributions are based on existing open source tools as Gazebo simulator combined with ROS (Robot Operating System) and the open-source multi-platform autopilot ArduPilot to bring them to a broad audience.

Keywords: ROS, ArduPilot, MRS, simulation, drones, Gazebo

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Authors: Magdy B. Eteiba, Wael Ismael Wahba, Shimaa Barakat

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This paper presents a fault identification, classification and fault location estimation method based on Discrete Wavelet Transform and Adaptive Network Fuzzy Inference System (ANFIS) for medium voltage cable in the distribution system. Different faults and locations are simulated by ATP/EMTP, and then certain selected features of the wavelet transformed signals are used as an input for a training process on the ANFIS. Then an accurate fault classifier and locator algorithm was designed, trained and tested using current samples only. The results obtained from ANFIS output were compared with the real output. From the results, it was found that the percentage error between ANFIS output and real output is less than three percent. Hence, it can be concluded that the proposed technique is able to offer high accuracy in both of the fault classification and fault location.

Keywords: ANFIS, fault location, underground cable, wavelet transform

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Authors: Ivan Struhár, Martin Sebera, Lenka Dovrtělová

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The aim of this study was to find out if the special type of exercise with elastic cord can improve the level of postural stability. The exercise programme was conducted twice a week for 3 months. The participants were randomly divided into an experimental group and a control group. The electronic balance board was used for testing of postural stability. All participants trained for 18 hours at the time of experiment without any special form of coordination programme. The experimental group performed 90 minutes plus of coordination exercise. The result showed that differences between pre-test and post-test occurred in the experimental group. It was used the nonparametric Wilcoxon t-test for paired samples (p=0.012; the significance level 95%). We calculated effect size by Cohen´s d. In the experimental group d is 1.96 which indicates a large effect. In the control group d is 0.04 which confirms no significant improvement.

Keywords: balance board, balance training, coordination, stability

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Authors: Francisco Borja de Lara, Ali Ravanbakhsh, Robert F. Wimmer-Schweingruber, Lars Seimetz, Fermín Navarro

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The intense structural loads during the launch of a spacecraft represent a challenge for the space structure designers because enough resistance has to be achieved while maintaining at the same time the mass and volume within the allowable margins of the mission requirements and inside the limits of the budget project. In this conference, we present the structural analysis of the Lunar Lander Neutron Dosimetry (LND) experiment on the Chang'E4 mission, the first probe to land on the moon’s far side included in the Chinese’ Moon Exploration Program by the Chinese National Space Administration. To this target, the software Nastran/Patran has been used: a structural model in Patran and a structural analysis through Nastran have been realized. Next, the results obtained are used both for the optimization process of the spacecraft structure, and as input parameters for the model structural test campaign. In this way, the feasibility of the lunar instrument structure is demonstrated in terms of the modal modes, stresses, and random vibration and a better understanding of the structural tests design is provided by our results.

Keywords: Chang’E4, Chinese national space administration, lunar lander neutron dosimetry, nastran-patran, structural analysis

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Authors: A. J. Shah, G. R. Vesmawala

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Connections perform a fundamental role in the steel structures as global behaviour. In order to evaluate the real influence of the physical and geometrical parameters that control their behaviour, many experimental tests and analysis have been developed but a definitive answer to the problem in question still stands. Here, various configurations of bolts were tried and the resulting moment rotation (M-θ) curves were plotted. The connection configuration is such that two bolts are located above each of the flanges and beside each of the webs. The model considers the combined effects of prying action, the formation of yield lines, and failures due to punching shear and beam section failure. For many types of connections, the stiffness at the service load level falls somewhere in between the fully restrained and simple limits and designers need to account for its behaviour. The (M-θ) curves are generally assumed to be the best characterization of connection behaviour. The moment rotation curves are generally derived from experiments on cantilever type specimens. The moments are calculated directly from the statics of the specimen, while the rotations are measured over a distance typically equal to the point of loading. Thus, this paper establishes the relationship between M-θ behaviour of different types of connections tested and presents the relative strength of various possible arrangements of bolts.

Keywords: bolt, moment, rotation, stiffness, connections

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Authors: Noga Bregman

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Accurate and efficient earthquake detection and phase picking are crucial for seismic hazard assessment and emergency response. This study introduces EQMamba, a deep-learning method that combines the strengths of the Earthquake Transformer and the Mamba model for simultaneous earthquake detection and phase picking. EQMamba leverages the computational efficiency of Mamba layers to process longer seismic sequences while maintaining a manageable model size. The proposed architecture integrates convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM) networks, and Mamba blocks. The model employs an encoder composed of convolutional layers and max pooling operations, followed by residual CNN blocks for feature extraction. Mamba blocks are applied to the outputs of BiLSTM blocks, efficiently capturing long-range dependencies in seismic data. Separate decoders are used for earthquake detection, P-wave picking, and S-wave picking. We trained and evaluated EQMamba using a subset of the STEAD dataset, a comprehensive collection of labeled seismic waveforms. The model was trained using a weighted combination of binary cross-entropy loss functions for each task, with the Adam optimizer and a scheduled learning rate. Data augmentation techniques were employed to enhance the model's robustness. Performance comparisons were conducted between EQMamba and the EQTransformer over 20 epochs on this modest-sized STEAD subset. Results demonstrate that EQMamba achieves superior performance, with higher F1 scores and faster convergence compared to EQTransformer. EQMamba reached F1 scores of 0.8 by epoch 5 and maintained higher scores throughout training. The model also exhibited more stable validation performance, indicating good generalization capabilities. While both models showed lower accuracy in phase-picking tasks compared to detection, EQMamba's overall performance suggests significant potential for improving seismic data analysis. The rapid convergence and superior F1 scores of EQMamba, even on a modest-sized dataset, indicate promising scalability for larger datasets. This study contributes to the field of earthquake engineering by presenting a computationally efficient and accurate method for simultaneous earthquake detection and phase picking. Future work will focus on incorporating Mamba layers into the P and S pickers and further optimizing the architecture for seismic data specifics. The EQMamba method holds the potential for enhancing real-time earthquake monitoring systems and improving our understanding of seismic events.

Keywords: earthquake, detection, phase picking, s waves, p waves, transformer, deep learning, seismic waves

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Authors: Tonga Agadi Danladi, Ajao Wasiu Bamidele, Terdue Dyeko

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Recent advancement in microwave communications technologies especially in telecommunications and broadcasting have resulted in congestion on the frequencies below 10GHz. This has forced microwave designers to look for high frequencies. Unfortunately for frequencies greater than 10GHz rain becomes one of the main factors of attenuation in signal strength. At frequencies from 10GHz upwards, rain drop sizes leads to outages that compromises the availability and quality of service this making it a critical factor in satellite link budget design. Rain rate and rain attenuation predictions are vital steps to be considered when designing microwave satellite communication link operating at Ku-band frequencies (112-18GHz). Unreliable rain rates data in the tropical regions of the world like Nigeria from radio communication group of the international Telecommunication Union (ITU-R) makes it difficult for microwave engineers to determine a realistic rain margin that needs to be accommodated in satellite link budget design in such region. This work presents an empirical tool for predicting the amount of signal due to rain on DBS signal operating at the Ku-band.

Keywords: attenuation, Ku-Band, microwave communication, rain rates

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Authors: T. Damak, O. Kriaa, A. Baccar, M. A. Ben Ayed, N. Masmoudi

Abstract:

In the last few years, Automatic Number Plate Recognition (ANPR) systems have become widely used in the safety, the security, and the commercial aspects. Forethought, several methods and techniques are computing to achieve the better levels in terms of accuracy and real time execution. This paper proposed a computer vision algorithm of Number Plate Localization (NPL) and Characters Segmentation (CS). In addition, it proposed an improved method in Optical Character Recognition (OCR) based on Deep Learning (DL) techniques. In order to identify the number of detected plate after NPL and CS steps, the Convolutional Neural Network (CNN) algorithm is proposed. A DL model is developed using four convolution layers, two layers of Maxpooling, and six layers of fully connected. The model was trained by number image database on the Jetson TX2 NVIDIA target. The accuracy result has achieved 95.84%.

Keywords: ANPR, CS, CNN, deep learning, NPL

Procedia PDF Downloads 301

Authors: Z. Zerdoumi, D. Benatia, , D. Chicouche

Abstract:

This paper investigates the application of artificial neural network to the problem of nonlinear channel equalization. The difficulties caused by channel distortions such as inter symbol interference (ISI) and nonlinearity can overcome by nonlinear equalizers employing neural networks. It has been shown that multilayer perceptron based equalizer outperform significantly linear equalizers. We present a multilayer perceptron based equalizer with decision feedback (MLP-DFE) trained with the back propagation algorithm. The capacity of the MLP-DFE to deal with nonlinear channels is evaluated. From simulation results it can be noted that the MLP based DFE improves significantly the restored signal quality, the steady state mean square error (MSE), and minimum Bit Error Rate (BER), when comparing with its conventional counterpart.

Keywords: Artificial Neural Network, signal restoration, Nonlinear Channel equalization, equalization

Procedia PDF Downloads 492

Authors: Olivia A. Kjorlien, Maryam Asghari, Farshid Alizadeh-Shabdiz

Abstract:

The goal of this paper is to investigate the impact of mosaic augmentation on the performance of object detection solutions. To carry out the study, YOLOv4 and YOLOv4-Tiny models have been selected, which are popular, advanced object detection models. These models are also representatives of two classes of complex and simple models. The study also has been carried out on two categories of objects, simple and complex. For this study, YOLOv4 and YOLOv4 Tiny are trained with and without mosaic augmentation for two sets of objects. While mosaic augmentation improves the performance of simple object detection, it deteriorates the performance of complex object detection, specifically having the largest negative impact on the false positive rate in a complex object detection case.

Keywords: accuracy, false positives, mosaic augmentation, object detection, YOLOV4, YOLOV4-Tiny

Procedia PDF Downloads 121

Authors: Tomasz Smolinski, Irena Herdzik-Koniecko, Marta Pyszynska, M. Rogowski

Abstract:

Old electronic components can be easily found nowadays. Significant quantities of valuable metals such as gold, silver or copper are used for the production of advanced electronic devices. Old useless electronic device slowly became a new source of precious metals, very often more efficient than natural. For example, it is possible to recover more gold from 1-ton personal computers than seventeen tons of gold ore. It makes urban mining industry very profitable and necessary for sustainable development. For the recovery of metals from waste of electronic equipment, various treatment options based on conventional physical, hydrometallurgical and pyrometallurgical processes are available. In this group hydrometallurgy processes with their relatively low capital cost, low environmental impact, potential for high metal recoveries and suitability for small scale applications, are very promising options. Institute of Nuclear Chemistry and Technology has great experience in hydrometallurgy processes especially focused on recovery metals from industrial and agricultural wastes. At the moment, urban mining project is carried out. The method of effective recovery of valuable metals from central processing units (CPU) components has been developed. The principal processes such as acidic leaching and solvent extraction were used for precious metals recovery from old processors and graphic cards. Electronic components were treated by acidic solution at various conditions. Optimal acid concentration, time of the process and temperature were selected. Precious metals have been extracted to the aqueous phase. At the next step, metals were selectively extracted by organic solvents such as oximes or tributyl phosphate (TBP) etc. Multistage mixer-settler equipment was used. The process was optimized.

Keywords: electronic waste, leaching, hydrometallurgy, metal recovery, solvent extraction

Procedia PDF Downloads 132

Authors: Ijaz Ahmed, Anwar Zada, Muhammad Waqas, M. U. Ashraf

Abstract:

We demonstrate the performance of a very efficient tagger applies on hadronically decaying top quark pairs as signal based on deep neural network algorithms and compares with the QCD multi-jet background events. A significant enhancement of performance in boosted top quark events is observed with our limited computing resources. We also compare modern machine learning approaches and perform a multivariate analysis of boosted top-pair as well as single top quark production through weak interaction at √s = 14 TeV proton-proton Collider. The most relevant known background processes are incorporated. Through the techniques of Boosted Decision Tree (BDT), likelihood and Multlayer Perceptron (MLP) the analysis is trained to observe the performance in comparison with the conventional cut based and count approach

Keywords: top tagger, multivariate, deep learning, LHC, single top

Procedia PDF Downloads 104