Search results for: motion data acquisition

Authors: Rakesh Kumar, A. K. Ghosh

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The paper presents the modeling of linear and nonlinear longitudinal aerodynamics using real flight data of Hansa-3 aircraft gathered at low and high angles of attack. The Neural-Gauss-Newton (NGN) method has been applied to model the linear and nonlinear longitudinal dynamics and estimate parameters from flight data. Unsteady aerodynamics due to flow separation at high angles of attack near stall has been included in the aerodynamic model using Kirchhoff’s quasi-steady stall model. NGN method is an algorithm that utilizes Feed Forward Neural Network (FFNN) and Gauss-Newton optimization to estimate the parameters and it does not require any a priori postulation of mathematical model or solving of equations of motion. NGN method was validated on real flight data generated at moderate angles of attack before application to the data at high angles of attack. The estimates obtained from compatible flight data using NGN method were validated by comparing with wind tunnel values and the maximum likelihood estimates. Validation was also carried out by comparing the response of measured motion variables with the response generated by using estimates a different control input. Next, NGN method was applied to real flight data generated by executing a well-designed quasi-steady stall maneuver. The results obtained in terms of stall characteristics and aerodynamic parameters were encouraging and reasonably accurate to establish NGN as a method for modeling nonlinear aerodynamics from real flight data at high angles of attack.

Keywords: parameter estimation, NGN method, linear and nonlinear, aerodynamic modeling

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Authors: M. Vassou, A. Karasimos

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A primary concern in the field of Second Language Acquisition (SLA) research is to determine the innate mechanisms of second language learning and acquisition through the systematic study of a learner's interlanguage. Errors emerge while a learner attempts to communicate using the target-language and can be seen either as the observable linguistic product of the latent cognitive and language process of mental representations or as an indispensable learning mechanism. Therefore, the study of the learner’s erroneous forms may depict the various strategies and mechanisms that take place during the language acquisition process resulting in deviations from the target-language norms and difficulties in communication. Mapping the erroneous utterances of a late adult learner in the process of acquiring Greek as a second language constitutes one of the main aims of this study. For our research purposes, we created an error-tagged learner corpus composed of the participant’s written texts produced throughout a period of a 4- year instructed language acquisition. Error analysis and interlanguage theory constitute the methodological and theoretical framework, respectively. The research questions pertain to the learner's most frequent errors per linguistic category and per year as well as his choices concerning the Greek Article System. According to the quantitative analysis of the data, the most frequent errors are observed in the categories of the stress system and syntax, whereas a significant fluctuation and/or gradual reduction throughout the 4 years of instructed acquisition indicate the emergence of developmental stages. The findings with regard to the article usage bespeak fossilization of erroneous structures in certain contexts. In general, our results point towards the existence and further development of an established learner’s (inter-) language system governed not only by mother- tongue and target-language influences but also by the learner’s assumptions and set of rules as the result of a complex cognitive process. It is expected that this study will contribute not only to the knowledge in the field of Greek as a second language and SLA generally, but it will also provide an insight into the cognitive mechanisms and strategies developed by multilingual learners of late adulthood.

Keywords: Greek as a second language, error analysis, interlanguage, late adult learner

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Authors: Atoum Abdullah

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The purpose of this research is to determine the extent to which computer animation and narration affect students’ understanding of complex scientific concepts and improve their exam performance, this is compared to traditional lectures that include PowerPoints with texts and static images. A mixed-method design in data collection was used, including quantitative and qualitative data. Quantitative data was collected using a pre and post-test method and a close-ended questionnaire. Qualitative data was collected through an open-ended questionnaire. A pre and posttest strategy was used to measure the level of students’ understanding with and without the use of animation. The test included multiple-choice questions to test factual knowledge, open-ended questions to test conceptual knowledge, and to label the diagram questions to test application knowledge. The results showed that students on average, performed significantly higher on the posttest as compared to the pretest on all areas of acquired knowledge. However, the increase in the posttest score with respect to the acquisition of conceptual and application knowledge was higher compared to the increase in the posttest score with respect to the acquisition of factual knowledge. This result demonstrates that animation is more beneficial when acquiring deeper, conceptual, and cognitive knowledge than when only factual knowledge is acquired.

Keywords: animation, narration, science, teaching

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Authors: SangWon Han, MuWook Pyeon, Sujung Moon, DaeKyo Seo

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Recently, technologies based on three-dimensional (3D) space information are being developed and quality of life is improving as a result. Research on real-time digital map (RDM) is being conducted now to provide 3D space information. RDM is a service that creates and supplies 3D space information in real time based on location/shape detection. Research subjects on RDM include the construction of 3D space information with matching image data, complementing the weaknesses of image acquisition using multi-source data, and data collection methods using big data. Using RDM will be effective for space analysis using 3D space information in a U-City and for other space information utilization technologies.

Keywords: RDM, multi-source data, big data, U-City

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Authors: Yildiz Stella Dak, Jale Tezcan

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Ground motion models that relate a strong motion parameter of interest to a set of predictive seismological variables describing the earthquake source, the propagation path of the seismic wave, and the local site conditions constitute a critical component of seismic hazard analyses. When a sufficient number of strong motion records are available, ground motion relations are developed using statistical analysis of the recorded ground motion data. In regions lacking a sufficient number of recordings, a synthetic database is developed using stochastic, theoretical or hybrid approaches. Regardless of the manner the database was developed, ground motion relations are developed using regression analysis. Development of a ground motion relation is a challenging process which inevitably requires the modeler to make subjective decisions regarding the inclusion criteria of the recordings, the functional form of the model and the set of seismological variables to be included in the model. Because these decisions are critically important to the validity and the applicability of the model, there is a continuous interest on procedures that will facilitate the development of ground motion models. This paper proposes the use of the Least Absolute Shrinkage and Selection Operator (LASSO) in selecting the set predictive seismological variables to be used in developing a ground motion relation. The LASSO can be described as a penalized regression technique with a built-in capability of variable selection. Similar to the ridge regression, the LASSO is based on the idea of shrinking the regression coefficients to reduce the variance of the model. Unlike ridge regression, where the coefficients are shrunk but never set equal to zero, the LASSO sets some of the coefficients exactly to zero, effectively performing variable selection. Given a set of candidate input variables and the output variable of interest, LASSO allows ranking the input variables in terms of their relative importance, thereby facilitating the selection of the set of variables to be included in the model. Because the risk of overfitting increases as the ratio of the number of predictors to the number of recordings increases, selection of a compact set of variables is important in cases where a small number of recordings are available. In addition, identification of a small set of variables can improve the interpretability of the resulting model, especially when there is a large number of candidate predictors. A practical application of the proposed approach is presented, using more than 600 recordings from the National Geospatial-Intelligence Agency (NGA) database, where the effect of a set of seismological predictors on the 5% damped maximum direction spectral acceleration is investigated. The set of candidate predictors considered are Magnitude, Rrup, Vs30. Using LASSO, the relative importance of the candidate predictors has been ranked. Regression models with increasing levels of complexity were constructed using one, two, three, and four best predictors, and the models’ ability to explain the observed variance in the target variable have been compared. The bias-variance trade-off in the context of model selection is discussed.

Keywords: ground motion modeling, least absolute shrinkage and selection operator, penalized regression, variable selection

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Authors: M. Mueller, M. Kuehn, M. Voelker

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In small and medium-sized enterprises (SMEs), the challenge is to create a well-grounded and reliable basis for process analysis, optimization and planning due to a lack of data. SMEs have limited access to methods with which they can effectively and efficiently analyse processes and identify cause-and-effect relationships in order to generate the necessary database and derive optimization potential from it. The implementation of digitalization within the framework of Industry 4.0 thus becomes a particular necessity for SMEs. For these reasons, the abstract presents an analysis methodology that is subject to the objective of developing an SME-appropriate methodology for efficient, temporarily feasible data collection and evaluation in flexible production and logistics systems as a basis for process analysis and optimization. The overall methodology focuses on retrospective, event-based tracing and analysis of material flow objects. The technological basis consists of Bluetooth low energy (BLE)-based transmitters, so-called beacons, and smart mobile devices (SMD), e.g. smartphones as receivers, between which distance data can be measured and derived motion profiles. The distance is determined using the Received Signal Strength Indicator (RSSI), which is a measure of signal field strength between transmitter and receiver. The focus is the development of a software-based methodology for interpretation of relative movements of transmitters and receivers based on distance data. The main research is on selection and implementation of pattern recognition methods for automatic process recognition as well as methods for the visualization of relative distance data. Due to an existing categorization of the database regarding process types, classification methods (e.g. Support Vector Machine) from the field of supervised learning are used. The necessary data quality requires selection of suitable methods as well as filters for smoothing occurring signal variations of the RSSI, the integration of methods for determination of correction factors depending on possible signal interference sources (columns, pallets) as well as the configuration of the used technology. The parameter settings on which respective algorithms are based have a further significant influence on result quality of the classification methods, correction models and methods for visualizing the position profiles used. The accuracy of classification algorithms can be improved up to 30% by selected parameter variation; this has already been proven in studies. Similar potentials can be observed with parameter variation of methods and filters for signal smoothing. Thus, there is increased interest in obtaining detailed results on the influence of parameter and factor combinations on data quality in this area. The overall methodology is realized with a modular software architecture consisting of independently modules for data acquisition, data preparation and data storage. The demonstrator for initialization and data acquisition is available as mobile Java-based application. The data preparation, including methods for signal smoothing, are Python-based with the possibility to vary parameter settings and to store them in the database (SQLite). The evaluation is divided into two separate software modules with database connection: the achievement of an automated assignment of defined process classes to distance data using selected classification algorithms and the visualization as well as reporting in terms of a graphical user interface (GUI).

Keywords: event-based tracing, machine learning, process classification, parameter settings, RSSI, signal smoothing

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Authors: Y. S. Oh, P. Abhishesh, B. S. Ryuh

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This paper presents the method of trajectory planning by the robot end-effector which accounts for more accurate and smooth differential geometry of the ruled surface generated by tool line fixed with end-effector based on the methods of curvature theory of ruled surface and the dual curvature theory, and focuses on the underlying relation to unite them for enhancing the efficiency for trajectory planning. Robot motion can be represented as motion properties of the ruled surface generated by trajectory of the Tool Center Point (TCP). The linear and angular properties of the six degree-of-freedom motion of end-effector are computed using the explicit formulas and functions from curvature theory and dual curvature theory. This paper explains the complete dualization of ruled surface and shows that the linear and angular motion applied using the method of dual curvature theory is more accurate and less complex.

Keywords: dual curvature theory, robot end effector, ruled surface, TCP (Tool Center Point)

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Authors: İmren Öztürk Yılmaz, Abdullah Yasin Bilici, Yasin Atalay Candemir

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The system consists of 4 main elements: motion system, image acquisition system, image processing software, and control interface. The parts coming out of the production line to enter the image processing system with the conveyor belt at the end of the line. The 3D scanning of the produced part is performed with the laser scanning system integrated into the system entry side. With the 3D scanning method, it is determined at what position and angle the parts enter the system, and according to the data obtained, parameters such as part origin and conveyor speed are calculated with the designed software, and the robot is informed about the position where it will take part. The robot, which receives the information, takes the produced part on the belt conveyor and shows it to high-resolution cameras for quality control. Measurement processes are carried out with a maximum error of 20 microns determined by the experiments.

Keywords: quality control, industry 4.0, image processing, automated fault detection, digital visual inspection

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Authors: Narupon Kunbootsri, P. Sirasaporn

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Frozen shoulder is a common problem present by pain and limit range of motion. The prevalence of frozen shoulder showed 18-31% of population. The effect of frozen shoulder lead to limit activities daily living life, high medical care cost and so on. Physiotherapy is one of the treatments for frozen shoulder but there was no data about the treatment of physiotherapy. Moreover, it is question about onset of symptom relate to physiotherapy program and obtaining physical modalities and delayed start physiotherapy program lead to delayed improvement. Thus the aim of this study was to investigate physiotherapy program for frozen shoulder relate to onset of symptom, range of motion and obtaining physical modalities. A retrospective study design was conducted. 182 medical records of patients with frozen shoulder were reviewed. These frozen shoulders were treated at physiotherapy unit, department of Rehabilitation last 3 years (January, 2014- December, 2016). The data consist of onset of symptom, range of motion and obtaining physical modalities were recorded. There was a statistically significant increase in shoulder flexion [mean difference 38.88 with 95%CI were [16.00-61.77], shoulder abduction [mean difference 48.47 with 95%CI were 16.07-90.59], shoulder internal rotation [mean difference 22.36 with 95%CI were 2.81-37.18] and shoulder external rotation [mean difference 32.12 with 95%CI were [(-2.47)-(46.91)]. In addition, the onset of symptom was 76.42±46.90 days. And the physical modalities used frequently were hot pack 14.8% and ultrasound diathermy 13.7%. In conclusion, the physiotherapy program including, hot pack and ultrasound diathermy seem to be useful for frozen shoulder. But onset of symptom is too long to start physiotherapy programs.

Keywords: frozen shoulder, range of motions, onset of symptom, physiotherapy, physical modality

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Authors: Yuan-Yu Chiang, Yu-Han Tseng, Chin-Chen Wen

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Many southeast-Asia women migrate to Taiwan by marriage, and it usually takes 4 to 8 years for them to acquire Taiwanese citizenship. This study investigates the relationships between their citizenship acquisition and whether they develop Taiwanese identities. One hundred and ninety-two immigrant women were measured using Multigroup Ethnic Identity Measure-Revised and a global 10-point ethnic identity question. Correlation tests and hierarchical regression were performed to explore whether acquiring citizenship would help immigrant women to develop Taiwanese identities. The results revealed that citizenship acquisition does help immigrant women to identify with Taiwanese society symbolically. However, the results also indicated that acquiring citizenship would not help these immigrant women become involved in deeper cultural exploration of Taiwan nor would it encourage them to make commitments to the host society.

Keywords: immigrants, international marriage, ethnic identity, Taiwan

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Authors: Ailing Yang, Penghan Song, Aihua Cai

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The nonlinear maneuvering target tracking problem is mainly a state estimation problem when the target motion model is uncertain. Traditional solutions include Kalman filtering based on Bayesian filtering framework and extended Kalman filtering. However, these methods need prior knowledge such as kinematics model and state system distribution, and their performance is poor in state estimation of nonprior complex dynamic systems. Therefore, in view of the problems existing in traditional algorithms, a convolution LSTM target state estimation (SAConvLSTM-SE) algorithm based on Self-Attention memory (SAM) is proposed to learn the historical motion state of the target and the error distribution information measured at the current time. The measured track point data of airborne radar are processed into data sets. After supervised training, the data-driven deep neural network based on SAConvLSTM can directly obtain the target state at the next moment. Through experiments on two different maneuvering targets, we find that the network has stronger robustness and better tracking accuracy than the existing tracking methods.

Keywords: maneuvering target, state estimation, Kalman filter, LSTM, self-attention

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Authors: Marcio Leal, Marta Villamil

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Computacional recognition of sign languages aims to allow a greater social and digital inclusion of deaf people through interpretation of their language by computer. This article presents a model of recognition of two of global parameters from sign languages; hand configurations and hand movements. Hand motion is captured through an infrared technology and its joints are built into a virtual three-dimensional space. A Multilayer Perceptron Neural Network (MLP) was used to classify hand configurations and Dynamic Time Warping (DWT) recognizes hand motion. Beyond of the method of sign recognition, we provide a dataset of hand configurations and motion capture built with help of fluent professionals in sign languages. Despite this technology can be used to translate any sign from any signs dictionary, Brazilian Sign Language (Libras) was used as case study. Finally, the model presented in this paper achieved a recognition rate of 80.4%.

Keywords: artificial neural network, computer vision, dynamic time warping, infrared, sign language recognition

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Authors: Chih-Jer Lin, Jian-Hong Hou

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Industry 4.0 is a vision of integrated industry implemented by artificial intelligent computing, software, and Internet technologies. The main goal of industry 4.0 is to deal with the difficulty owing to competitive pressures in the marketplace. For today’s manufacturing factories, the type of production is changed from mass production (high quantity production with low product variety) to medium quantity-high variety production. To offer flexibility, better quality control, and improved productivity, robot manipulators are used to combine material processing, material handling, and part positioning systems into an integrated manufacturing system. To implement the automated system for sheet metal bending operations, motion planning of a 7-degrees of freedom (DOF) robot is studied in this paper. A virtual reality (VR) environment of a bending cell, which consists of the robot and a bending machine, is established using the virtual robot experimentation platform (V-REP) simulator. For sheet metal bending operations, the robot only needs six DOFs for the pick-and-place or tracking tasks. Therefore, this 7 DOF robot has more DOFs than the required to execute a specified task; it can be called a redundant robot. Therefore, this robot has kinematic redundancies to deal with the task-priority problems. For redundant robots, Pseudo-inverse of the Jacobian is the most popular motion planning method, but the pseudo-inverse methods usually lead to a kind of chaotic motion with unpredictable arm configurations as the Jacobian matrix lose ranks. To overcome the above problem, we proposed a method to formulate the motion planning problems as optimization problem. Moreover, a genetic algorithm (GA) based method is proposed to deal with motion planning of the redundant robot. Simulation results validate the proposed method feasible for motion planning of the redundant robot in an automated sheet-metal bending operations.

Keywords: redundant robot, motion planning, genetic algorithm, obstacle avoidance

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Authors: Jacqueline Żammit

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Recent research indicates that the utilization of right-brain strategies holds significant implications for the acquisition of language skills. Nevertheless, the utilization of visual culture as a means to stimulate these strategies and amplify language retention among adults engaging in second language (L2) learning remains a relatively unexplored area. This investigation delves into the impact of visual culture on activating right-brain processes during the initial stages of language acquisition, particularly in the context of teaching Maltese as a second language (ML2) to adult learners. By employing a qualitative research approach, this study convenes a focus group comprising twenty-seven educators to delve into a range of visual culture techniques integrated within language instruction. The collected data is subjected to thematic analysis using NVivo software. The findings underscore a variety of impactful visual culture techniques, encompassing activities such as drawing, sketching, interactive matching games, orthographic mapping, memory palace strategies, wordless picture books, picture-centered learning methodologies, infographics, Face Memory Game, Spot the Difference, Word Search Puzzles, the Hidden Object Game, educational videos, the Shadow Matching technique, Find the Differences exercises, and color-coded methodologies. These identified techniques hold potential for application within ML2 classes for adult learners. Consequently, this study not only provides insights into optimizing language learning through specific visual culture strategies but also furnishes practical recommendations for enhancing language competencies and skills.

Keywords: visual culture, right-brain strategies, second language acquisition, maltese as a second language, visual aids, language-based activities

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Authors: Dilson Subedi

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Current transformers are an integral part of power system because it provides a proportional safe amount of current for protection and measurement applications. However, due to upgradation of electromechanical relays to numerical relays and electromechanical energy meters to digital meters, the connected burden, which defines some of the CT characteristics, has drastically reduced. This has led to the system experiencing high currents damaging the connected relays and meters. Since the protection and metering equipment's are designed to withstand only certain amount of current with respect to time, these high currents pose a risk to man and equipment. Therefore, during such instances, the CT saturation characteristics have a huge influence on the safety of both man and equipment and on the reliability of the protection and metering system. This paper shows the effectiveness of a bank of parallel connected resistors, as a burden compensation technique, in compensating the burden of under-burdened CT’s. The response of the CT in the case of failure of one or more resistors at different levels of overcurrent will be captured using the LabVIEWTM data acquisition hardware (DAQ). The analysis is done on the real-time data gathered using LabVIEWTM. Variation of current transformer saturation characteristics with changes in burden will be discussed.

Keywords: accuracy limiting factor, burden, burden compensation, current transformer

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Authors: Muhammet Dursun Kaya, Hasan Asil

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One of the most important factors of research and development is the deep insight into the evolutions of scientific development. The state-of-the-art tools and instruments can considerably assist the researchers, and many of the world organizations have become aware of the advantages of data mining for the acquisition of the knowledge required for the unstructured data. This paper was an attempt to review the articles on the information technology published in the past five years with the aid of data mining. A clustering approach was used to study these articles, and the research results revealed that three topics, namely health, innovation, and information systems, have captured the special attention of the researchers.

Keywords: information technology, data mining, scientific development, clustering

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Authors: Mary Joy C. Montenegro, Voltaire M. Mistades

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This action research was implemented to enhance the teaching of linear motion and to improve students' conceptual understanding and engagement using a developed 7R-based module called 'module on vectors and one-dimensional kinematics' (MVOK). MVOK was validated in terms of objectives, contents, format, and language used, presentation, usefulness, and overall presentation. The validation process revealed a value of 4.7 interpreted as 'Very Acceptable' with a substantial agreement (0. 60) from the validators. One intact class of 46 Grade 12 STEM students from one of the public schools in Paranaque City served as the participants of this study. The students were taught using the module during the first semester of the academic year 2019–2020. Employing the mixed-method approach, quantitative data were gathered using pretest/posttest, activity sheets, problem sets, and survey form, while qualitative data were obtained from surveys, interviews, observations, and reflection log. After the implementation, there was a significant difference of 18.4 on students’ conceptual understanding as shown in their pre-test and post-test scores on the 24-item test with a moderate Hake gain equal to 0.45 and an effect size of 0.83. Moreover, the scores on activity and problem sets have a 'very good' to 'excellent' rating, which signifies an increase in the level of students’ conceptual understanding. There also exists a significant difference between the mean scores of students’ engagement overall (t= 4.79, p = 0.000, p < 0.05) and in the dimension of emotion (t = 2.51, p = 0.03) and participation/interaction (t = 5.75, p = 0.001). These findings were supported by gathered qualitative data. Positive views were elicited from the students since it is an accessible tool for learning and has well-detailed explanations and examples. The results of this study may substantiate that using MVOK will lead to better physics content understanding and higher engagement.

Keywords: conceptual understanding, engagement, linear motion, module

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Authors: Han Ding, Wang Xiaoliang, Duan Dengping

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During the process of airship design, the layout and the geometric shape of the hull and fins are crucial to the motion characteristics of the airship. In this paper, we obtained the quantification motion sensitivity of the airship to geometric parameters through turning circles and horizontal/vertical zigzag maneuvers by the parameterization of airship shape and building the dynamic model using Lagrangian approach and MATLAB Simulink program. In the dynamics simulation program, the affection of geometric parameters to the mass, center of gravity, moments of inertia, product of inertia, added mass and the aerodynamic forces and moments have been considered.

Keywords: airship, Lagrangian approach, turning circles, horizontal/vertical zigzag maneuvers

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Authors: Haider Aamir, Muhammad Ali Khalid

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Electricity harvesting speed bump (EHSB) is speed breaker of conventional shape, but the difference is that it is not fixed, rather it moves up and down, and electricity can be generated from its vibrating motion. This speed bump consists of an upper cover which will move up and down, a shaft mechanism which will be used to drive the generator and a rack and pinion mechanism which will connect the cover and shaft. There is a spring mechanism to return the cover to its initial state when a vehicle has passed over the bump. Produced energy in the past was up to 80 Watts. For this purpose, a clutch mechanism is used so that both the up-down movements of the cover can be used to drive the generator. Mechanical Motion Rectifier (MMR) mechanism ensures the conversion of both the linear motions into rotational motion which is used to drive the generator.

Keywords: electricity harvesting, generator, rack and pinion, stainless steel shaft

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Authors: Wen-Yuh Jywe, Bor-Jeng Lin, Jing-Chung Shen, Jeng-Dao Lee, Hsueh-Liang Huang, Tung-Hsien Hsieh

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In this study, a simple 2-D measurement system based on optical design was developed to measure the motion errors of the linear guideway. Compared with the transitional methods about the linear guideway for measuring the motion errors, our proposed 2-D optical measurement system can simultaneously measure horizontal and vertical running straightness errors for the linear guideway. The performance of the 2-D optical measurement system is verified by experimental results. The standard deviation of the 2-D optical measurement system is about 0.4 μm in the measurement range of 100 mm. The maximum measuring speed of the proposed automatic measurement instrument is 1 m/sec.

Keywords: 2-D measurement, linear guideway, motion errors, running straightness

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Authors: Yuan-Hsiang Chang, Pin-Chi Lin, Li-Der Jeng

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Advance in techniques of image and video processing has enabled the development of intelligent video surveillance systems. This study was aimed to automatically detect moving human objects and to analyze events of dual human interaction in a surveillance scene. Our system was developed in four major steps: image preprocessing, human object detection, human object tracking, and motion trajectory analysis. The adaptive background subtraction and image processing techniques were used to detect and track moving human objects. To solve the occlusion problem during the interaction, the Kalman filter was used to retain a complete trajectory for each human object. Finally, the motion trajectory analysis was developed to distinguish between the interaction and non-interaction events based on derivatives of trajectories related to the speed of the moving objects. Using a database of 60 video sequences, our system could achieve the classification accuracy of 80% in interaction events and 95% in non-interaction events, respectively. In summary, we have explored the idea to investigate a system for the automatic classification of events for interaction and non-interaction events using surveillance cameras. Ultimately, this system could be incorporated in an intelligent surveillance system for the detection and/or classification of abnormal or criminal events (e.g., theft, snatch, fighting, etc.).

Keywords: motion detection, motion tracking, trajectory analysis, video surveillance

Procedia PDF Downloads 519

Authors: Fatemeh Sadeghi, Peyman Sheikhzadeh

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Introduction: Block Sequential Regularized Expectation Maximization (BSREM) reconstruction algorithm was recently developed to suppress excessive noise by applying a relative difference penalty. The aim of this study was to investigate the effect of various strengths of noise penalization factor in the BSREM algorithm under different acquisition duration and lesion sizes in order to determine an optimum penalty factor by considering both quantitative and qualitative image evaluation parameters in clinical uses. Materials and Methods: The NEMA IQ phantom and 15 clinical whole-body patients with prostate cancer were evaluated. Phantom and patients were injected withGallium-68 Prostate-Specific Membrane Antigen(68 Ga-PSMA)and scanned on a non-time-of-flight Discovery IQ Positron Emission Tomography/Computed Tomography(PET/CT) scanner with BGO crystals. The data were reconstructed using BSREM with a β-value of 100-500 at an interval of 100. These reconstructions were compared to OSEM as a widely used reconstruction algorithm. Following the standard NEMA measurement procedure, background variability (BV), recovery coefficient (RC), contrast recovery (CR) and residual lung error (LE) from phantom data and signal-to-noise ratio (SNR), signal-to-background ratio (SBR) and tumor SUV from clinical data were measured. Qualitative features of clinical images visually were ranked by one nuclear medicine expert. Results: The β-value acts as a noise suppression factor, so BSREM showed a decreasing image noise with an increasing β-value. BSREM, with a β-value of 400 at a decreased acquisition duration (2 min/ bp), made an approximately equal noise level with OSEM at an increased acquisition duration (5 min/ bp). For the β-value of 400 at 2 min/bp duration, SNR increased by 43.7%, and LE decreased by 62%, compared with OSEM at a 5 min/bp duration. In both phantom and clinical data, an increase in the β-value is translated into a decrease in SUV. The lowest level of SUV and noise were reached with the highest β-value (β=500), resulting in the highest SNR and lowest SBR due to the greater noise reduction than SUV reduction at the highest β-value. In compression of BSREM with different β-values, the relative difference in the quantitative parameters was generally larger for smaller lesions. As the β-value decreased from 500 to 100, the increase in CR was 160.2% for the smallest sphere (10mm) and 12.6% for the largest sphere (37mm), and the trend was similar for SNR (-58.4% and -20.5%, respectively). BSREM visually was ranked more than OSEM in all Qualitative features. Conclusions: The BSREM algorithm using more iteration numbers leads to more quantitative accuracy without excessive noise, which translates into higher overall image quality and lesion detectability. This improvement can be used to shorter acquisition time.

Keywords: BSREM reconstruction, PET/CT imaging, noise penalization, quantification accuracy

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Authors: Esra Zengin, Sinan Akkar

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Reliable and accurate prediction of nonlinear structural response requires specification of appropriate earthquake ground motions to be used in nonlinear time history analysis. The current research has mainly focused on selection and manipulation of real earthquake records that can be seen as the most critical step in the performance based seismic design and assessment of the structures. Utilizing amplitude scaled ground motions that matches with the target spectra is commonly used technique for the estimation of nonlinear structural response. Representative ground motion ensembles are selected to match target spectrum such as scenario-based spectrum derived from ground motion prediction equations, Uniform Hazard Spectrum (UHS), Conditional Mean Spectrum (CMS) or Conditional Spectrum (CS). Different sets of criteria exist among those developed methodologies to select and scale ground motions with the objective of obtaining robust estimation of the structural performance. This study presents ground motion selection and scaling procedure that considers the spectral variability at target demand with the level of ground motion dispersion. The proposed methodology provides a set of ground motions whose response spectra match target median and corresponding variance within a specified period interval. The efficient and simple algorithm is used to assemble the ground motion sets. The scaling stage is based on the minimization of the error between scaled median and the target spectra where the dispersion of the earthquake shaking is preserved along the period interval. The impact of the spectral variability on nonlinear response distribution is investigated at the level of inelastic single degree of freedom systems. In order to see the effect of different selection and scaling methodologies on fragility curve estimations, results are compared with those obtained by CMS-based scaling methodology. The variability in fragility curves due to the consideration of dispersion in ground motion selection process is also examined.

Keywords: ground motion selection, scaling, uncertainty, fragility curve

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Authors: Wen Zhen

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Compared to content words, function words have been relatively overlooked by English learners especially articles. The article system, to a certain extent, becomes a resistance to know English better, driven by different elements. Three principal factors can be summarized in term of the nature of the articles when referring to the difficulty of the English article system. However, making the article system more complex are difficulties in the second acquisition process, for [-ART] learners have to create another category, causing even most non-native speakers at proficiency level to make errors. According to the sequences of acquisition of the English article, it is showed that the zero article is first acquired and in high inaccuracy. The zero article is often overused in the early stages of L2 acquisition. Although learners at the intermediate level move to underuse the zero article for they realize that the zero article does not cover any case, overproduction of the zero article even occurs among advanced L2 learners. The aim of the study is to investigate noun-phrase factors which give rise to incorrect usage or overuse of the zero article, thus providing suggestions for L2 English acquisition. Moreover, it enables teachers to carry out effective instruction that activate conscious learning of students. The research question will be answered through a corpus-based, data- driven approach to analyze the noun-phrase elements from the semantic context and countability of noun-phrases. Based on the analysis of the International Thurber Thesis corpus, the results show that: (1) Although context of [-definite,-specific] favored the zero article, both[-definite,+specific] and [+definite,-specific] showed less influence. When we reflect on the frequency order of the zero article , prototypicality plays a vital role in it .(2)EFL learners in this study have trouble classifying abstract nouns as countable. We can find that it will bring about overuse of the zero article when learners can not make clear judgements on countability altered from (+definite ) to (-definite).Once a noun is perceived as uncountable by learners, the choice would fall back on the zero article. These findings suggest that learners should be engaged in recognition of the countability of new vocabulary by explaining nouns in lexical phrases and explore more complex aspects such as analysis dependent on discourse.

Keywords: noun phrase, zero article, corpus, second language acquisition

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Authors: Jeronimo Cox, Tomonari Furukawa

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Magnetometers have become more popular in inertial measurement units (IMU) for their ability to correct estimations using the earth's magnetic field. Accelerometer and gyroscope-based packages fail with dead-reckoning errors accumulated over time. Localization in robotic applications with magnetometer-inclusive IMUs has become popular as a way to track the odometry of slower-speed robots. With high-speed motions, the accumulated error increases over smaller periods of time, making them difficult to track with IMU. Tracking a high-speed motion is especially difficult with limited observability. Visual obstruction of motion leaves motion-tracking cameras unusable. When motions are too dynamic for estimation techniques reliant on the observability of the gravity vector, the use of magnetometers is further justified. As available magnetometer calibration methods are limited with the assumption that background magnetic fields are uniform, estimation in nonuniform magnetic fields is problematic. Hard iron distortion is a distortion of the magnetic field by other objects that produce magnetic fields. This kind of distortion is often observed as the offset from the origin of the center of data points when a magnetometer is rotated. The magnitude of hard iron distortion is dependent on proximity to distortion sources. Soft iron distortion is more related to the scaling of the axes of magnetometer sensors. Hard iron distortion is more of a contributor to the error of attitude estimation with magnetometers. Indoor environments or spaces inside ferrite-based structures, such as building reinforcements or a vehicle, often cause distortions with proximity. As positions correlate to areas of distortion, methods of magnetometer localization include the production of spatial mapping of magnetic field and collection of distortion signatures to better aid location tracking. The goal of this paper is to compare magnetometer methods that don't need pre-productions of magnetic field maps. Mapping the magnetic field in some spaces can be costly and inefficient. Dynamic measurement fusion is used to track the motion of a multi-link system with us. Conventional calibration by data collection of rotation at a static point, real-time estimation of calibration parameters each time step, and using two magnetometers for determining local hard iron distortion are compared to confirm the robustness and accuracy of each technique. With opposite-facing magnetometers, hard iron distortion can be accounted for regardless of position, Rather than assuming that hard iron distortion is constant regardless of positional change. The motion measured is a repeatable planar motion of a two-link system connected by revolute joints. The links are translated on a moving base to impulse rotation of the links. Equipping the joints with absolute encoders and recording the motion with cameras to enable ground truth comparison to each of the magnetometer methods. While the two-magnetometer method accounts for local hard iron distortion, the method fails where the magnetic field direction in space is inconsistent.

Keywords: motion tracking, sensor fusion, magnetometer, state estimation

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Authors: Mohammad Bakhshandeh

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

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

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Authors: Hyojin Lim, Cuong Nguyen Khac, Yeongyu Choi, Ho-Youl Jung

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In this paper, we propose a simple moving object detection, which is based on motion vectors obtained from pyramidal Lucas-Kanade optical flow. The proposed method detects moving objects such as pedestrians, the other vehicles and some obstacles at the front-side of the host vehicle, and it can provide the warning to the driver. Motion vectors are obtained by using pyramidal Lucas-Kanade optical flow, and some outliers are eliminated by comparing the amplitude of each vector with the pre-defined threshold value. The background model is obtained by calculating the mean and the variance of the amplitude of recent motion vectors in the rectangular shaped local region called the cell. The model is applied as the reference to classify motion vectors of moving objects and those of background. Motion vectors are clustered to rectangular regions by using the unsupervised clustering K-means algorithm. Labeling method is applied to label groups which is close to each other, using by distance between each center points of rectangular. Through the simulations tested on four kinds of scenarios such as approaching motorbike, vehicle, and pedestrians to host vehicle, we prove that the proposed is simple but efficient for moving object detection in parking lots.

Keywords: moving object detection, dynamic scene, optical flow, pyramidal optical flow

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Authors: Bagus Hanindhito, Fariz Azmi Pratama, Ulfah Nadiya

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Nowadays, digital system including, but not limited to, computer and internet have penetrated the education system widely. Critical information such as students’ academic records is stored in a server off- or on-campus. Although several countermeasures have been taken to protect the vital resources from outsider attack, the defense from insiders threat is not getting serious attention. At the end of 2017, a security incident that involved academic information system in one of the most respected universities in Indonesia affected not only the reputation of the institution and its academia but also academic integrity in Indonesia. In this paper, we will explain our efforts in investigating this security incident where we have implemented a novel rapid evidence remote acquisition method in high-availability server and storage system thus our data collection efforts do not disrupt the academic information system and can be conducted remotely minutes after incident report has been received. The acquired evidence is analyzed during digital forensic by constructing the model of the system in an isolated environment which allows multiple investigators to work together. In the end, the suspect is identified as a student (insider), and the investigation result is used by prosecutors to charge the suspect as an academic crime.

Keywords: academic information system, academic crime, digital forensic, high-availability server and storage, rapid evidence remote acquisition, security incident

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Authors: Ebasa Girma, Nathnael Minyelshowa, Lebsework Negash

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The use of unmanned aerial vehicles (UAVs) in precision agriculture has seen a huge increase recently. As such, systems that aim to apply various algorithms on the field need a structured framework of abstractions. This paper defines the various tasks of the UAVs in precision agriculture and models them into an architectural framework. The presented architecture is built on the context that there will be minimal physical intervention to do the tasks defined with multiple coordinated and cooperative UAVs. Various tasks such as image processing, path planning, communication, data acquisition, and field mapping are employed in the architecture to provide an efficient system. Besides, different limitation for applying Multi-UAVs in precision agriculture has been considered in designing the architecture. The architecture provides an autonomous end-to-end solution, starting from mission planning, data acquisition, and image processing framework that is highly efficient and can enable farmers to comprehensively deploy UAVs onto their lands. Simulation and field tests show that the architecture offers a number of advantages that include fault-tolerance, robustness, developer, and user-friendliness.

Keywords: deep learning, multi-UAVs, precision agriculture, UAVs architecture

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Authors: Anosh Arshad Sundhu, Francesco Giordano, Giacomo Della Croce, Maurizio Arnone

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Digital Twin (DT) is a technology that generates a virtual representation of a physical system or process, enabling real-time monitoring, analysis, and simulation. DT of an Electrical Distribution System (EDS) can perform online analysis by integrating the static and real-time data in order to show the current grid status and predictions about the future status to the Distribution System Operator (DSO), producers and consumers. DT technology for EDS also offers the opportunity to DSO to test hypothetical scenarios. This paper discusses the development of a DT of an EDS by Smart Grid Controller (SGC) application, which is developed using open-source libraries and languages. The developed application can be integrated with Supervisory Control and Data Acquisition System (SCADA) of any EDS for creating the DT. The paper shows the performance of developed tools inside the application, tested on real EDS for grid observability, Smart Recursive Load Flow (SRLF) calculation and state estimation of loads in MV feeders.

Keywords: digital twin, distributed energy resources, remote terminal units, supervisory control and data acquisition system, smart recursive load flow

Procedia PDF Downloads 82