Search results for: pointing accuracy

Authors: Fatema Tasmia, Brishti Majumder, Atiqur Rahman

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Attaining appropriate comfort conditions in a place where the climate is hot and humid can be perplexing. Especially, when it is resided at a congested place like old Dhaka Bangladesh, the provision of giving cross ventilation and building with proper orientation is quite difficult. Courtyards are the part of buildings which are used as space for outdoor household activities, social gathering and it is also proved to have indoor thermal comfort as an effect of courtyard. This paper aims to investigate the effect of courtyard in indoor thermal environment of a room adjacent to the courtyard and a room next to transitional space after a courtyard through field measurements of a case study house. The field measurement was conducted in a two-storey house. Among different aspects of thermal environment, the study of this paper is based on the analysis of temperature in both situations. Ventilation or air movement was considered to have no impact because of the rooms’ layout and location. Other aspects and their variables were considered as constant (especially material) for accuracy and avoidance of confusion. This study focuses on the outcome that can ultimately contribute to the configuration of courtyards and in its relation to indoor space while achieving thermal comfort.

Keywords: courtyard, old Dhaka, temperature, thermal comfort, transitional space

Procedia PDF Downloads 199

Authors: Tong He, Long Chen, Irag Mantegh, Wen-Fang Xie

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This paper proposes an image-based obstacle avoidance and tracking target identification strategy in GPS-degraded or GPS-denied environment for an Unmanned Aerial Vehicle (UAV). The traditional force algorithm for obstacle avoidance could produce local minima area, in which UAV cannot get away obstacle effectively. In order to eliminate it, an artificial potential approach based on harmonic potential is proposed to guide the UAV to avoid the obstacle by using the vision system. And image-based visual servoing scheme (IBVS) has been adopted to implement the proposed obstacle avoidance approach. In IBVS, the pixel accuracy is a key factor to realize the obstacle avoidance. In this paper, the deep reinforcement learning framework has been applied by reducing pixel errors through constant interaction between the environment and the agent. In addition, the combination of OpenTLD and Tensorflow based on neural network is used to identify the type of tracking target. Numerical simulation in Matlab and ROS GAZEBO show the satisfactory result in target identification and obstacle avoidance.

Keywords: image-based visual servoing, obstacle avoidance, tracking target identification, deep reinforcement learning, artificial potential approach, neural network

Procedia PDF Downloads 121

Authors: Cheng Fang, Lingwei Quan, Cunyue Lu

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Human pose estimation and tracking are to accurately identify and locate the positions of human joints in the video. It is a computer vision task which is of great significance for human motion recognition, behavior understanding and scene analysis. There has been remarkable progress on human pose estimation in recent years. However, more researches are needed for human pose tracking especially for online tracking. In this paper, a framework, called PoseSRPN, is proposed for online single-person pose estimation and tracking. We use Siamese network attaching a pose estimation branch to incorporate Single-person Pose Tracking (SPT) and Visual Object Tracking (VOT) into one framework. The pose estimation branch has a simple network structure that replaces the complex upsampling and convolution network structure with deconvolution. By augmenting the loss of fully convolutional Siamese network with the pose estimation task, pose estimation and tracking can be trained in one stage. Once trained, PoseSRPN only relies on a single bounding box initialization and producing human joints location. The experimental results show that while maintaining the good accuracy of pose estimation on COCO and PoseTrack datasets, the proposed method achieves a speed of 59 frame/s, which is superior to other pose tracking frameworks.

Keywords: computer vision, pose estimation, pose tracking, Siamese network

Procedia PDF Downloads 132

Authors: Un-Hwan Park, Jun-Hyeok Heo, In-Sung Lee, Seong-Jin Cho, Tae-Hyeon Oh, Dae-Kyu Park

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Sound absorption coefficient is considered important when designing because noise affects emotion quality of car. It is designed with lots of experiment tunings in the field because it is unreliable to predict it for multi-layer material. In this paper, we present the design of sound absorption for automotive interior material with multiple layers using estimation software of sound absorption coefficient for reverberation chamber. Additionally, we introduce the method for estimation of physical properties required to predict sound absorption coefficient of car interior materials with multiple layers too. It is calculated by inverse algorithm. It is very economical to get information about physical properties without expensive equipment. Correlation test is carried out to ensure reliability for accuracy. The data to be used for the correlation is sound absorption coefficient measured in the reverberation chamber. In this way, it is considered economical and efficient to design automotive interior materials. And design optimization for sound absorption coefficient is also easy to implement when it is designed.

Keywords: sound absorption coefficient, optimization design, inverse algorithm, automotive interior material, multiple layers nonwoven, scaled reverberation chamber, sound impedance tubes

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Authors: Junjie Zhong, Kai Hong, Lei Wang

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Passage Retrieval (PR) plays an important role in many Natural Language Processing (NLP) tasks. Traditional efficient retrieval models relying on exact term-matching, such as TF-IDF or BM25, have nowadays been exceeded by pre-trained language models which match by semantics. Though they gain effectiveness, deep language models often require large memory as well as time cost. To tackle the trade-off between efficiency and effectiveness in PR, this paper proposes Graph Passage Retriever (GraphPR), a graph-based model inspired by the development of graph learning techniques. Different from existing works, GraphPR is end-to-end and integrates both term-matching information and semantics. GraphPR constructs a passage-level graph from BM25 retrieval results and trains a GCN-like model on the graph with graph-based objectives. Passages were regarded as nodes in the constructed graph and were embedded in dense vectors. PR can then be implemented using embeddings and a fast vector-similarity search. Experiments on a variety of real-world retrieval datasets show that the proposed model outperforms related models in several evaluation metrics (e.g., mean reciprocal rank, accuracy, F1-scores) while maintaining a relatively low query latency and memory usage.

Keywords: efficiency, effectiveness, graph learning, language model, passage retrieval, term-matching model

Procedia PDF Downloads 108

Authors: Seyedeh Mandan Kazzazi, Elena Kranioti

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In the field of human osteology, sex estimation is an important step in developing biological profile. There are a number of methods that can be used to estimate the sex of human remains varying from visual assessments to metric analysis of sexually dimorphic traits. Teeth are one of the most durable physical elements in human body that can be used for this purpose. The present study investigated the utility of cervical measurements for sex estimation through discriminant analysis. The permanent molar teeth of 75 skeletons (28 females and 52 males) from Hasanlu site in North-western Iran were studied. Cervical mesiodistal and buccolingual measurements were taken from both maxillary and mandibular first and second molars. Discriminant analysis was used to evaluate the accuracy of each diameter in assessing sex. The results showed that males had statistically larger teeth than females for maxillary and mandibular molars and both measurements (P < 0.05). The range of classification rate was from (75.7% to 85.5%) for the original and cross-validated data. The most dimorphic teeth were maxillary and mandibular second molars providing 85.5% and 83.3% correct classification rate respectively. The data generated from the present study suggested that cervical mesiodistal and buccolingual measurements of the molar teeth can be useful and reliable for sex estimation in Iranian archaeological populations.

Keywords: cervical measurements, Hasanlu, premolars, sex estimation

Procedia PDF Downloads 311

Authors: Hamid Ahmadi, Shadi Asoodeh

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The fatigue life of tubular joints commonly found in offshore industry is not only dependent on the value of hot-spot stress (HSS), but is also significantly influenced by the through-the-thickness stress distribution characterized by the degree of bending (DoB). The determination of DoB values in a tubular joint is essential for improving the accuracy of fatigue life estimation using the stress-life (S–N) method and particularly for predicting the fatigue crack growth based on the fracture mechanics (FM) approach. In the present paper, data extracted from finite element (FE) analyses of tubular KT-joints, verified against experimental data and parametric equations, was used to investigate the effects of geometrical parameters on DoB values at the crown 0˚, saddle, and crown 180˚ positions along the weld toe of central brace in tubular KT-joints subjected to axial loading. Parametric study was followed by a set of nonlinear regression analyses to derive DoB parametric formulas for the fatigue analysis of KT-joints under axial loads. The tubular KT-joint is a quite common joint type found in steel offshore structures. However, despite the crucial role of the DoB in evaluating the fatigue performance of tubular joints, this paper is the first attempt to study and formulate the DoB values in KT-joints.

Keywords: tubular KT-joint, fatigue, degree of bending (DoB), axial loading, parametric formula

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Authors: Masoud Ghermezi

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Studying the sheet metals is one of the most important research areas in the field of metal forming due to their extensive applications in the aerospace industries. A useful method for determining the forming limit of these materials and consequently preventing the rupture of sheet metals during the forming process is the use of the forming limit curve (FLC). In addition to specifying the forming limit, this curve also delineates a boundary for the allowed values of strain in sheet metal forming; these characteristics of the FLC along with its accuracy of computation and wide range of applications have made this curve the basis of research in the present paper. This study presents a new model that not only agrees with the results obtained from the above mentioned theory, but also eliminates its shortcomings. In this theory, like in the M-K theory, a thin sheet with an inhomogeneity as a gradient thickness reduction with a sinusoidal function has been chosen and subjected to two-dimensional stress. Through analytical evaluation, ultimately, a governing differential equation has been obtained. The numerical solution of this equation for the range of positive strains (stretched region) yields the results that agree with the results obtained from M-K theory. Also the solution of this equation for the range of negative strains (tension region) completes the FLC curve. The findings obtained by applying this equation on two alloys with the hardening exponents of 0.4 and 0.24 indicate the validity of the presented equation.

Keywords: sheet metal, metal forming, forming limit curve (FLC), M-K theory

Procedia PDF Downloads 343

Authors: Nursuhaili Najwa Masrol, Nur Hafizah Mohammed, Nur Nadhirah Rusyda Rosnan, Vijaya Subramaniam, Sim Choon Cheak

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Replanting in oil palm cultivation is vital to enable the introduction of planting materials and provides an opportunity to improve the road, drainage, terrace design, and planting density. Oil palm replanting is fundamentally necessary every 25 years. The adoption of the digital replanting blueprint is imperative as it can assist the Malaysia Oil Palm industry in addressing challenges such as labour shortages and limited expertise related to replanting tasks. Effective replanting planning should commence at least 6 months prior to the actual replanting process. Therefore, this study will help to plan and design the replanting blueprint with high-precision translation on the ground. With the advancement of geospatial technology, it is now feasible to engage in thoroughly researched planning, which can help maximize the potential yield. A blueprint designed before replanting is to enhance management’s ability to optimize the planting program, address manpower issues, or even increase productivity. In terrace planting blueprints, geographic tools have been utilized to design the roads, drainages, terraces, and planting points based on the ARM standards. These designs are mapped with location information and undergo statistical analysis. The geospatial approach is essential in precision agriculture and ensuring an accurate translation of design to the ground by implementing high-accuracy technologies. In this study, geospatial and remote sensing technologies played a vital role. LiDAR data was employed to determine the Digital Elevation Model (DEM), enabling the precise selection of terraces, while ortho imagery was used for validation purposes. Throughout the designing process, Geographical Information System (GIS) tools were extensively utilized. To assess the design’s reliability on the ground compared with the current conventional method, high-precision GPS instruments like EOS Arrow Gold and HIPER VR GNSS were used, with both offering accuracy levels between 0.3 cm and 0.5cm. Nearest Distance Analysis was generated to compare the design with actual planting on the ground. The analysis revealed that it could not be applied to the roads due to discrepancies between actual roads and the blueprint design, which resulted in minimal variance. In contrast, the terraces closely adhered to the GPS markings, with the most variance distance being less than 0.5 meters compared to actual terraces constructed. Considering the required slope degrees for terrace planting, which must be greater than 6 degrees, the study found that approximately 65% of the terracing was constructed at a 12-degree slope, while over 50% of the terracing was constructed at slopes exceeding the minimum degrees. Utilizing blueprint replanting promising strategies for optimizing land utilization in agriculture. This approach harnesses technology and meticulous planning to yield advantages, including increased efficiency, enhanced sustainability, and cost reduction. From this study, practical implementation of this technique can lead to tangible and significant improvements in agricultural sectors. In boosting further efficiencies, future initiatives will require more sophisticated techniques and the incorporation of precision GPS devices for upcoming blueprint replanting projects besides strategic progression aims to guarantee the precision of both blueprint design stages and its subsequent implementation on the field. Looking ahead, automating digital blueprints are necessary to reduce time, workforce, and costs in commercial production.

Keywords: replanting, geospatial, precision agriculture, blueprint

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Authors: Andrew R. Winters, Gregor J. Gassner

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A high-order numerical magentohydrodynamics (MHD) solver built upon a non-linear entropy stable numerical flux function that supports eight traveling wave solutions will be described. The method is designed to treat the divergence-free constraint on the magnetic field in a similar fashion to a hyperbolic divergence cleaning technique. The solver is especially well-suited for flows involving strong discontinuities due to its strong stability without the need to enforce artificial low density or energy limits. Furthermore, a new formulation of the numerical algorithm to guarantee positivity of the pressure during the simulation is described and presented. By construction, the solver conserves mass, momentum, and energy and is entropy stable. High spatial order is obtained through the use of a third order limiting technique. High temporal order is achieved by utilizing the family of strong stability preserving (SSP) Runge-Kutta methods. Main attributes of the solver are presented as well as details on an implementation of the new solver into the multi-physics, multi-scale simulation code FLASH. The accuracy, robustness, and computational efficiency is demonstrated with a variety of numerical tests. Comparisons are also made between the new solver and existing methods already present in FLASH framework.

Keywords: entropy stability, finite volume scheme, magnetohydrodynamics, pressure positivity

Procedia PDF Downloads 322

Authors: Aakansha Gupta, Neha Vadnere, Tapasvi Soni, M. Anbarsi

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Protein secondary structure prediction is one of the most important goals pursued by bioinformatics and theoretical chemistry; it is highly important in medicine and biotechnology. Some aspects of protein functions and genome analysis can be predicted by secondary structure prediction. This is used to help annotate sequences, classify proteins, identify domains, and recognize functional motifs. In this paper, we represent protein secondary structure as a mathematical model. To extract and predict the protein secondary structure from the primary structure, we require a set of parameters. Any constants appearing in the model are specified by these parameters, which also provide a mechanism for efficient and accurate use of data. To estimate these model parameters there are many algorithms out of which the most popular one is the EM algorithm or called the Expectation Maximization Algorithm. These model parameters are estimated with the use of protein datasets like RS126 by using the Bayesian Probabilistic method (data set being categorical). This paper can then be extended into comparing the efficiency of EM algorithm to the other algorithms for estimating the model parameters, which will in turn lead to an efficient component for the Protein Secondary Structure Prediction. Further this paper provides a scope to use these parameters for predicting secondary structure of proteins using machine learning techniques like neural networks and fuzzy logic. The ultimate objective will be to obtain greater accuracy better than the previously achieved.

Keywords: model parameters, expectation maximization algorithm, protein secondary structure prediction, bioinformatics

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Authors: Kritiyaporn Kunsook

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Machine learning algorithms (MLAs) such us artificial neural networks (ANNs), decision tree, support vector machines (SVMs), Naïve Bayes, and ensemble classifier by voting are powerful data driven methods that are relatively less widely used in the mapping of technique of system, and thus have not been comparatively evaluated together thoroughly in this field. The performances of a series of MLAs, ANNs, decision tree, SVMs, Naïve Bayes, and ensemble classifier by voting in technique of hydroponic systems prospectively modeling are compared based on the accuracy of each model. Classification of hydroponic systems only covers the test samples from vegetables grown with Nutrient film technique (NFT) and Deep flow technique (DFT). The feature, which are the characteristics of vegetables compose harvesting height width, temperature, require light and color. The results indicate that the classification performance of the ANNs is 98%, decision tree is 98%, SVMs is 97.33%, Naïve Bayes is 96.67%, and ensemble classifier by voting is 98.96% algorithm respectively.

Keywords: artificial neural networks, decision tree, support vector machines, naïve Bayes, ensemble classifier by voting

Procedia PDF Downloads 339

Authors: Karim Hamidi Machekposhti, Hossein Sedghi, Abdolrasoul Telvari, Hossein Babazadeh

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Floods have huge environmental and economic impact. Therefore, flood prediction is given a lot of attention due to its importance. This study analysed the annual maximum streamflow (discharge) (AMS or AMD) of Karkheh River in Karkheh River Basin for flood predicting using ARIMA model. For this purpose, we use the Box-Jenkins approach, which contains four-stage method model identification, parameter estimation, diagnostic checking and forecasting (predicting). The main tool used in ARIMA modelling was the SAS and SPSS software. Model identification was done by visual inspection on the ACF and PACF. SAS software computed the model parameters using the ML, CLS and ULS methods. The diagnostic checking tests, AIC criterion, RACF graph and RPACF graphs, were used for selected model verification. In this study, the best ARIMA models for Annual Maximum Discharge (AMD) time series was (4,1,1) with their AIC value of 88.87. The RACF and RPACF showed residuals’ independence. To forecast AMD for 10 future years, this model showed the ability of the model to predict floods of the river under study in the Karkheh River Basin. Model accuracy was checked by comparing the predicted and observation series by using coefficient of determination (R²).

Keywords: time series modelling, stochastic processes, ARIMA model, Karkheh river

Procedia PDF Downloads 271

Authors: Nayden Chivarov, Denis Chikurtev, Kaloyan Yovchev, Nedko Shivarov

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This paper represents methods for improving the efficiency and precision of service mobile robot. This robot is used for increasing the quality of life of elderly and disabled people. The key concept of the proposed Intelligent Service Mobile Robot is its easier adaptability to achieve services for a wide range of Elderly or Disabled Person’s needs, by performing different tasks for supporting Elderly or Disabled Persons care. We developed robot autonomous navigation and computer vision systems in order to recognize different objects and bring them to the people. Web based user interface is developed to provide easy access and tele-control of the robot by any device through the internet. In this study algorithms for object recognition and localization are proposed for providing successful object recognition and accuracy in the positioning. Different methods for sending movement commands to the mobile robot system are proposed and evaluated. After executing some experiments to show the results of the research, we can summarize that these systems and algorithms provide good control of the service mobile robot and it will be more useful to help the elderly and disabled persons.

Keywords: service robot, mobile robot, autonomous navigation, computer vision, web user interface, ROS

Procedia PDF Downloads 319

Authors: Samar Alkindy

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Purpose: The BIM model has been used to support the planning of different construction projects in the industry by showing the different stages of the construction process. The model has been instrumental in identifying some of the common errors in the construction process through the spatial arrangement. The continuous use of the BIM model in the construction industry has resulted in various radical changes such as virtual prototyping. Construction virtual prototyping is a highly advanced technology that incorporates a BIM model with realistic graphical simulations, and facilitates the simulation of the project before a product is built in the factory. The paper presents virtual prototyping in the construction industry by examining its application, challenges and benefits to a construction project. Methodology approach: A case study was conducted for this study in four major construction projects, which incorporate virtual construction prototyping in several stages of the construction project. Furthermore, there was the administration of interviews with the project manager and engineer and the planning manager. Findings: Data collected from the methodological approach shows a positive response for virtual construction prototyping in construction, especially concerning communication and visualization. Furthermore, the use of virtual prototyping has increased collaboration and efficiency between construction experts handling a project. During the planning stage, virtual prototyping has increased accuracy, reduced planning time, and reduced the amount of rework during the implementation stage. Irrespective of virtual prototyping being a new concept in the construction industry, the findings outline that the approach will benefit the management of construction projects.

Keywords: construction operations, construction planning, process simulation, virtual prototyping

Procedia PDF Downloads 206

Authors: Emna Benmohamed, Hela Ltifi, Mounir Ben Ayed

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Bayesian Network (BN) is one of the most efficient classification methods. It is widely used in several fields (i.e., medical diagnostics, risk analysis, bioinformatics research). The BN is defined as a probabilistic graphical model that represents a formalism for reasoning under uncertainty. This classification method has a high-performance rate in the extraction of new knowledge from data. The construction of this model consists of two phases for structure learning and parameter learning. For solving this problem, the K2 algorithm is one of the representative data-driven algorithms, which is based on score and search approach. In addition, the integration of the expert's knowledge in the structure learning process allows the obtainment of the highest accuracy. In this paper, we propose a hybrid approach combining the improvement of the K2 algorithm called K2 algorithm for Parents and Children search (K2PC) and the expert-driven method for learning the structure of BN. The evaluation of the experimental results, using the well-known benchmarks, proves that our K2PC algorithm has better performance in terms of correct structure detection. The real application of our model shows its efficiency in the analysis of the phosphate laundry effluents' impact on the watershed in the Gafsa area (southwestern Tunisia).

Keywords: Bayesian network, classification, expert knowledge, structure learning, surface water analysis

Procedia PDF Downloads 103

Authors: Po-Jen Su, Huann-Ming Chou

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In this article we uses the residual correction method to deal with transient thermoelastic problems with a hollow spherical region when the continuum medium possesses spherically isotropic thermoelastic properties. Based on linear thermoelastic theory, the equations of hyperbolic heat conduction and thermoelastic motion were combined to establish the thermoelastic dynamic model with consideration of the deformation acceleration effect and non-Fourier effect under the condition of transient thermal shock. The approximate solutions of temperature and displacement distributions are obtained using the residual correction method based on the maximum principle in combination with the finite difference method, making it easier and faster to obtain upper and lower approximations of exact solutions. The proposed method is found to be an effective numerical method with satisfactory accuracy. Moreover, the result shows that the effect of transient thermal shock induced by deformation acceleration is enhanced by non-Fourier heat conduction with increased peak stress. The influence on the stress increases with the thermal relaxation time.

Keywords: maximum principle, non-Fourier heat conduction, residual correction method, thermo-elastic response

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Authors: Seun Mayowa Sunday

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Loan prediction is one of the most significant and recognised fields of research in the banking, insurance, and the financial security industries. Some prediction systems on the market include the construction of static software. However, due to the fact that static software only operates with strictly regulated rules, they cannot aid customers beyond these limitations. Application of many machine learning (ML) techniques are required for loan prediction. Four separate machine learning models, random forest (RF), decision tree (DT), k-nearest neighbour (KNN), and logistic regression, are used to create the loan prediction model. Using the anaconda navigator and the required machine learning (ML) libraries, models are created and evaluated using the appropriate measuring metrics. From the finding, the random forest performs with the highest accuracy of 80.17% which was later implemented into the Django framework. For real-time testing, the web application is deployed on the Alibabacloud which is among the top 4 biggest cloud computing provider. Hence, to the best of our knowledge, this research will serve as the first academic paper which combines the model development and the Django framework, with the deployment into the Alibaba cloud computing application.

Keywords: k-nearest neighbor, random forest, logistic regression, decision tree, django, cloud computing, alibaba cloud

Procedia PDF Downloads 108

Authors: Chalakorn Chitsaart, Suchada Rianmora, Mann Rattana-Areeyagon, Wutichai Namjaiprasert

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G-code is the main factor in computer numerical control (CNC) machine for controlling the tool-paths and generating the profile of the object’s features. For obtaining high surface accuracy of the surface finish, non-stop operation is required for CNC machine. Recently, to design a new product, the strategy that concerns about a change that has low impact on business and does not consume lot of resources has been introduced. Cost and time for designing minor changes can be reduced since the traditional geometric details of the existing models are applied. In order to support this strategy as the alternative channel for machining operation, this research proposes the automatic generating codes for CNC milling operation. Using this technique can assist the manufacturer to easily change the size and the geometric shape of the product during the operation where the time spent for setting up or processing the machine are reduced. The algorithm implemented on MATLAB platform is developed by analyzing and evaluating the geometric information of the part. Codes are created rapidly to control the operations of the machine. Comparing to the codes obtained from CAM, this developed algorithm can shortly generate and simulate the cutting profile of the part.

Keywords: geometric shapes, milling operation, minor changes, CNC Machine, G-code, cutting parameters

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Authors: R. N. Begum, Ambalika Sharma, G. K. Singh

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Identity theft has serious ramifications beyond data and personal information loss. This necessitates the implementation of robust and efficient user identification systems. Therefore, automatic biometric recognition systems are the need of the hour, and ECG-based systems are unquestionably the best choice due to their appealing inherent characteristics. The CNNs are the recent state-of-the-art techniques for ECG-based user identification systems. However, the results obtained are significantly below standards, and the situation worsens as the number of users and types of heartbeats in the dataset grows. As a result, this study proposes a highly accurate and resilient ECG-based person identification system using CNN's dense learning framework. The proposed research explores explicitly the calibre of dense CNNs in the field of ECG-based human recognition. The study tests four different configurations of dense CNN which are trained on a dataset of recordings collected from eight popular ECG databases. With the highest FAR of 0.04 percent and the highest FRR of 5%, the best performing network achieved an identification accuracy of 99.94 percent. The best network is also tested with various train/test split ratios. The findings show that DenseNets are not only extremely reliable but also highly efficient. Thus, they might also be implemented in real-time ECG-based human recognition systems.

Keywords: Biometrics, Dense Networks, Identification Rate, Train/Test split ratio

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Authors: Felix Boehnisch, Alexander Lutz

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Based on the number of users and the amount of content posted daily, Facebook is considered the largest social network in the world. This content includes images or text posts from companies but also private persons, which are also commented on by other users. However, it can sometimes be difficult for companies to keep track of all the posts and the reactions to them, especially when there are several posts a day that contain hundreds to thousands of comments. To facilitate this, the following paper deals with the possible applications of sentiment analysis to social media comments in order to be able to support the work in social media marketing. In a first step, post comments were divided into positive and negative by a subjective rating, then the same comments were checked for their polarity value by the two german python libraries TextBlobDE and SentiWS and also grouped into positive, negative, or even neutral. As a control, the subjective classifications were compared with the machine-generated ones by a confusion matrix, and relevant quality criteria were determined. The accuracy of both libraries was not really meaningful, with 60% to 66%. However, many words or sentences were not evaluated at all, so there seems to be room for optimization to possibly get more accurate results. In future studies, the use of these specific German libraries can be optimized to gain better insights by either applying them to stricter cleaned data or by adding a sentiment value to emojis, which have been removed from the comments in advance, as they are not contained in the libraries.

Keywords: Facebook, German libraries, polarity, sentiment analysis, social media comments

Procedia PDF Downloads 157

Authors: Asma Omri, Iheb Sifaoui, Sofiane Sayahi, Hichem Besbes

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Future vehicle systems demand advanced capabilities, notably in-cabin life detection and driver monitoring systems, with a particular emphasis on drowsiness detection. To meet these requirements, several techniques employ artificial intelligence methods based on real-time vital sign measurements. In parallel, Frequency-Modulated Continuous-Wave (FMCW) radar technology has garnered considerable attention in the domains of healthcare and biomedical engineering for non-invasive vital sign monitoring. FMCW radar offers a multitude of advantages, including its non-intrusive nature, continuous monitoring capacity, and its ability to penetrate through clothing. In this paper, we propose a system utilizing the AWR6843AOP radar from Texas Instruments (TI) to extract precise vital sign information. The radar allows us to estimate Ballistocardiogram (BCG) signals, which capture the mechanical movements of the body, particularly the ballistic forces generated by heartbeats and respiration. These signals are rich sources of information about the cardiac cycle, rendering them suitable for heart rate estimation. The process begins with real-time subject positioning, followed by clutter removal, computation of Doppler phase differences, and the use of various filtering methods to accurately capture subtle physiological movements. To address the challenges associated with FMCW radar-based vital sign monitoring, including motion artifacts due to subjects' movement or radar micro-vibrations, Long Short-Term Memory (LSTM) networks are implemented. LSTM's adaptability to different heart rate patterns and ability to handle real-time data make it suitable for continuous monitoring applications. Several crucial steps were taken, including feature extraction (involving amplitude, time intervals, and signal morphology), sequence modeling, heart rate estimation through the analysis of detected cardiac cycles and their temporal relationships, and performance evaluation using metrics such as Root Mean Square Error (RMSE) and correlation with reference heart rate measurements. For dataset construction and LSTM training, a comprehensive data collection system was established, integrating the AWR6843AOP radar, a Heart Rate Belt, and a smart watch for ground truth measurements. Rigorous synchronization of these devices ensured data accuracy. Twenty participants engaged in various scenarios, encompassing indoor and real-world conditions within a moving vehicle equipped with the radar system. Static and dynamic subject’s conditions were considered. The heart rate estimation through LSTM outperforms traditional signal processing techniques that rely on filtering, Fast Fourier Transform (FFT), and thresholding. It delivers an average accuracy of approximately 91% with an RMSE of 1.01 beat per minute (bpm). In conclusion, this paper underscores the promising potential of FMCW radar technology integrated with artificial intelligence algorithms in the context of automotive applications. This innovation not only enhances road safety but also paves the way for its integration into the automotive ecosystem to improve driver well-being and overall vehicular safety.

Keywords: ballistocardiogram, FMCW Radar, vital sign monitoring, LSTM

Procedia PDF Downloads 50

Authors: E. U. Dampage, Ovindi D. Bandara, Vinushi S. Waraketiya, Samitha S. R. De Silva, Yasiru S. Gunarathne

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The knowledge of water inflow figures is paramount in decision making on the allocation for consumption for numerous purposes; irrigation, hydropower, domestic and industrial usage, and flood control. The understanding of how reservoir inflows are affected by different climatic and hydrological conditions is crucial to enable effective water management and downstream flood control. In this research, we propose a method using a Long Short Term Memory (LSTM) Artificial Neural Network (ANN) to assist the aforesaid decision-making process. The Kotmale reservoir, which is the uppermost reservoir in the Mahaweli reservoir complex in Sri Lanka, was used as the test bed for this research. The ANN uses the runoff in the Kotmale reservoir catchment area and the effect of Sea Surface Temperatures (SST) to make a forecast for seven days ahead. Three types of ANN are tested; Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and LSTM. The extensive field trials and validation endeavors found that the LSTM ANN provides superior performance in the aspects of accuracy and latency.

Keywords: convolutional neural network, CNN, inflow, long short-term memory, LSTM, multi-layer perceptron, MLP, neural network

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Authors: Sara Mahesar, Saleem M. Chandio, Hira Soomro

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Nonlinear system of equations in two variables is a system which contains variables of degree greater or equal to two or that comprises of the transcendental functions. Mathematical modeling of numerous physical problems occurs as a system of nonlinear equations. In applied and pure mathematics it is the main dispute to solve a system of nonlinear equations. Numerical techniques mainly used for finding the solution to problems where analytical methods are failed, which leads to the inexact solutions. To find the exact roots or solutions in case of the system of non-linear equations there does not exist any analytical technique. Various methods have been proposed to solve such systems with an improved rate of convergence and accuracy. In this paper, a new scheme is developed for solving system of non-linear equation in two variables. The iterative scheme proposed here is modified form of the conventional Newton’s Method (CN) whose order of convergence is two whereas the order of convergence of the devised technique is three. Furthermore, the detailed error and convergence analysis of the proposed method is also examined. Additionally, various numerical test problems are compared with the results of its counterpart conventional Newton’s Method (CN) which confirms the theoretic consequences of the proposed method.

Keywords: conventional Newton’s method, modified Newton’s method, order of convergence, system of nonlinear equations

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Authors: Amin Nezarat, Naeime Seifadini

Abstract:

Trip mode selection is a behavioral characteristic of passengers with immense importance for travel demand analysis, transportation planning, and traffic management. Identification of trip mode distribution will allow transportation authorities to adopt appropriate strategies to reduce travel time, traffic and air pollution. The majority of existing trip mode inference models operate based on human selected features and traditional machine learning algorithms. However, human selected features are sensitive to changes in traffic and environmental conditions and susceptible to personal biases, which can make them inefficient. One way to overcome these problems is to use neural networks capable of extracting high-level features from raw input. In this study, the convolutional neural network (CNN) architecture is used to predict the trip mode distribution based on raw GPS trajectory data. The key innovation of this paper is the design of the layout of the input layer of CNN as well as normalization operation, in a way that is not only compatible with the CNN architecture but can also represent the fundamental features of motion including speed, acceleration, jerk, and Bearing rate. The highest prediction accuracy achieved with the proposed configuration for the convolutional neural network with batch normalization is 85.26%.

Keywords: predicting, deep learning, neural network, urban trip

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Authors: Rajai Al-Rousan

Abstract:

This paper presents a proposed analytical model for predicting the shear strength of reinforced concrete beams strengthened with CFRP composites as external reinforcement. The proposed analytical model can predict the shear contribution of CFRP composites of RC beams with an acceptable coefficient of correlation with the tested results. Based on the comparison of the proposed model with the published well-known models (ACI model, Triantafillou model, and Colotti model), the ACI model had a wider range of 0.16 to 10.08 for the ratio between tested and predicted ultimate shears at failure. Also, an acceptable range of 0.27 to 2.78 for the ratio between tested and predicted ultimate shears by the Triantafillou model. Finally, the best prediction (the ratio between the tested and predicted ones) of the ultimate shear capacity is observed by using Colotti model with a range of 0.20 to 1.78. Thus, the contribution of the CFRP composites as external reinforcement can be predicted with high accuracy by using the proposed analytical model.

Keywords: predicting, shear capacity, reinforced concrete, beams, strengthened, externally, CFRP composites

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Authors: Belkacem Soundes, Guezouli Larbi

Abstract:

This paper presents a robust and accurate method for text detection and localization over lecture videos. Frame regions are classified into text or background based on visual feature analysis. However, lecture video shows significant degradation mainly related to acquisition conditions, camera motion and environmental changes resulting in low quality videos. Hence, affecting feature extraction and description efficiency. Moreover, traditional text detection methods cannot be directly applied to lecture videos. Therefore, robust feature extraction methods dedicated to this specific video genre are required for robust and accurate text detection and extraction. Method consists of a three-step process: Slide region detection and segmentation; Feature extraction and non-text filtering. For robust and effective features extraction moment functions are used. Two distinct types of moments are used: orthogonal and non-orthogonal. For orthogonal Zernike Moments, both Pseudo Zernike moments are used, whereas for non-orthogonal ones Hu moments are used. Expressivity and description efficiency are given and discussed. Proposed approach shows that in general, orthogonal moments show high accuracy in comparison to the non-orthogonal one. Pseudo Zernike moments are more effective than Zernike with better computation time.

Keywords: text detection, text localization, lecture videos, pseudo zernike moments

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Authors: Jay N. Vyas, Sachin Daxini

Abstract:

Numerical simulation techniques are widely used now in product development and testing instead of expensive, time-consuming and sometimes dangerous laboratory experiments. Numerous numerical methods are available for performing simulation of physical problems of different engineering fields. Grid based methods, like Finite Element Method, are extensively used in performing various kinds of static, dynamic, structural and non-structural analysis during product development phase. Drawbacks of grid based methods in terms of discontinuous secondary field variable, dealing fracture mechanics and large deformation problems led to development of a relatively a new class of numerical simulation techniques in last few years, which are popular as Meshless methods or Meshfree Methods. Meshless Methods are expected to be more adaptive and flexible than Finite Element Method because domain descretization in Meshless Method requires only nodes. Present paper introduces Meshless Methods and differentiates it with Finite Element Method in terms of following aspects: Shape functions used, role of weight function, techniques to impose essential boundary conditions, integration techniques for discrete system equations, convergence rate, accuracy of solution and computational effort. Capabilities, benefits and limitations of Meshless Methods are discussed and concluded at the end of paper.

Keywords: numerical simulation, Grid-based methods, Finite Element Method, Meshless Methods

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Authors: S. Chami, J. Chauvin, T. Demarest, Stan Ng, M. Straus, W. Jahner

Abstract:

Biometrics tools such as fingerprint and iris are widely used in industry to protect critical assets. However, their vulnerability and lack of robustness raise several worries about the protection of highly critical assets. Biometrics based on Electrocardiographic (ECG) signals is a robust identification tool. However, most of the state-of-the-art techniques have worked on clinical signals, which are of high quality and less noisy, extracted from wearable devices like a smartwatch. In this paper, we are presenting a complete machine learning pipeline that identifies people using ECG extracted from an off-person device. An off-person device is a wearable device that is not used in a medical context such as a smartwatch. In addition, one of the main challenges of ECG biometrics is the variability of the ECG of different persons and different situations. To solve this issue, we proposed two different approaches: per person classifier, and one-for-all classifier. The first approach suggests making binary classifier to distinguish one person from others. The second approach suggests a multi-classifier that distinguishes the selected set of individuals from non-selected individuals (others). The preliminary results, the binary classifier obtained a performance 90% in terms of accuracy within a balanced data. The second approach has reported a log loss of 0.05 as a multi-class score.

Keywords: biometrics, electrocardiographic, machine learning, signals processing

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Authors: Aya Salama

Abstract:

Digital Twin is an emerging research topic that attracted researchers in the last decade. It is used in many fields, such as smart manufacturing and smart healthcare because it saves time and money. It is usually related to other technologies such as Data Mining, Artificial Intelligence, and Machine Learning. However, Human digital twin (HDT), in specific, is still a novel idea that still needs to prove its feasibility. HDT expands the idea of Digital Twin to human beings, which are living beings and different from the inanimate physical entities. The goal of this research was to create a Human digital twin that is responsible for real-time human replies automation by simulating human behavior. For this reason, clustering, supervised classification, topic extraction, and sentiment analysis were studied in this paper. The feasibility of the HDT for personal replies generation on social messaging applications was proved in this work. The overall accuracy of the proposed approach in this paper was 63% which is a very promising result that can open the way for researchers to expand the idea of HDT. This was achieved by using Random Forest for clustering the question data base and matching new questions. K-nearest neighbor was also applied for sentiment analysis.

Keywords: human digital twin, sentiment analysis, topic extraction, supervised machine learning, unsupervised machine learning, classification, clustering

Procedia PDF Downloads 67