Search results for: Radial Basis Functions (RBF) neural networks

Authors: Rahul Saini, Roshan Lal

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The present paper deals with the free axisymmetric vibrations of bi-directional functionally graded circular plates using Mindlin’s plate theory and physical neutral surface. The temperature-dependent, as well as temperature-independent mechanical properties of the plate material, varies in radial and transverse directions. Also, temperature profile for one- and two-dimensional temperature variations has been obtained from the heat conduction equation. A simple computational formulation for the governing differential equation of motion for such a plate model has been derived using Hamilton's principle for the clamped and simply supported plates at the periphery. Employing the generalized differential quadrature method, the corresponding frequency equations have been obtained and solved numerically to retain their lowest three roots as the natural frequencies for the first three modes. The effect of various other parameters such as temperature profile, functionally graded indices, and boundary conditions on the vibration characteristics has been presented. In order to validate the accuracy and efficiency of the method, the results have been compared with those available in the literature.

Keywords: bi-directionally FG, GDQM, Mindlin’s circular plate, neutral axis, vibrations

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Authors: Indu Verma, Santanu Kumar Pal

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Interactions between DNA and adsorbed Poly (L-lysine) (PLL) on liquid crystal (LC) droplets were investigated using polarizing optical microcopy (POM) and epi-fluorescence microscopy. Earlier, we demonstrated that adsorption of PLL to the LC/aqueous interface resulted in homeotropic orientation of the LC and thus exhibited a radial configuration of the LC confined within the droplets. Subsequent adsorption of DNA (single stranded DNA/double stranded DNA) at PLL coated LC droplets was found to trigger a LC reorientation within the droplets leading to pre-radial/bipolar configuration of those droplets. To our surprise, subsequent exposure of complementary ssDNA (c-ssDNA) to ssDNA/ adsorbed PLL modified LC droplets did not cause the LC reorientation. This is likely due to the formation of polyplexes (DNA-PLL complex) as confirmed by fluorescence microscopy and atomic force microscopy. In addition, dsDNA adsorbed PLL droplets have been found to be effectively used to displace (controlled release) propidium iodide (a model drug) encapsulated within dsDNA over time. These observations suggest the potential for a label free droplet based LC detection system that can respond to DNA and may provide a simple method to develop DNA-based drug nano-carriers.

Keywords: DNA biosensor, drug delivery, interfaces, liquid crystal droplets

Procedia PDF Downloads 298

Authors: Mahmoud Ahmed Farrag, Ali Zain Elabdeen Heikal, Mohamed Shawky Ahmed, Ahmed Osama Amer

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Crowd-sourced data refers to data that is collected and shared by a large number of individuals or organizations, often through the use of digital technologies such as mobile devices and social media. The shortage in crash data collection in developing countries makes it difficult to fully understand and address road safety issues in these regions. In developing countries, crowd-sourced data can be a valuable tool for improving road safety, particularly in urban areas where the majority of road crashes occur. This study is -to our best knowledge- the first to develop safety performance functions using crowd-sourced data by adopting a negative binomial structure model and the Full Bayes model to investigate traffic safety for urban road networks and provide insights into the impact of roadway characteristics. Furthermore, as a part of the safety management process, network screening has been undergone through applying two different methods to rank the most hazardous road segments: PCR method (adopted in the Highway Capacity Manual HCM) as well as a graphical method using GIS tools to compare and validate. Lastly, recommendations were suggested for policymakers to ensure safer roads.

Keywords: crowdsourced data, road crashes, safety performance functions, Full Bayes models, network screening

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Authors: Iwan Cony Setiadi, Aulia M. T. Nasution

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The multispectral imaging (MSI) technique has been used for skin analysis, especially for distant mapping of in-vivo skin chromophores by analyzing spectral data at each reflected image pixel. For ergonomic purpose, our multispectral imaging system is decomposed in two parts: a light source compartment based on LED with 11 different wavelenghts and a monochromatic 8-Bit CCD camera with C-Mount Objective Lens. The software based on GUI MATLAB to control the system was also developed. Our system provides 11 monoband images and is coupled with a software reconstructing hyperspectral cubes from these multispectral images. In this paper, we proposed a new method to build a hyperspectral reflectance cube based on artificial neural network algorithm. After preliminary corrections, a neural network is trained using the 32 natural color from X-Rite Color Checker Passport. The learning procedure involves acquisition, by a spectrophotometer. This neural network is then used to retrieve a megapixel multispectral cube between 380 and 880 nm with a 5 nm resolution from a low-spectral-resolution multispectral acquisition. As hyperspectral cubes contain spectra for each pixel; comparison should be done between the theoretical values from the spectrophotometer and the reconstructed spectrum. To evaluate the performance of reconstruction, we used the Goodness of Fit Coefficient (GFC) and Root Mean Squared Error (RMSE). To validate reconstruction, the set of 8 colour patches reconstructed by our MSI system and the one recorded by the spectrophotometer were compared. The average GFC was 0.9990 (standard deviation = 0.0010) and the average RMSE is 0.2167 (standard deviation = 0.064).

Keywords: multispectral imaging, reflectance cube, spectral reconstruction, artificial neural network

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Authors: Fadzil Ahmad, Noor Ashidi Mat Isa

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This study introduces an improved genetic algorithm procedure that focuses search around near optimal solution corresponded to a group of elite chromosome. This is achieved through a novel crossover technique known as Segmented Multi Chromosome Crossover. It preserves the highly important information contained in a gene segment of elite chromosome and allows an offspring to carry information from gene segment of multiple chromosomes. In this way the algorithm has better possibility to effectively explore the solution space. The improved GA is applied for the automatic and simultaneous parameter optimization and feature selection of artificial neural network in pattern recognition of medical problem, the cancer and diabetes disease. The experimental result shows that the average classification accuracy of the cancer and diabetes dataset has improved by 0.1% and 0.3% respectively using the new algorithm.

Keywords: genetic algorithm, artificial neural network, pattern clasification, classification accuracy

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Authors: Yazhou Zhou, Yong Huang, Guoqin Ge

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In the process of human social development, the coupling and coordinated development among the ecological environment(E), production(P), and living functions(L) is of great significance for sustainable development. This study uses an improved coupling coordination degree model (CCDM) to discover the coordination conflict between E and human settlement environment. The main work of this study is as follows: (1) It is found that in the process of urbanization development of Ya 'an city from 2014 to 2018, the degree of coupling (DOC) value between E, P, and L is high, but the coupling coordination degree (CCD) of the three is low, especially the DOC value of E and the other two has the biggest decline. (2) A more objective weight value is obtained, which can avoid the analysis error caused by subjective judgment weight value.

Keywords: ecological environment, coupling coordination degree, neural network, sustainable development

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Authors: Nada Mouchfiq, Ahmed Habbani, Chaimae Benjbara

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The security aspect of the IoT occupies a place of great importance especially after the evolution that has known this field lastly because it must take into account the transformations and the new applications .Blockchain is a new technology dedicated to the data sharing. However, this does not work the same way in the different systems with different operating principles. This article will discuss network security using the Blockchain to facilitate the sending of messages and information, enabling the use of new processes and enabling autonomous coordination of devices. To do this, we will discuss proposed solutions to ensure a high level of security in these networks in the work of other researchers. Finally, our article will propose a method of security more adapted to our needs as a team working in the ad hoc networks, this method is based on the principle of the Blockchain and that we named ”MPR Blockchain”.

Keywords: Ad hocs networks, blockchain, MPR, security

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Authors: Gautam Kumar Saharia, Sagardeep Talukdar, Riki Dutta, Sudipta Nandy

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The physics informed neural network (PINN) method opens up an approach for numerically solving nonlinear partial differential equations leveraging fast calculating speed and high precession of modern computing systems. We construct the PINN based on strong universal approximation theorem and apply the initial-boundary value data and residual collocation points to weekly impose initial and boundary condition to the neural network and choose the optimization algorithms adaptive moment estimation (ADAM) and Limited-memory Broyden-Fletcher-Golfard-Shanno (L-BFGS) algorithm to optimize learnable parameter of the neural network. Next, we improve the PINN with a weighted loss function to obtain both the bright and dark soliton solutions of Fokas-Lenells equation (FLE). We find the proposed scheme of adjustable weight coefficients into PINN has a better convergence rate and generalizability than the basic PINN algorithm. We believe that the PINN approach to solve the partial differential equation appearing in nonlinear optics would be useful to study various optical phenomena.

Keywords: deep learning, optical Soliton, neural network, partial differential equation

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Authors: Abderrahim Elmoataz

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More and more contemporary applications involve data in the form of functions defined on irregular and topologically complicated domains (images, meshs, points clouds, networks, etc). Such data are not organized as familiar digital signals and images sampled on regular lattices. However, they can be conveniently represented as graphs where each vertex represents measured data and each edge represents a relationship (connectivity or certain affinities or interaction) between two vertices. Processing and analyzing these types of data is a major challenge for both image and machine learning communities. Hence, it is very important to transfer to graphs and networks many of the mathematical tools which were initially developed on usual Euclidean spaces and proven to be efficient for many inverse problems and applications dealing with usual image and signal domains. Historically, the main tools for the study of graphs or networks come from combinatorial and graph theory. In recent years there has been an increasing interest in the investigation of one of the major mathematical tools for signal and image analysis, which are Partial Differential Equations (PDEs) variational methods on graphs. The normalized p-laplacian operator has been recently introduced to model a stochastic game called tug-of-war-game with noise. Part interest of this class of operators arises from the fact that it includes, as particular case, the infinity Laplacian, the mean curvature operator and the traditionnal Laplacian operators which was extensiveley used to models and to solve problems in image processing. The purpose of this paper is to introduce and to study a new class of normalized p-Laplacian on graphs. The introduction is based on the extension of p-harmonious function introduced in as discrete approximation for both infinity Laplacian and p-Laplacian equations. Finally, we propose to use these operators as a framework for solving many inverse problems in image processing.

Keywords: normalized p-laplacian, image processing, stochastic game, inverse problems

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Authors: Mayank Raj, V. G. Narendra

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Ever since the DARPA challenge on autonomous vehicles in 2005, there has been a lot of buzz about ‘Autonomous Vehicles’ amongst the major tech giants such as Google, Uber, and Tesla. Numerous approaches have been adopted to solve this problem, which can have a long-lasting impact on mankind. In this paper, we have used Deep Learning techniques and TensorFlow framework with the goal of building a neural network model to predict (speed, acceleration, steering angle, and brake) features needed for navigation of autonomous vehicles. The Deep Neural Network has been trained on images and sensor data obtained from the comma.ai dataset. A heatmap was used to check for correlation among the features, and finally, four important features were selected. This was a multivariate regression problem. The final model had five convolutional layers, followed by five dense layers. Finally, the calculated values were tested against the labeled data, where the mean squared error was used as a performance metric.

Keywords: autonomous vehicles, deep learning, computer vision, artificial intelligence

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Authors: Fadwa Haraka, Ahmad Elouatouati, Mourad Taha Janan

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In this study, we propose a neural network based method in order to calculate the heat source temperature of isotropic plate with lower surface forced cooling. To validate the proposed model, the heat source temperatures values will be compared to the analytical method -variables separation- and finite element model. The mathematical simulation is done through 3D numerical simulation by COMSOL software considering three different materials: Aluminum, Copper, and Graphite. The proposed method will lead to a formulation of the heat source temperature based on the thermal and geometric properties of the base plate.

Keywords: thermal model, thermal resistance, finite element simulation, neural network

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Authors: Erick C. Valverde, Wansu Lim

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Facial emotion recognition (FER) system has been recently developed for more advanced computer vision applications. The ability to identify human emotions would enable smart healthcare facility to diagnose mental health illnesses (e.g., depression and stress) as well as better human social interactions with smart technologies. The FER system involves two steps: 1) face detection task and 2) facial emotion recognition task. It classifies the human expression in various categories such as angry, disgust, fear, happy, sad, surprise, and neutral. This system requires intensive research to address issues with human diversity, various unique human expressions, and variety of human facial features due to age differences. These issues generally affect the ability of the FER system to detect human emotions with high accuracy. Early stage of FER systems used simple supervised classification task algorithms like K-nearest neighbors (KNN) and artificial neural networks (ANN). These conventional FER systems have issues with low accuracy due to its inefficiency to extract significant features of several human emotions. To increase the accuracy of FER systems, deep learning (DL)-based methods, like convolutional neural networks (CNN), are proposed. These methods can find more complex features in the human face by means of the deeper connections within its architectures. However, the inference speed and computational costs of a DL-based FER system is often disregarded in exchange for higher accuracy results. To cope with this drawback, an optimized DL-based FER system is proposed in this study.An extreme version of Inception V3, known as Xception model, is leveraged by applying different network optimization methods. Specifically, network pruning and quantization are used to enable lower computational costs and reduce memory usage, respectively. To support low resource requirements, a 68-landmark face detector from Dlib is used in the early step of the FER system.Furthermore, a DL compiler is utilized to incorporate advanced optimization techniques to the Xception model to improve the inference speed of the FER system. In comparison to VGG-Net and ResNet50, the proposed optimized DL-based FER system experimentally demonstrates the objectives of the network optimization methods used. As a result, the proposed approach can be used to create an efficient and real-time FER system.

Keywords: deep learning, face detection, facial emotion recognition, network optimization methods

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Authors: Fuyuan Ma, Yuhan Wang, Junhe Zhang, Ying Wang

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Identifying the sources of information dissemination is a pivotal task in the study of collective behaviors in networks, enabling us to discern and intercept the critical pathways through which information propagates from its origins. This allows for the control of the information’s dissemination impact in its early stages. Numerous methods for source detection rely on pre-existing, underlying propagation models as prior knowledge. Current models that eschew prior knowledge attempt to harness label propagation algorithms to model the statistical characteristics of propagation states or employ Graph Neural Networks (GNNs) for deep reverse modeling of the diffusion process. These approaches are either deficient in modeling the propagation patterns of information or are constrained by the over-smoothing problem inherent in GNNs, which limits the stacking of sufficient model depth to excavate global propagation patterns. Consequently, we introduce the ODESI model. Initially, the model employs a label propagation algorithm to delineate the distribution density of infected states within a graph structure and extends the representation of infected states from integers to state vectors, which serve as the initial states of nodes. Subsequently, the model constructs a deep architecture based on GNNs-coupled Ordinary Differential Equations (ODEs) to model the global propagation patterns of continuous propagation processes. Addressing the challenges associated with solving ODEs on graphs, we approximate the analytical solutions to reduce computational costs. Finally, we conduct simulation experiments on two real-world social network datasets, and the results affirm the efficacy of our proposed ODESI model in source identification tasks.

Keywords: source identification, ordinary differential equations, label propagation, complex networks

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Authors: Yuhua Yang, Yingcheng Li

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As a new form of intercity economic cooperation, innovation enclaves have received much attention from governments and scholars in China, which are of great significance in promoting the flow of innovation elements and advancing regional integration. Utilizing inter-city linkages of innovation enclaves within and beyond the Yangtze River Delta Region, we construct multi-scalar innovation enclave networks in 2018 and 2022, and analyze the evolutionary characteristics and underlying mechanisms of the networks. Overall, we find that: (1) The intercity innovation enclave networks have the characteristics of preferential connection and are gradually forming a clear multi-scale and hierarchical structure, with Shanghai, Hangzhou and Nanjing as the core and other cities as the general nodes; (2) The intercity innovation enclave networks exhibit local clustering dominated by geographical proximity connections, and are becoming more noticeable in the effect of distance decay and functionally polycentric as the spatial scale decreases; (3) The intercity innovation enclave networks are influenced by both functional distance and multidimensional proximity. While the innovation potential differences caused by urban attributes internally drive the formation of innovation enclave cooperation, geographic proximity, technological proximity and institutional proximity externally affect the selection of cooperation partners.

Keywords: economic enclave, intercity cooperation, proximity, yangtze river delta region

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Authors: Shyam Sajan, Kamal Bijlani

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The widespread effects of digital media help people to explore the world more and get entertained with no effort. People became fond of these kind of sedentary life style. The increase in sedentary time and a decrease in physical activities has negative impacts on human health. Even though the addiction to video games has been exploited in exergames, to make people exercise and enjoy game challenges, the contribution is restricted only to physical wellness. This paper proposes creation and implementation of a game with the help of digital media in a virtual environment. The game is designed by collaborating ideas from neural linguistic programming and Stroop effect that can also be used to identify a person’s mental state, to improve concentration and to eliminate various phobias. The multiplayer game is played in a virtual environment created with Kinect sensor, to make the game more motivating and interactive.

Keywords: exergaming, Kinect Sensor, Neural Linguistic Programming, Stroop Effect

Procedia PDF Downloads 436

Authors: Ahmed Kayad Abdourazak, Abderafi Souad, Zejli Driss, Idriss Abdoulkader Ibrahim

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In this paper Artificial Neural Network (ANN) is used to predict the solar irradiation in Djibouti for the first Time that is useful to the integration of Concentrating Solar Power (CSP) and sites selections for new or future solar plants as part of solar energy development. An ANN algorithm was developed to establish a forward/reverse correspondence between the latitude, longitude, altitude and monthly solar irradiation. For this purpose the German Aerospace Centre (DLR) data of eight Djibouti sites were used as training and testing in a standard three layers network with the back propagation algorithm of Lavenber-Marquardt. Results have shown a very good agreement for the solar irradiation prediction in Djibouti and proves that the proposed approach can be well used as an efficient tool for prediction of solar irradiation by providing so helpful information concerning sites selection, design and planning of solar plants.

Keywords: artificial neural network, solar irradiation, concentrated solar power, Lavenberg-Marquardt

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Authors: Anshu Radha Aggarwal

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As per the Right to Education Act (RTE), 2009, every child in the age group of 6-14 years shall be admitted in a neighborhood school. All the Out of School Children identified have to be enrolled / mainstreamed in to age appropriate class and there-after be provided special training. This paper addresses issues emerging from provisions in the RTE Act that specifically refer to the enrolment of out-of school children into age appropriate classes and the requirement to provide special trainings that will enable this to take place. In the context of RTE Act, the Out-of-School Children are first enrolled in the formal school and then they are provided with Special Training through NRSTCs (Long Term / Short term basis). These centers are functioning in formal school campus itself. This paper specifies the role of special training centers (STC). It presents a re-envisioning of assessment that recognizes two principal functions of assessment, assessment for learning and assessment of learning, instead of the more familiar categories of formative, diagnostic, summative, and evaluative assessment. The use of these two functions of assessment highlights and emphasizes the role of special training centers (STC) to assess their level for giving them appropriate special training and to evaluate their improvement in learning level. Challenge of problem faced by teachers to do diagnostic assessment, including its place in the sequence of assessment procedures appropriate in identifying and addressing individual children’s learning difficulties are solved by special training centers (STC). It is important that assessment is used to identify children with learning difficulties at the earliest possible stage so that appropriate support and intervention can be put in place. So appropriate challenges with tools are presented here for their assessment at entry level and at completion level of primary children by special training centers (STC).

Keywords: right to education, assessment, challenges, out of school children

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Authors: Babak Forouraghi

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A genetic circuit is a collection of interacting genes and proteins that enable individual cells to implement and perform vital biological functions such as cell division, growth, death, and signaling. In cell engineering, synthetic gene circuits are engineered networks of genes specifically designed to implement functionalities that are not evolved by nature. These engineered networks enable scientists to tackle complex problems such as engineering cells to produce therapeutics within the patient's body, altering T cells to target cancer-related antigens for treatment, improving antibody production using engineered cells, tissue engineering, and production of genetically modified plants and livestock. Construction of computational models to realize genetic circuits is an especially challenging task since it requires the discovery of the flow of genetic information in complex biological systems. Building synthetic biological models is also a time-consuming process with relatively low prediction accuracy for highly complex genetic circuits. The primary goal of this study was to investigate the utility of a pre-trained bidirectional encoder transformer that can accurately predict gene expressions in genetic circuit designs. The main reason behind using transformers is their innate ability (attention mechanism) to take account of the semantic context present in long DNA chains that are heavily dependent on the spatial representation of their constituent genes. Previous approaches to gene circuit design, such as CNN and RNN architectures, are unable to capture semantic dependencies in long contexts, as required in most real-world applications of synthetic biology. For instance, RNN models (LSTM, GRU), although able to learn long-term dependencies, greatly suffer from vanishing gradient and low-efficiency problem when they sequentially process past states and compresses contextual information into a bottleneck with long input sequences. In other words, these architectures are not equipped with the necessary attention mechanisms to follow a long chain of genes with thousands of tokens. To address the above-mentioned limitations, a transformer model was built in this work as a variation to the existing DNA Bidirectional Encoder Representations from Transformers (DNABERT) model. It is shown that the proposed transformer is capable of capturing contextual information from long input sequences with an attention mechanism. In previous works on genetic circuit design, the traditional approaches to classification and regression, such as Random Forrest, Support Vector Machine, and Artificial Neural Networks, were able to achieve reasonably high R2 accuracy levels of 0.95 to 0.97. However, the transformer model utilized in this work, with its attention-based mechanism, was able to achieve a perfect accuracy level of 100%. Further, it is demonstrated that the efficiency of the transformer-based gene expression classifier is not dependent on the presence of large amounts of training examples, which may be difficult to compile in many real-world gene circuit designs.

Keywords: machine learning, classification and regression, gene circuit design, bidirectional transformers

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Authors: Mario Isaza-Ruget, Claudia C. Colmenares-Mejia, Nancy Yomayusa, Camilo A. González, Andres Cely, Jossie Murcia

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Quantification of risks associated with complications development from chronic kidney disease (CKD) through accurate survival models can help with patient management. A retrospective cohort that included patients diagnosed with CKD from a primary care program and followed up between 2013 and 2018 was carried out. Time-dependent and static covariates associated with demographic, clinical, and laboratory factors were included. Deep Learning (DL) survival analyzes were developed for three CKD outcomes: CKD stage progression, >25% decrease in Estimated Glomerular Filtration Rate (eGFR), and Renal Replacement Therapy (RRT). Models were evaluated and compared with Random Survival Forest (RSF) based on concordance index (C-index) metric. 2.143 patients were included. Two models were developed for each outcome, Deep Neural Network (DNN) model reported C-index=0.9867 for CKD stage progression; C-index=0.9905 for reduction in eGFR; C-index=0.9867 for RRT. Regarding the RSF model, C-index=0.6650 was reached for CKD stage progression; decreased eGFR C-index=0.6759; RRT C-index=0.8926. DNN models applied in survival analysis context with considerations of longitudinal covariates at the start of follow-up can predict renal stage progression, a significant decrease in eGFR and RRT. The success of these survival models lies in the appropriate definition of survival times and the analysis of covariates, especially those that vary over time.

Keywords: artificial intelligence, chronic kidney disease, deep neural networks, survival analysis

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Authors: Hafdaoui Hichem, Mehadjebia Cherifa, Benatia Djamel

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In this paper, we propose a new method for Bulk detection of an acoustic microwave signal during the propagation of acoustic microwaves in a piezoelectric substrate (Lithium Niobate LiNbO3). We have used the classification by probabilistic neural network (PNN) as a means of numerical analysis in which we classify all the values of the real part and the imaginary part of the coefficient attenuation with the acoustic velocity in order to build a model from which we note the Bulk waves easily. These singularities inform us of presence of Bulk waves in piezoelectric materials. By which we obtain accurate values for each of the coefficient attenuation and acoustic velocity for Bulk waves. This study will be very interesting in modeling and realization of acoustic microwaves devices (ultrasound) based on the propagation of acoustic microwaves.

Keywords: piezoelectric material, probabilistic neural network (PNN), classification, acoustic microwaves, bulk waves, the attenuation coefficient

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Authors: Kostas Kouvaris, Anastasia Eleftheriou, Georgios A. Sarantitis, Apostolos Chondronasios

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To achieve the objective of almost zero carbon energy solutions by 2050, the EU needs to accelerate the development of integrated, highly efficient and environmentally friendly solutions. In this direction, district heating and cooling (DHC) emerges as a viable and more efficient alternative to conventional, decentralized heating and cooling systems, enabling a combination of more efficient renewable and competitive energy supplies. In this paper, we develop a forecasting tool for near real-time local weather and thermal energy demand predictions for an entire DHC network. In this fashion, we are able to extend the functionality and to improve the energy efficiency of the DHC network by predicting and adjusting the heat load that is distributed from the heat generation plant to the connected buildings by the heat pipe network. Two case-studies are considered; one for Vransko, Slovenia and one for Montpellier, France. The data consists of i) local weather data, such as humidity, temperature, and precipitation, ii) weather forecast data, such as the outdoor temperature and iii) DHC operational parameters, such as the mass flow rate, supply and return temperature. The external temperature is found to be the most important energy-related variable for space conditioning, and thus it is used as an external parameter for the energy demand models. For the development of the forecasting tool, we use state-of-the-art deep neural networks and more specifically, recurrent networks with long-short-term memory cells, which are able to capture complex non-linear relations among temporal variables. Firstly, we develop models to forecast outdoor temperatures for the next 24 hours using local weather data for each case-study. Subsequently, we develop models to forecast thermal demand for the same period, taking under consideration past energy demand values as well as the predicted temperature values from the weather forecasting models. The contributions to the scientific and industrial community are three-fold, and the empirical results are highly encouraging. First, we are able to predict future thermal demand levels for the two locations under consideration with minimal errors. Second, we examine the impact of the outdoor temperature on the predictive ability of the models and how the accuracy of the energy demand forecasts decreases with the forecast horizon. Third, we extend the relevant literature with a new dataset of thermal demand and examine the performance and applicability of machine learning techniques to solve real-world problems. Overall, the solution proposed in this paper is in accordance with EU targets, providing an automated smart energy management system, decreasing human errors and reducing excessive energy production.

Keywords: machine learning, LSTMs, district heating and cooling system, thermal demand

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Authors: Noga Bregman

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

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

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Authors: Yitao Lei, Xingxiang Zhai, Burra Venkata Durga Kumar

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As the quantity and complexity of computing in large-scale software systems increase, distributed system computing becomes increasingly important. The distributed system realizes high-performance computing by collaboration between different computing resources. If there are no efficient resource scheduling resources, the abuse of distributed computing may cause resource waste and high costs. However, resource scheduling is usually an NP-hard problem, so we cannot find a general solution. However, some optimization algorithms exist like genetic algorithm, ant colony optimization, etc. The large scale of distributed systems makes this traditional optimization algorithm challenging to work with. Heuristic and machine learning algorithms are usually applied in this situation to ease the computing load. As a result, we do a review of traditional resource scheduling optimization algorithms and try to introduce a deep reinforcement learning method that utilizes the perceptual ability of neural networks and the decision-making ability of reinforcement learning. Using the machine learning method, we try to find important factors that influence the performance of distributed system computing and help the distributed system do an efficient computing resource scheduling. This paper surveys the application of deep reinforcement learning on distributed system computing resource scheduling proposes a deep reinforcement learning method that uses a recurrent neural network to optimize the resource scheduling, and proposes the challenges and improvement directions for DRL-based resource scheduling algorithms.

Keywords: resource scheduling, deep reinforcement learning, distributed system, artificial intelligence

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Authors: Yi-Fei Yang, Nai-Bao He, Shao-Bang Xing

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This study investigates the permanent magnet synchronous linear motor (PMSLM) chaotic motion under the specific physical parameters, the stability and the security of motor-driven system will be unavoidably influenced. Therefore, it is really necessary to investigate the methods of controlling or suppressing chaos in PMSLM. Firstly, we derive a chaotic model of PMSLM in the closed-loop system. Secondly, in order to realize the local asymptotic stabilization of the mechanical subsystem and the global stabilization of the motor-driven system including electrical subsystem, we propose an improved uniting control lyapunov functions by introducing backstepping approach. Finally, an illustrated example is also given to show the electiveness of the obtained results.

Keywords: linear motor, lyapunov functions, chao control, hybrid controller

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Authors: Marlene Perez Villalpando, Kelly Joel Gurubel Tun

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In this paper, we propose an optimal control strategy for a hydroelectric power plant subject to input disturbances like meteorological phenomena. The engineering characteristics of the system are described by a nonlinear model. The random availability of renewable sources is predicted by a high-order neural network trained with an extended Kalman filter, whereas the power generation is regulated by the optimal control law. The main advantage of the system is the stabilization of the amount of power generated in the plant. A control supervisor maintains stability and availability in hydropower reservoirs water levels for power generation. The proposed approach demonstrated a good performance to stabilize the reservoir level and the power generation along their desired trajectories in the presence of disturbances.

Keywords: hydropower, high order neural network, Kalman filter, optimal control

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Authors: Tim Steinhaus, Christian Beidl

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Urban air quality and climate protection are two major challenges for future mobility systems. Despite the steady reduction of pollutant emissions from vehicles over past decades, local immission load within cities partially still reaches heights, which are considered hazardous to human health. Although traffic-related emissions account for a major part of the overall urban pollution, modeling the exact interaction remains challenging. In this paper, a novel approach for the determination of the emission-immission interaction on the basis of neural network modeling for traffic induced NO2-immission load within a near-traffic hot-spot scenario is presented. In a detailed sensitivity analysis, the significance of relevant influencing variables on the prevailing NO2 concentration is initially analyzed. Based on this, the generation process of the model is described, in which not only environmental influences but also the vehicle fleet composition including its associated segment- and certification-specific real driving emission factors are derived and used as input quantities. The validity of this approach, which has been presented in the past, is re-examined in this paper using updated data on vehicle emissions and recent immission measurement data. Within the framework of a final scenario analysis, the future development of the immission load is forecast for different developments in the vehicle fleet composition. It is shown that immission levels of less than half of today’s yearly average limit values are technically feasible in hot-spot situations.

Keywords: air quality, emission, emission-immission-interaction, immission, NO2, zero impact

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Authors: Tayebeh Jameh-Bozorgi, M. Soleymani

Abstract:

In this research, the role of social networks in promoting ethics in Iranian sports was investigated. The research adopted a descriptive-analytic method, and the survey’s population consisted of all the athletes invited to the national football, volleyball, wrestling and taekwondo teams. Considering the limited population, the size of the society was considered as the sample size. After the distribution of the questionnaires, 167 respondents answered the questionnaires correctly. The data collection tool was chosen according to Hamid Ghasemi`s, standard questionnaire for social networking and mass media, which has 28 questions. Reliability of the questionnaire was calculated using Cronbach's alpha coefficient (94%). The content validity of the questionnaire was also approved by the professors. In this study, descriptive statistics and inferential statistical methods were used to analyze the data using statistical software. The benchmark tests used in this research included the following: Binomial test, Friedman test, Spearman correlation coefficient, Vermont Creamers, Good fit test and comparative prototypes. The results showed that athletes believed that social network has a significant role in promoting sport ethics in the community. Telegram has been known to play a big role than other social networks. Moreover, the respondents' view on the role of social networks in promoting sport ethics was significantly different in both men and women groups. In fact, women had a more positive attitude towards the role of social networks in promoting sport ethics than men. The respondents' view of the role of social networks in promoting the ethics of sports in the study groups also had a significant difference. Additionally, there was a significant and reverse relationship between the sports experience and the attitude of national athletes regarding the role of social networks in promoting ethics in sports.

Keywords: ethics, social networks, mass media, Iranian sports, internet

Procedia PDF Downloads 288

Authors: Laidi Maamar, Hanini Salah

Abstract:

The present work investigates the potential of artificial neural network (ANN) model to predict the horizontal global solar radiation (HGSR). The ANN is developed and optimized using three years meteorological database from 2011 to 2013 available at the meteorological station of Blida (Blida 1 university, Algeria, Latitude 36.5°, Longitude 2.81° and 163 m above mean sea level). Optimal configuration of the ANN model has been determined by minimizing the Root Means Square Error (RMSE) and maximizing the correlation coefficient (R2) between observed and predicted data with the ANN model. To select the best ANN architecture, we have conducted several tests by using different combinations of parameters. A two-layer ANN model with six hidden neurons has been found as an optimal topology with (RMSE=4.036 W/m²) and (R²=0.999). A graphical user interface (GUI), was designed based on the best network structure and training algorithm, to enhance the users’ friendliness application of the model.

Keywords: artificial neural network, global solar radiation, solar energy, prediction, Algeria

Procedia PDF Downloads 499

Authors: Lara Góes Da Costa

Abstract:

This paper analyzes the social dimension of the empowerment of women in Brazil, following the principles of human development of the UN WOMEN, in particular the sixth principle, which establishes the promotion of gender equality through social policy initiatives and activism in general aimed at community. In Brazil, women's empowerment has taken social networks through the creation of avatars and pages of dissemination and promotion of gender equality, as well as denunciations and educational posts such as 'Observe Gender', 'Empower Two Women', 'Black Intellectual Women', among others. At the same time, women's social inclusion bills in various sectors are trailing in the legislative apparatus, with little or no relation to the current discussion of gender diversity and intersectionality. In this sense, this article establishes an analytical parallel between the media manifestations of social networks and the social distance of the representatives of the legislative power. This parallelly shows the political failing to meet the social demands of inclusion, as to multiply the creation of laws and the effectiveness of the principle of promoting gender equality.

Keywords: gender, rights, justice, social networks

Procedia PDF Downloads 394

Authors: Vedant Vardhan Rathour, Anant Agrawal

Abstract:

The project aims to provide a touchless, intuitive interface for human-computer interaction, enabling users to control their computer using hand gestures and voice commands. The system leverages advanced computer vision techniques using the MediaPipe framework and OpenCV to detect and interpret real time hand gestures, transforming them into mouse actions such as clicking, dragging, and scrolling. Additionally, the integration of a voice assistant powered by the Speech Recognition library allows for seamless execution of tasks like web searches, location navigation and gesture control on the system through voice commands.

Keywords: gesture recognition, hand tracking, machine learning, convolutional neural networks

Procedia PDF Downloads 12