Search results for: neural paths

Authors: P. W. Tsai, W. L. Hong, C. W. Chen, C. Y. Chen

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In this paper, we present a neural network (NN) based approach represent a nonlinear Tagagi-Sugeno (T-S) system. A linear differential inclusion (LDI) state-space representation is utilized to deal with the NN models. Taking advantage of the LDI representation, the stability conditions and controller design are derived for a class of nonlinear structural systems. Moreover, the concept of utilizing the Parallel Particle Swarm Optimization (PPSO) algorithm to solve the common P matrix under the stability criteria is given in this paper.

Keywords: Lyapunov stability, parallel particle swarm optimization, linear differential inclusion, artificial intelligence

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Authors: Choo Hong Ang

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The most popular approach to save energy for large commercial buildings in Malaysia is to replace the existing chiller plant of high kW/ton to one of lower kW/ton. This approach, however, entails large capital outlay with a long payment period of up to 7 years. This paper shows that by using multiple approaches, other than replacing the existing chiller plant, an energy saving of up to 20 %, is possible. The main methodology adopted was to identify and then plugged all heat ingress paths into a building, including putting up glass structures to prevent mixing of internal air-conditioned air with the ambient environment, and replacing air curtains with glass doors. This methodology could save up to 10 % energy bill. Another methodology was to change fixed speed motors of air handling units (AHU) to variable speed drive (VSD) and changing escalators to motion-sensor type. Other methodologies included reducing heat load by blocking air supply to non-occupied parcels, rescheduling chiller plant operation, changing of fluorescent lights to LED lights, and conversion from tariff B to C1. A case example of Komtar, the tallest building in Penang, is given here. The total energy bill for Komtar was USD2,303,341 in 2016 but was reduced to USD 1,842,927.39 in 2018, a significant saving of USD460,413.86 or 20 %. In terms of kWh, there was a reduction from 18, 302,204.00 kWh in 2016 to 14,877,105.00 kWh in 2018, a reduction of 3,425,099.00 kWh or 18.71 %. These methodologies used were relatively low cost and the payback period was merely 24 months. With this achievement, the Komtar building was awarded champion of the Malaysian National Energy Award 2019 and second runner up of the Asean Energy Award. This experience shows that a strong commitment to energy saving is the key to effective energy saving.

Keywords: chiller plant, energy saving measures, heat ingress, large building

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Authors: Gokcen Akyurek, Hulya Kayihan, Deniz Yuce, Selen Yilmaz

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The stigma towards mental illness is still very rooted in our society, despite the number of studies, campaigns, and anti-stigma programs developed in recent years. Stigma represents a serious obstacle to recovery and social integration for people who experience a mental illness, affecting directly their well-being and quality of life. It implies that these persons have to deal with many other barriers apart from the disease symptoms (1-5). Convergent, recent literature suggests that less positive attitudes by mental health professionals interfere with the self-determination and recovery process (4-10).The aim of this study was to translate the Attribution Questionnaire-27 (AQ-27) to the Turkish language (AQ-27-T), and to examine the reliability and validity of this new Turkish version. Cultural adaptation was implemented according to the internationally suggested method. To determine the understandability and appropriateness of this measure for the Turkish culture, a pretest was administered and the final form was generated. Then, 424 randomly chosen people took part in the study. Participant’s mean age was 36.9±12.7 years and %52 of them female. Cronbach's alpha and intra-class coefficients were used to estimate instrument reliability. The AQ-27-T was assessed again 14 days later for test retest reliability. The AQ-27-T demonstrated acceptable internal consistency, with a Cronbach's alpha of 0.88 for the total scale and ranging between 0.86 and 0.89 for the items. The test-retest reliability was good, with Pearson correlation coefficients of 0.79 for the total scale and ranging between 0.35 and 0.77 for the items (p<0.05). Correlation between subscales was moderate-good, with Pearson correlation coefficients of 0.18-0.88 (p<0.05). Fit indices of the model supported the factor structure and paths. The AQ-27-T is a reliable measure to assess stigmatizing attitudes in Turkish.

Keywords: attribution questionnaire, validity, reliability, stigma

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Authors: Andrew Gryguć, Seyed Behzad Behravesh, Hamid Jahed, Mary Wells, Wojciech Macek, Bruce Williams

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This extended abstract investigates the influence of the multiaxial loading on the fatigue behavior of forged magnesium through quantitative analysis of its fracture surface topography and mesoscopic cracking orientation. Fatigue tests were performed on hollow tubular sample geometries extracted from closed-die forged AZ80 Mg components, with three different multiaxial strain paths (axial/shear), proportional, 45° out of phase, and 90° out of phase. Regardless of the strain path, fatigue cracks are initiated at the outer surface of the specimen where the combined stress state is largest. Depending on the salient mode of deformation, distinctive features in the fracture surface manifested themselves with different topographic amplitudes, surface roughness, and mesoscopic cracking orientation in the vicinity of the initiation site. The dominant crack propagation path was in the circumferential direction of the hollow tubular specimen (i.e., cracking transverse to the sample axis, with little to no branching), which is congruent with previous findings of low to moderate shear strain energy density (SED) multiaxial loading. For proportional loading, the initiation zone surface morphology was largely flat and striated, whereas, at phase angles of 45° and 90°, the initiation surface became more faceted and inclined. Overall, both a qualitative and quantitative link was developed between the fracture surface morphology and the level of non-proportionality in the loading providing useful insight into the fracture mechanics of forged magnesium as a relevant focus for future study.

Keywords: fatigue, fracture, magnesium, forging, fractography, anisotropy, strain energy density, asymmetry, multiaxial fatigue

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Authors: Bharath R., Tharun Sai N., Bhuvan G.

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This project focuses on developing a Parallel Computing method aimed at optimizing matrix multiplication through GPU acceleration. Addressing algorithmic challenges, GPU programming intricacies, and integration issues, the project aims to enhance efficiency and scalability. The methodology involves algorithm design, GPU programming, and optimization techniques. Future plans include advanced optimizations, extended functionality, and integration with high-level frameworks. User engagement is emphasized through user-friendly interfaces, open- source collaboration, and continuous refinement based on feedback. The project's impact extends to significantly improving matrix multiplication performance in scientific computing and machine learning applications.

Keywords: matrix multiplication, parallel processing, cuda, performance boost, neural networks

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Authors: Vidhi Jain

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Communication between speakers and non-speakers of American Sign Language (ASL) can be problematic, inconvenient, and expensive. This project attempts to bridge the communication gap by designing a portable glove that captures the user’s ASL gestures and outputs the translated text on a smartphone. The glove is equipped with flex sensors, contact sensors, and a gyroscope to measure the flexion of the fingers, the contact between fingers, and the rotation of the hand. The glove’s Arduino UNO microcontroller analyzes the sensor readings to identify the gesture from a library of learned gestures. The Bluetooth module transmits the gesture to a smartphone. Using this device, one day speakers of ASL may be able to communicate with others in an affordable and convenient way.

Keywords: sign language, morse code, convolutional neural network, American sign language, gesture recognition

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Authors: Ardiansyah I. Ryan, Ashsholih K. R., Fathurrohman G. R., Kurniadi M. R., Huda P. A

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The burden of brain-related disease is very high. There are a lot of brain-related diseases with limited treatment result and thus raise the burden more. The Parkinson Disease (PD), Mental Health Problem, or Paralysis of extremities treatments had risen concern, as the patients for those diseases usually had a low quality of life and low chance to recover fully. There are also many other brain or related neural diseases with the similar condition, mainly the treatments for those conditions are still limited as our understanding of the brain function is insufficient. Brain Implant Technology had given hope to help in treating this condition. In this paper, we examine the current update of the brain implant technology. Neurotechnology is growing very rapidly worldwide. The United States Food and Drug Administration (FDA) has approved the use of Deep Brain Stimulation (DBS) as a brain implant in humans. As for neural implant both the cochlear implant and retinal implant are approved by FDA too. All of them had shown a promising result. DBS worked by stimulating a specific region in the brain with electricity. This device is planted surgically into a very specific region of the brain. This device consists of 3 main parts: Lead (thin wire inserted into the brain), neurostimulator (pacemaker-like device, planted surgically in the chest) and an external controller (to turn on/off the device by patient/programmer). FDA had approved DBS for the treatment of PD, Pain Management, Epilepsy and Obsessive Compulsive Disorder (OCD). The target treatment of DBS in PD is to reduce the tremor and dystonia symptoms. DBS has been showing the promising result in animal and limited human trial for other conditions such as Alzheimer, Mental Health Problem (Major Depression, Tourette Syndrome), etc. Every surgery has risks of complications, although in DBS the chance is very low. DBS itself had a very satisfying result as long as the subject criteria to be implanted this device based on indication and strictly selection. Other than DBS, there are several brain implant devices that still under development. It was included (not limited to) implant to treat paralysis (In Spinal Cord Injury/Amyotrophic Lateral Sclerosis), enhance brain memory, reduce obesity, treat mental health problem and treat epilepsy. The potential of neurotechnology is unlimited. When brain function and brain implant were fully developed, it may be one of the major breakthroughs in human history like when human find ‘fire’ for the first time. Support from every sector for further research is very needed to develop and unveil the true potential of this technology.

Keywords: brain implant, deep brain stimulation (DBS), deep brain stimulation, Parkinson

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Authors: Ayperi̇ Haspolat Özcan, Meltem Anafarta Şendağ

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This study aimed to establish a model in the light of current approaches for understanding the mediating role of early maladaptive schemas in the relationship between attachment and anger. Accordingly, the proposed mediation model was tested by mediation with bootstrapping technique, considering gender and attachment figure differences. The university students (N= 444) with ages ranging from 17 to 28 participated in the study. Participants filled out Parental and Peer Attachment Scale Short Form, Young Schema Questionnaire - Short Form 3, Trait Anger and Anger Expression Scales. The mediating role of early maladaptive schemas (impaired autonomy, disconnection and rejection, unrelenting standards, other-directedness, and impaired limits) in the relationship between attachment (mother and father) and anger aspects (trait anger, anger in, anger out and anger control) were found to be significant for both male and female participants. Separate mediation analyses for both genders and different attachment figures have also drawn attention to noticeable differences in the results. Specifically, for females, various paths were discovered in predicting various aspects of anger (anger in, anger out, anger control, and trait anger). On the other hand, for males only anger directed inwards was found to be predicted by any source of attachment through disconnection and rejection schema only. These obvious gender differences in understanding the mechanism of anger are discussed in the light of cultural gender roles and the social acceptance of anger in males. In the area of application, the study of various aspects of anger with particular attention to attachment and early maladaptive schemas as well as the importance of distinguishing the gender differences are emphasized as important points.

Keywords: anger expression, attachment, early maladaptive schemas, trait anger

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Authors: C. Mayr, J. Periya, A. Kariminezhad

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In this paper, we developed a learning framework for the classification of vulnerable road users (VRU) by their range-Doppler signatures. The frequency-modulated continuous-wave (FMCW) radar raw data is first pre-processed to obtain robust object range-Doppler maps per coherent time interval. The complex-valued range-Doppler maps captured from our outdoor measurements are further fed into a convolutional neural network (CNN) to learn the classification. This CNN has gone through a hyperparameter optimization process for improved learning. By learning VRU range-Doppler signatures, the three classes 'pedestrian', 'dog', and 'noise' are classified with an average accuracy of almost 95%. Interestingly, this classification accuracy holds for a combined longitudinal and lateral object trajectories.

Keywords: machine learning, radar, signal processing, autonomous driving

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Authors: Seung Ho Park, Young Su Moon, Nam Ik Cho

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We propose a convolution neural network (CNN) for quality adaptive compression artifact reduction named QCARNet. The proposed method is different from the existing discriminative models that learn a specific model at a certain quality level. The method is composed of a quality estimation CNN (QECNN) and a compression artifact reduction CNN (CARCNN), which are two functionally separate CNNs. By connecting the QECNN and CARCNN, each CARCNN layer is able to adaptively reduce compression artifacts and preserve details depending on the estimated quality level map generated by the QECNN. We experimentally demonstrate that the proposed method achieves better performance compared to other state-of-the-art blind compression artifact reduction methods.

Keywords: compression artifact reduction, deblocking, image denoising, image restoration

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Authors: Jeh Patel, Chandrahas Paidi, Ahmed Hambaba

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Traditional methods for analyzing athlete movement often lack the detail and immediacy required for optimal training. This project aims to address this limitation by developing a Real-time human pose estimation system specifically designed to enhance athlete training across various sports. This system leverages the power of convolutional neural networks (CNNs) to provide a comprehensive and immediate analysis of an athlete’s movement patterns during training sessions. The core architecture utilizes dilated convolutions to capture crucial long-range dependencies within video frames. Combining this with the robust encoder-decoder architecture to further refine pose estimation accuracy. This capability is essential for precise joint localization across the diverse range of athletic poses encountered in different sports. Furthermore, by quantifying movement efficiency, power output, and range of motion, the system provides data-driven insights that can be used to optimize training programs. Pose estimation data analysis can also be used to develop personalized training plans that target specific weaknesses identified in an athlete’s movement patterns. To overcome the limitations posed by outdoor environments, the project employs strategies such as multi-camera configurations or depth sensing techniques. These approaches can enhance pose estimation accuracy in challenging lighting and occlusion scenarios, where pose estimation accuracy in challenging lighting and occlusion scenarios. A dataset is collected From the labs of Martin Luther King at San Jose State University. The system is evaluated through a series of tests that measure its efficiency and accuracy in real-world scenarios. Results indicate a high level of precision in recognizing different poses, substantiating the potential of this technology in practical applications. Challenges such as enhancing the system’s ability to operate in varied environmental conditions and further expanding the dataset for training were identified and discussed. Future work will refine the model’s adaptability and incorporate haptic feedback to enhance the interactivity and richness of the user experience. This project demonstrates the feasibility of an advanced pose detection model and lays the groundwork for future innovations in assistive enhancement technologies.

Keywords: computer vision, deep learning, human pose estimation, U-NET, CNN

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Authors: Shubham Gogri, Lucia Florescu

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Medical diagnosis and radiotherapy treatment planning using Computed Tomography (CT) rely on the quantitative accuracy and quality of the CT images. At the same time, requirements for CT imaging include reducing the radiation dose exposure to patients and minimizing scanning time. A solution to this is the sparse-view CT technique, based on a reduced number of projection views. This, however, introduces a new problem— the incomplete projection data results in lower quality of the reconstructed images. To tackle this issue, deep learning methods have been applied to enhance the quality of the sparse-view CT images. A first approach involved employing Mir-Net, a dedicated deep neural network designed for image enhancement. This showed promise, utilizing an intricate architecture comprising encoder and decoder networks, along with the incorporation of the Charbonnier Loss. However, this approach was computationally demanding. Subsequently, a specialized Generative Adversarial Network (GAN) architecture, rooted in the Pix2Pix framework, was implemented. This GAN framework involves a U-Net-based Generator and a Discriminator based on Convolutional Neural Networks. To bolster the GAN's performance, both Charbonnier and Wasserstein loss functions were introduced, collectively focusing on capturing minute details while ensuring training stability. The integration of the perceptual loss, calculated based on feature vectors extracted from the VGG16 network pretrained on the ImageNet dataset, further enhanced the network's ability to synthesize relevant images. A series of comprehensive experiments with clinical CT data were conducted, exploring various GAN loss functions, including Wasserstein, Charbonnier, and perceptual loss. The outcomes demonstrated significant image quality improvements, confirmed through pertinent metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) between the corrected images and the ground truth. Furthermore, learning curves and qualitative comparisons added evidence of the enhanced image quality and the network's increased stability, while preserving pixel value intensity. The experiments underscored the potential of deep learning frameworks in enhancing the visual interpretation of CT scans, achieving outcomes with SSIM values close to one and PSNR values reaching up to 76.

Keywords: generative adversarial networks, sparse view computed tomography, CT image correction, Mir-Net

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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: Amani Tahat, Serdal Kirmizialtin

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Trajectory fragment methods such as Markov State Models (MSM), Milestoning (MS) and Transition Path sampling are the prime choice of extending the timescale of all atom Molecular Dynamics simulations. In these approaches, a set of structures that covers the accessible phase space has to be chosen a priori using cluster analysis. Structural clustering serves to partition the conformational state into natural subgroups based on their similarity, an essential statistical methodology that is used for analyzing numerous sets of empirical data produced by Molecular Dynamics (MD) simulations. Local transition kernel among these clusters later used to connect the metastable states using a Markovian kinetic model in MSM and a non-Markovian model in MS. The choice of clustering approach in constructing such kernel is crucial since the high dimensionality of the biomolecular structures might easily confuse the identification of clusters when using the traditional hierarchical clustering methodology. Of particular interest, in the case of MS where the milestones are very close to each other, accurate determination of the milestone identity of the trajectory becomes a challenging issue. Throughout this work we present two cluster analysis methods applied to the cis–trans isomerism of dinucleotide AA. The choice of nucleic acids to commonly used proteins to study the cluster analysis is two fold: i) the energy landscape is rugged; hence transitions are more complex, enabling a more realistic model to study conformational transitions, ii) Nucleic acids conformational space is high dimensional. A diverse set of internal coordinates is necessary to describe the metastable states in nucleic acids, posing a challenge in studying the conformational transitions. Herein, we need improved clustering methods that accurately identify the AA structure in its metastable states in a robust way for a wide range of confused data conditions. The single linkage approach of the hierarchical clustering available in GROMACS MD-package is the first clustering methodology applied to our data. Self Organizing Map (SOM) neural network, that also known as a Kohonen network, is the second data clustering methodology. The performance comparison of the neural network as well as hierarchical clustering method is studied by means of computing the mean first passage times for the cis-trans conformational rates. Our hope is that this study provides insight into the complexities and need in determining the appropriate clustering algorithm for kinetic analysis. Our results can improve the effectiveness of decisions based on clustering confused empirical data in studying conformational transitions in biomolecules.

Keywords: milestoning, self organizing map, single linkage, structure clustering

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Authors: Maria Del Mar Maira Vidal, Alvaro Briales

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In this paper, we will conduct an analysis of the results of the I+D+i research project “New types of socio-existential vulnerability, support and care in Spain” (VULSOCU) (2016-20). This project had the objective to analyze the new types of vulnerability that are the result of the combination of several factors as the economic crisis, the unemployment, the transformations of the Welfare State, the individualization, etc. We have, therefore, analyzed the way that Spanish long-term unemployed over 45 years experience vulnerability and its consequences on their lives. We have focused on long-term unemployed over 45 that had previously developed stable career paths and have been looking for a job for two years or more. In order to carry out this analysis, we will try to break the dichotomy between the social and the individual, between the socio-historical and the subjectivity, to overcome some of the limits of the research on unemployment. The fieldwork consisted of more than ten focus groups and fifty in-depth interviews. The work and life ethics completely changed at the turn of the nineteenth and twentieth centuries. In the nineteenth century, companies had trouble maintaining their staff, but in the 21st century, unemployed workers feel that they are useless people. Workers value themselves if they have a job. This unveils that labor is a comprehensive social relationship in capitalist societies. In general, unemployed workers are not able to analyze their unemployment as a social problem. They analyze their unemployment as an individual problem. They blame themselves for their unemployment; instead of taking into account that there are millions of unemployed, they talk about themselves as if they were on their own. And the problems caused by unemployment are explained as psychological problems and are medicalized. Anyway, it is important to highlight that this is the result of an ideology and a social relationship that is part of our historical time.

Keywords: life ethics, work ethics, unemployment, unemployed over 45 years old

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Authors: Majid Rezayi, Sh. Shahaboddin, HNM E. Mahmud, A. Yadollah, A. Saeid, A. Yatimah

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In this study, the adaptive neuro-fuzzy inference system (ANFIS) was applied to obtain the membrane composition model affecting the potential response of our reported polymeric PVC sensor for determining the titanium (III) ions. The performance statistics of the artificial neural network (ANN) and linear regression models for potential slope prediction of membrane composition of titanium (III) ion selective electrode were compared with ANFIS technique. The results show that the ANFIS model can be used as a practical tool for obtaining the Nerntian slope of the proposed sensor in this study.

Keywords: adaptive neuro fuzzy inference, PVC sensor, titanium (III) ions, Nerntian slope

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Authors: Seyed Sobhan Alvani, Mohammad Gohari

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By developing cities and increasing the population, traffic congestion has become a vital problem. Due to this crisis, urban designers try to present solutions to decrease this difficulty. On the other hand, predicting the model with perfect accuracy is essential for solution-providing. The current study presents a model based on artificial intelligence which can predict traffic index based on city population, growth rate, and area. The accuracy of the model was evaluated, which is acceptable and it is around 90%. Thus, urban designers and planners can employ it for predicting traffic index in the future to provide strategies.

Keywords: traffic index, population growth rate, cities wideness, artificial neural network

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Authors: Nuwan Kuruwitaarachchi, M. K. M. Peiris, C. N. Madawala, K. M. A. R. Perera, V. U. N Perera

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Dealing with businesses with the foreign market always took a special place in a country’s economy. Political and social factors came into play making currency rate changes fluctuate rapidly. Currency rate prediction has become an important factor for larger international businesses since large amounts of money exchanged between countries. This research focuses on comparing the accuracy of mainly three models; Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Networks(ANN) and Support Vector Machines(SVM). series of data import, export, USD currency exchange rate respect to LKR has been selected for training using above mentioned algorithms. After training the data set and comparing each algorithm, it was able to see that prediction in SVM performed better than other models. It was improved more by combining SVM and SVR models together.

Keywords: ARIMA, ANN, FFNN, RMSE, SVM, SVR

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Authors: Rajkumar Ghosh

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The Out-of-Sequence Thrust (OOST) is a common phenomenon in collisional tectonic settings like the Himalayas. These OOSTs are activated in different locations at different time frames. These OOST are linked with the multiple Himalayan Thrusts. Apart from minimal documentation in geological mapping for OOST, there exists a lack of field data to establish OOST in the field. This work has considered three thrusts from NW Himalaya in Himachal Pradesh with published data from other sources, allowing a re-examination for correlation of OOST. For the Sutlej section, the approach has been to do fieldwork and microstructural studies. The information related to the cross-cut signature of S/C- and relative time relation could help to predict the nature of OOST. The activation timing, along with the basis of identification of OOST in Higher Himalayan, was documented in various literature. Compilation of the Grain Boundary Migration (GBM) associated temperature range (400–750 °C) was documented from microstructural studies along the Jhakri-Chaura section. No such significant temperature variation across thrusts was observed. Strain variation paths using S Ʌ C angle measurement were carried out along the Jeori-Wangtu transect to distinguish overprinting structures for OOSTs. Near the Chaura Thrust (CT), angular variation of S Ʌ C was documented, and it varies within a range of 15° - 28 °. Along the NH22 (National Highway, 22), all tectonic units of the orogen are exposed in NW Himalaya, INDIA. But there are inherent difficulties in finding field evidence of OOST, largely due to the lack of adequate surface morphology, including topography and drainage pattern.

Keywords: out-of-sequence thrust (OOST), main central thrust (MCT), south tibetan detachment system (STDS), jhakri thrust (JT), sarahan thrust (ST), chaura thrust (CT), higher himalaya (HH), greater himalayan crystalline (GHC)

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Authors: M. Khouil, N. Saber, M. Mestari

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This study aimed to propose, a different architecture of a Path Planning using the NECMOP. where several nonlinear objective functions must be optimized in a conflicting situation. The ability to detect and avoid collision is very important for mobile intelligent machines. However, many artificial vision systems are not yet able to quickly and cheaply extract the wealth information. This network, which has been particularly reviewed, has enabled us to solve with a new approach the problem of collision detection between two convex polyhedra in a fixed time (O (1) time). We used two types of neurons linear and threshold logic, which simplified the actual implementation of all the networks proposed. This article represents a comprehensive algorithm that determine through the AMAXNET network a measure (a mini-maximum point) in a fixed time, which allows us to detect the presence of a potential collision.

Keywords: path planning, collision detection, convex polyhedron, neural network

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Authors: Mohammad Nadeem, Haider Banka, R. Venugopal

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Granulation or agglomeration is a size enlargement process to transform the fine particulates into larger aggregates since the fine size of available materials and minerals poses difficulty in their utilization. Though a long list of methods is available in the literature for the modeling of granulation process to facilitate the in-depth understanding and interpretation of the system, there is still scope of improvements using novel tools and techniques. Intelligent techniques, such as artificial neural network, fuzzy logic, self-organizing map, support vector machine and others, have emerged as compelling alternatives for dealing with imprecision and complex non-linearity of the systems. The present study tries to review the applications of intelligent techniques in the modeling of size enlargement process for fine materials.

Keywords: fine material, granulation, intelligent technique, modelling

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Authors: Yifan Fan, Xudong Luo, Pingping Lin

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Nowadays, dialogue systems increasingly become the way for humans to access many computer systems. So, humans can interact with computers in natural language. A dialogue system consists of three parts: understanding what humans say in natural language, managing dialogue, and generating responses in natural language. In this paper, we survey deep learning based methods for dialogue management, response generation and dialogue evaluation. Specifically, these methods are based on neural network, long short-term memory network, deep reinforcement learning, pre-training and generative adversarial network. We compare these methods and point out the further research directions.

Keywords: dialogue management, response generation, deep learning, evaluation

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Authors: Saahith M. S., Sivakami R.

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In the realm of football analytics, particularly focusing on predicting football player performance, the ability to forecast player success accurately is of paramount importance for teams, managers, and fans. This study introduces an elaborate examination of predicting football player performance through the integration of data visualization methods and machine learning algorithms. The research entails the compilation of an extensive dataset comprising player attributes, conducting data preprocessing, feature selection, model selection, and model training to construct predictive models. The analysis within this study will involve delving into feature significance using methodologies like Select Best and Recursive Feature Elimination (RFE) to pinpoint pertinent attributes for predicting player performance. Various machine learning algorithms, including Random Forest, Decision Tree, Linear Regression, Support Vector Regression (SVR), and Artificial Neural Networks (ANN), will be explored to develop predictive models. The evaluation of each model's performance utilizing metrics such as Mean Squared Error (MSE) and R-squared will be executed to gauge their efficacy in predicting player performance. Furthermore, this investigation will encompass a top player analysis to recognize the top-performing players based on the anticipated overall performance scores. Nationality analysis will entail scrutinizing the player distribution based on nationality and investigating potential correlations between nationality and player performance. Positional analysis will concentrate on examining the player distribution across various positions and assessing the average performance of players in each position. Age analysis will evaluate the influence of age on player performance and identify any discernible trends or patterns associated with player age groups. The primary objective is to predict a football player's overall performance accurately based on their individual attributes, leveraging data-driven insights to enrich the comprehension of player success on the field. By amalgamating data visualization and machine learning methodologies, the aim is to furnish valuable tools for teams, managers, and fans to effectively analyze and forecast player performance. This research contributes to the progression of sports analytics by showcasing the potential of machine learning in predicting football player performance and offering actionable insights for diverse stakeholders in the football industry.

Keywords: football analytics, player performance prediction, data visualization, machine learning algorithms, random forest, decision tree, linear regression, support vector regression, artificial neural networks, model evaluation, top player analysis, nationality analysis, positional analysis

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Authors: Félix David Suárez Bonilla

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A sentiment analysis service to detect polarity (positive, neural, and negative), based on transfer learning, was built using a Spanish version of BERT and applied to tweets written in Spanish. The dataset that was used consisted of 11975 reviews, which were extracted from Google Play using the google-play-scrapper package. The BETO trained model used: the AdamW optimizer, a batch size of 16, a learning rate of 2x10⁻⁵ and 10 epochs. The system was tested using tweets of three presidential pre-candidates from Costa Rica. The system was finally validated using human labeled examples, achieving an accuracy of 83.3%.

Keywords: NLP, transfer learning, BERT, sentiment analysis, social media, opinion mining

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Authors: Aouina Nabila Yasmina, Zine El Abidine Chaoui

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Recently, understanding the interactions of electrons with the DNA molecule and its components has attracted considerable interest because DNA is the main site damaged by ionizing radiation. The interactions of radiation with DNA induce a variety of molecular damage such as single-strand breaks, double-strand breaks, basic damage, cross-links between proteins and DNA, and others, or the formation of free radicals, which, by chemical reactions with DNA, can also lead to breakage of the strand. One factor that can contribute significantly to these processes is the effect of water hydration on the formation and reaction of radiation induced by these radicals in and / or around DNA. B-DNA requires about 30% by weight of water to maintain its native conformation in the crystalline state. The transformation depends on various factors such as sequence, ion composition, concentration and water activity. Partial dehydration converts it to DNA-A. The present study shows the results of theoretical calculations for positrons and electrons elastic scattering with DNA medium and water over a broad energy range from 10 eV to 100 keV. Indeed, electron elastic cross sections and elastic mean free paths are calculated using a corrected form of the independent atom method, taking into account the geometry of the biomolecule (SCAR macro). Moreover, the elastic scattering of electrons and positrons by atoms of the biomolecule was evaluated by means of relativistic (Dirac) partial wave analysis. Our calculated results are compared with theoretical data available in the literature in the absence of experimental data, in particular for positron. As a central result, our electron elastic cross sections are in good agreement with existing theoretical data in the range of 10 eV to 1 keV.

Keywords: elastic cross scrion, elastic mean free path, scar macro method, electron collision

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Authors: Carolina Senabre, Sergio Valero, Miguel Lopez, Antonio Gabaldon

Abstract:

This paper presents a comprehensive survey of the short-term load forecasting (STLF). Since the behavior of consumers and producers continue changing as new technologies, it is an ongoing process, and moreover, new policies become available. The results of a research study for the Spanish Transport System Operator (REE) is presented in this paper. It is presented the improvement of the forecasting accuracy in the Balearic Islands considering the introduction of meteorological variables, such as temperature to reduce forecasting error. Variables analyzed for the forecasting in terms of overall accuracy are cloudiness, solar radiation, and wind velocity. It has also been analyzed the type of days to be considered in the research.

Keywords: short-term load forecasting, power demand, neural networks, load forecasting

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Authors: Farnaz Farhangi

Abstract:

Many people in New Zealand believe that the access to water is inexhaustible, and it comes from a history of virtually unrestricted access to it. For the region like Hamilton which is one of New Zealand’s fastest growing cities, it is crucial for policy makers to know about the future water consumption and implementation of rules and regulation such as universal water metering. Hamilton residents use water freely and they do not have any idea about how much water they use. Hence, one of proposed objectives of this research is focusing on forecasting water consumption using different methods. Residential water consumption time series exhibits seasonal and trend variations. Seasonality is the pattern caused by repeating events such as weather conditions in summer and winter, public holidays, etc. The problem with this seasonal fluctuation is that, it dominates other time series components and makes difficulties in determining other variations (such as educational campaign’s effect, regulation, etc.) in time series. Apart from seasonality, a stochastic trend is also combined with seasonality and makes different effects on results of forecasting. According to the forecasting literature, preprocessing (de-trending and de-seasonalization) is essential to have more performed forecasting results, while some other researchers mention that seasonally non-adjusted data should be used. Hence, I answer the question that is pre-processing essential? A wide range of forecasting methods exists with different pros and cons. In this research, I apply double seasonal ARIMA and Artificial Neural Network (ANN), considering diverse elements such as seasonality and calendar effects (public and school holidays) and combine their results to find the best predicted values. My hypothesis is the examination the results of combined method (hybrid model) and individual methods and comparing the accuracy and robustness. In order to use ARIMA, the data should be stationary. Also, ANN has successful forecasting applications in terms of forecasting seasonal and trend time series. Using a hybrid model is a way to improve the accuracy of the methods. Due to the fact that water demand is dominated by different seasonality, in order to find their sensitivity to weather conditions or calendar effects or other seasonal patterns, I combine different methods. The advantage of this combination is reduction of errors by averaging of each individual model. It is also useful when we are not sure about the accuracy of each forecasting model and it can ease the problem of model selection. Using daily residential water consumption data from January 2000 to July 2015 in Hamilton, I indicate how prediction by different methods varies. ANN has more accurate forecasting results than other method and preprocessing is essential when we use seasonal time series. Using hybrid model reduces forecasting average errors and increases the performance.

Keywords: artificial neural network (ANN), double seasonal ARIMA, forecasting, hybrid model

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Authors: Henry Schwartz, Bogdan Iancu, Magnus Gustafsson, Johan Lilius

Abstract:

The shipping sector generates significant amounts of carbon emissions on annual basis. The excess amount of carbon dioxide is harmful for both the environment and the society, and partly for that reason, there is acute interest to decrease the volume of anthropogenic carbon dioxide emissions in shipping. The usage of the existing cargo carrying capacity can be maximized, and the share of time used in actual transportation operations could be increased if the whole transportation and logistics chain was optimized with the aid of information sharing done through a centralized marketplace and an information-sharing platform. The outcome of this change would be decreased carbon dioxide emission volumes produced per each metric ton of cargo transported by a vessel. Cargo coordination is a platform under development that matches the need for waterborne transportation services with the ships that operate at a given moment in time. In this research, the transition towards adopting cargo coordination is modelled with system dynamics. The model encompasses the complex supply-demand relationships of ship operators and cargo owners. The built scenarios predict the pace at which different stakeholders start using the digitalized platform and by doing so reduce the amount of annual CO2 emissions generated. To improve the reliability of the results, various sensitivity analyses considering the pace of transition as well as the overall impact on the environment (carbon dioxide emissions per amount of cargo transported) are conducted. The results of the study can be used to support investors and politicians in decision making towards more environmentally sustainable solutions. In addition, the model provides concepts and ideas for a wider discussion considering the paths towards carbon neutral transportation.

Keywords: carbon dioxide emissions, energy efficiency, sustainable transportation, system dynamics

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Authors: Angga S. Fajar, A. Aminullah, J. Kiyono, R. A. Safitri

Abstract:

This paper discusses about the application of ANN for optimizing of bridge structure design. ANN has been applied in various field of science concerning prediction and optimization. The structural optimization has several benefit including accelerate structural design process, saving the structural material, and minimize self-weight and mass of structure. In this paper, there are three types of bridge structure that being optimized including PSC I-girder superstructure, composite steel-concrete girder superstructure, and RC bridge pier. The different optimization strategy on each bridge structure implement back propagation method of ANN is conducted in this research. The optimal weight and easier design process of bridge structure with satisfied error are achieved.

Keywords: bridge structures, ANN, optimization, back propagation

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Authors: Victor Rocha Carvalho

Abstract:

The Bonner Sphere Spectrometry was used to obtain the average energy, the fluence rate, and radioprotection quantities such as the personal and ambient dose equivalent of the ²⁴¹AmLi and ²⁴¹AmF isotopic neutron sources used in the Neutron Metrology Laboratory - LN. The counts of the sources were performed with six different spherical moderators around the detector. Through this, the neutron spectrum was obtained by means of the software named NeuraLN, developed by the LN, that uses the neural networks technique. The 241AmLi achieved a result close to the literature, and 241AmF, which contains few published references, acquired a result with a slight variation from the literature. Therefore, besides fulfilling its objective, the work raises questions about a possible standard of the ²⁴¹AmLi and about the lack of work with the ²⁴¹AmF.

Keywords: nuclear physics, neutron metrology, neutron spectrometry, bonner sphere spectrometers

Procedia PDF Downloads 95