Search results for: neural regeneration

Authors: Amanda R. R. Oliveira, Caio F. F. C. Cunha, Juan C. L. Junior, Amorim H. P. Junior

Abstract:

Children deaths are traumatic events that most often can be prevented. The technology of prevention and intervention in cases of infant deaths is available at low cost and with solid evidence and favorable results, however, with low access cover. Weight is one of the main factors related to death in the neonatal period, so the newborns of low birth weight are a population at high risk of death in the neonatal period, especially early neonatal period. This paper describes the development of a model based in neural network analysis to predict the mortality risk rating in the early neonatal period for newborns of low birth weight to identify the individuals of this population with increased risk of death. The neural network applied was trained with a set of newborns data obtained from Brazilian health system. The resulting network presented great success rate in identifying newborns with high chances of death, which demonstrates the potential for using this tool in an integrated manner to the health system, in order to direct specific actions for improving prognosis of newborns.

Keywords: low birth weight, neonatal death risk, neural network, newborn

Procedia PDF Downloads 427

Authors: M. Caramihai, Irina Severin

Abstract:

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

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

Procedia PDF Downloads 388

Authors: Giselle Maggie-Fer Castañeda Lozano

Abstract:

The main objective of this research was to explore the role of neural network architectures in simulating behavioral phenomena as a potential explanation for selective associations, specifically related to biological constraints on learning. Biological constraints on learning refer to the limitations observed in conditioning procedures, where learning is expected to occur. The study involved simulations of five different experiments exploring various phenomena and sources of biological constraints in learning. These simulations included the interaction between response and reinforcer, stimulus and reinforcer, specificity of stimulus-reinforcer associations, species differences, neuroanatomical constraints, and learning in uncontrolled conditions. The overall results demonstrated that by manipulating neural network architectures, conditions can be created to model and explain diverse biological constraints frequently reported in comparative psychology literature as learning typicities. Additionally, the simulations offer predictive content worthy of experimental testing in the pursuit of new discoveries regarding the specificity of learning. The implications and limitations of these findings are discussed. Finally, it is suggested that this research could inaugurate a line of inquiry involving the use of neural networks to study biological factors in behavior, fostering the development of more ethical and precise research practices.

Keywords: comparative psychology, connectionism, conditioning, experimental analysis of behavior, neural networks

Procedia PDF Downloads 43

Authors: Mohit Gupta, Chialing Wei, Thomas Czerniawski

Abstract:

In this paper, we have built a neural network that can detect walls on 2D sheets and subsequently create a 3D model in Revit using Dynamo. The training set includes 3500 labeled images, and the detection algorithm used is YOLO. Typically, engineers/designers make concentrated efforts to convert 2D cad drawings to 3D models. This costs a considerable amount of time and human effort. This paper makes a contribution in automating the task of 3D walls modeling. 1. Detecting Walls in 2D cad and generating 3D pop-ups in Revit. 2. Saving designer his/her modeling time in drafting elements like walls from 2D cad to 3D representation. An object detection algorithm YOLO is used for wall detection and localization. The neural network is trained over 3500 labeled images of size 256x256x3. Then, Dynamo is interfaced with the output of the neural network to pop-up 3D walls in Revit. The research uses modern technological tools like deep learning and artificial intelligence to automate the process of generating 3D walls without needing humans to manually model them. Thus, contributes to saving time, human effort, and money.

Keywords: neural networks, Yolo, 2D to 3D transformation, CAD object detection

Procedia PDF Downloads 127

Authors: Jinan Fiaidhi, Sabah Mohammed

Abstract:

Diagnosing cataract severity is an important factor in deciding to undertake surgery. It is usually conducted by an ophthalmologist or through taking a variety of fundus photography that needs to be examined by the ophthalmologist. This paper carries out an investigation using a Siamese neural net that can be trained with small anchor samples to score cataract severity. The model used in this paper is based on a triplet loss function that takes the ophthalmologist best experience in rating positive and negative anchors to a specific cataract scaling system. This approach that takes the heuristics of the ophthalmologist is generally called the thick data approach, which is a kind of machine learning approach that learn from a few shots. Clinical Relevance: The lens of the eye is mostly made up of water and proteins. A cataract occurs when these proteins at the eye lens start to clump together and block lights causing impair vision. This research aims at employing thick data machine learning techniques to rate the severity of the cataract using Siamese neural network.

Keywords: thick data analytics, siamese neural network, triplet-loss model, few shot learning

Procedia PDF Downloads 86

Authors: Ibrahima Farikou, Kolontchang Gatchou Alberic Lionel, Tsiagadgui Jean Gustave, Ngo Yamben Marie-Ange, Handy Eone Daniel

Abstract:

Introduction: It was in 1956 that Ilizarov pioneered the concept of osteogenesis in distraction by the device that bears his name to help produce bone and soft tissue regeneration and bone consolidation. This technique is not widely used in Africa where, however, its applications are numerous (loss of bone substances, congenital or acquired malformations). Our goal is to bring the indications of Ilizarov's device back to our practice conditions. Methods: Our study was conducted in 2 hospitals over a period of 20 years. For the retrospective phase, this study included all complete usable records of patients operated on in the Ilizarov external fixator department, and for the prospective phase, all patients operated on in the departments with complete usable records. Our sample was consecutive and not exhaustive. Data were analyzed by SPSS software version 23.0. Results: A total of 52 patients were reviewed. The average age of our patients was 14.7 years. The sex ratio was 1.6 in favor of men. The lower limb was the most affected (49), with a predominance of the tibia (62.4%). The average elongation was 6.4 cm. Traumatic acquired pathologies (delayed union, malunion) represented 60.6%. The mean time to union was seven months. Correction of the limb length discrepancy or filling of loss of bone substance was obtained in 75% of cases. Functionally, 80.8% of the patients treated had regained autonomy at the end of treatment, but in 17.3% of the patients, pain and limping persisted. Conclusion: This technique should be popularized in Africa because the benefit that would accrue to patients is invaluable and would be an attractive alternative to many amputations sometimes carried out in Africa by despair.

Keywords: ilizarov, external fixator, limb lengthening, bone regeneration, africa

Procedia PDF Downloads 78

Authors: Najam Aziz, Nasru Minallah, Ahmad Junaid, Kashaf Gul

Abstract:

Landcover classification using automated classification techniques, while employing remotely sensed multi-spectral imagery, is one of the promising areas of research. Different land conditions at different time are captured through satellite and monitored by applying different classification algorithms in specific environment. In this paper, a SPOT-5 image provided by SUPARCO has been studied and classified in Environment for Visual Interpretation (ENVI), a tool widely used in remote sensing. Then, Artificial Neural Network (ANN) classification technique is used to detect the land cover changes in Abbottabad district. Obtained results are compared with a pixel based Distance classifier. The results show that ANN gives the better overall accuracy of 99.20% and Kappa coefficient value of 0.98 over the Mahalanobis Distance Classifier.

Keywords: landcover classification, artificial neural network, remote sensing, SPOT 5

Procedia PDF Downloads 517

Authors: Tsu-Wang Shen, Shan-Chun Chang, Chih-Hsien Wang, Te-Chao Fang

Abstract:

For electrocardiogram (ECG) biometrics system, it is a tedious process to pre-install user’s high-intensity heart rate (HR) templates in ECG biometric systems. Based on only resting enrollment templates, it is a challenge to identify human by using ECG with the high-intensity HR caused from exercises and stress. This research provides a heartbeat segment method with slope-oriented neural networks against the ECG morphology changes due to high intensity HRs. The method has overall system accuracy at 97.73% which includes six levels of HR intensities. A cumulative match characteristic curve is also used to compare with other traditional ECG biometric methods.

Keywords: high-intensity heart rate, heart rate resistant, ECG human identification, decision based artificial neural network

Procedia PDF Downloads 403

Authors: M. Jazouli, S. Elhoufi, A. Majda, A. Zarghili, R. Aalouane

Abstract:

Autism spectrum disorder is a complex developmental disability. It is defined by a certain set of behaviors. Persons with Autism Spectrum Disorders (ASD) frequently engage in stereotyped and repetitive motor movements. The objective of this article is to propose a method to automatically detect this unusual behavior. Our study provides a clinical tool which facilitates for doctors the diagnosis of ASD. We focus on automatic identification of five repetitive gestures among autistic children in real time: body rocking, hand flapping, fingers flapping, hand on the face and hands behind back. In this paper, we present a gesture recognition system for children with autism, which consists of three modules: model-based movement tracking, feature extraction, and gesture recognition using artificial neural network (ANN). The first one uses the Microsoft Kinect sensor, the second one chooses points of interest from the 3D skeleton to characterize the gestures, and the last one proposes a neural connectionist model to perform the supervised classification of data. The experimental results show that our system can achieve above 93.3% recognition rate.

Keywords: ASD, artificial neural network, kinect, stereotypical motor movements

Procedia PDF Downloads 288

Authors: Paromik Bhattacharyya, Vijay Kumar, Johannes Van Staden

Abstract:

Micropropagation is an important tool for the conservation of threatened and commercially important plant species of which orchids deserve special attention. Ansellia africana is one such medicinally important orchid species having much commercial significance. Thus, development of regeneration protocols for producing clonally stable regenerates using axillary buds is of much importance. However, for large-scale micropropagation to become not only successful but also acceptable by end-users, somaclonal variations occurring in the plantlets need to be eliminated. In the light of the various factors (genotype, ploidy level, in vitro culture age, explant and culture type, etc.) that may account for the somaclonal variations of divergent genetic changes at the cellular and molecular levels, genetic analysis of micropropagated plants using a multidisciplinary approach is of utmost importance. In the present study, the clonal integrity of the long term micropropagated A. africana plants were assessed using advanced molecular marker system i.e. Start Codon Targeted Polymorphism (SCoT). Our studies recorded a clonally stable regeneration protocol for A. africana with a very high degree of clonal fidelity amongst the regenerates. The results obtained from these molecular analyses could help in modifying the regeneration protocols for obtaining clonally stable true to type plantlets for sustainable commercial use.

Keywords: medicinal orchid micropropagation, start codon targeted polymorphism (SCoT), RAP), traditional African pharmacopoeia, genetic fidelity

Procedia PDF Downloads 403

Authors: Aouiche Abdelaziz, Soudani Mouhamed Salah, Aouiche El Moundhe

Abstract:

The present paper addresses the utilization of Artificial Neural Networks (ANNs) and Fuzzy Inference Systems (FISs) for the identification and control of dynamical systems with some degree of uncertainty. Because ANNs and FISs have an inherent ability to approximate functions and to adapt to changes in input and parameters, they can be used to control systems too complex for linear controllers. In this work, we show how ANNs and FISs can be put in order to form nets that can learn from external data. In sequence, it is presented structures of inputs that can be used along with ANNs and FISs to model non-linear systems. Four systems were used to test the identification and control of the structures proposed. The results show the ANNs and FISs (Back Propagation Algorithm) used were efficient in modeling and controlling the non-linear plants.

Keywords: non-linear systems, fuzzy set Models, neural network, control law

Procedia PDF Downloads 184

Authors: B. S. Akhmetov, A. I. Ivanov, T. S. Kartbayev, A. Y. Malygin, K. Mukapil, S. D. Tolybayev

Abstract:

The article examines the testing of the neural network converter of biometrics code. Determined the main reasons that prevented the use adopted in the works of foreign researchers classical a Binomial Law when describing distribution of measures of Hamming "Alien" codes-responses.

Keywords: biometrics, testing, neural network, converter of biometrics-code, Hamming's measure

Procedia PDF Downloads 1118

Authors: Gong Zhang

Abstract:

The villages surrounding protected areas of the Sites are a unique vernacular heritage due to their geographical location, long history, and the combination of nature and humanity. With the construction of more and more heritage sites, the villages around them are faced with the conflict between conservation and development. How to carry out sustainable micro-renewal while preserving the authenticity of the vernacular heritage is of great importance for the co-growth of the village residents and the site. This paper focuses on the process of revitalization of the villages nearby the Erlitou Site Park in China, aiming to study how sustainable village regeneration and conservation can be carried out through the activation of public culture. Firstly, through field research and literature review, this paper studies the vernacular morphology and architecture types of more than ten historical villages around the Erlitou site and investigates the traditional vernacular culture and the daily public activities of the local villagers. Secondly, taking the nearest village to the site area, Ranzhuang Village, as an example, the paper studies the role of public cultural activity interventions on the three different stages of vernacular heritage renewal: master planning, architecture group, and acupuncture-style micro-renewal of individual buildings, aiming to summarise its impact on villagers' lives and vernacular heritage. This paper concludes that a living regeneration with a moderate public cultural activity intervention can promote the symbiosis between the heritage site and the life of the villagers and increase the vitality of the village. This study aims to use the example of village regeneration in Henan, China, as a sustainable reference for the co-development of heritage sites and villages in other parts of the world.

Keywords: Erlitou site, public culture intervention, sustainable, vernacular heritage

Procedia PDF Downloads 227

Authors: Abdulkader Helwan

Abstract:

Rheumatoid joint inflammation is characterized as a perpetual incendiary issue which influences the joints by hurting body tissues Therefore, there is an urgent need for an effective intelligent identification system of knee Rheumatoid arthritis especially in its early stages. This paper is to develop a new intelligent system for the identification of Rheumatoid arthritis of the knee utilizing image processing techniques and neural classifier. The system involves two principle stages. The first one is the image processing stage in which the images are processed using some techniques such as RGB to gryascale conversion, rescaling, median filtering, background extracting, images subtracting, segmentation using canny edge detection, and features extraction using pattern averaging. The extracted features are used then as inputs for the neural network which classifies the X-ray knee images as normal or abnormal (arthritic) based on a backpropagation learning algorithm which involves training of the network on 400 X-ray normal and abnormal knee images. The system was tested on 400 x-ray images and the network shows good performance during that phase, resulting in a good identification rate 97%.

Keywords: rheumatoid arthritis, intelligent identification, neural classifier, segmentation, backpropoagation

Procedia PDF Downloads 516

Authors: P. Singh, R. M. Banik

Abstract:

Artificial neural network (ANN) was employed to optimize assay parameters viz., time, temperature, pH of reaction mixture, enzyme volume and substrate concentration of L-glutaminase from Bacillus cereus MTCC 1305. ANN model showed high value of coefficient of determination (0.9999), low value of root mean square error (0.6697) and low value of absolute average deviation. A multilayer perceptron neural network trained with an error back-propagation algorithm was incorporated for developing a predictive model and its topology was obtained as 5-3-1 after applying Levenberg Marquardt (LM) training algorithm. The predicted activity of L-glutaminase was obtained as 633.7349 U/l by considering optimum assay parameters, viz., pH of reaction mixture (7.5), reaction time (20 minutes), incubation temperature (35˚C), substrate concentration (40mM), and enzyme volume (0.5ml). The predicted data was verified by running experiment at simulated optimum assay condition and activity was obtained as 634.00 U/l. The application of ANN model for optimization of assay conditions improved the activity of L-glutaminase by 1.499 fold.

Keywords: Bacillus cereus, L-glutaminase, assay parameters, artificial neural network

Procedia PDF Downloads 410

Authors: Vaidehi Iyer, Konstantin Borozdin

Abstract:

Time series prediction problems have many important practical applications, but are notoriously difficult for statistical modeling. Recently, machine learning methods have been attracted significant interest as a practical tool applied to a variety of problems, even though developments in this field tend to be semi-empirical. This paper explores application of Long Short Term Memory based Recurrent Neural Networks to the one-step prediction of time series for both trend and stochastic components. Two types of data are analyzed - daily stock prices, that are often considered to be a typical example of a random walk, - and weather patterns dominated by seasonal variations. Results from both analyses are compared, and reinforced learning framework is used to select more efficient between Recurrent Neural Networks and more traditional auto regression methods. It is shown that both methods are able to follow long-term trends and seasonal variations closely, but have difficulties with reproducing day-to-day variability. Future research directions and potential real world applications are briefly discussed.

Keywords: long short term memory, prediction methods, recurrent neural networks, reinforcement learning

Procedia PDF Downloads 206

Authors: Razieh Rezabeigi Sani, Alireza Farahani, Mahdi Haghi

Abstract:

Displacement, local discontent, and forced exclusion have become prominent parts of urban regeneration activities in the Global North and South. This paper discusses the processes of massive displacement and neighborhood alteration as the consequences of a large-scale political/ideological placemaking project in central Tehran that transformed people's daily lives in surrounding neighborhoods. The conversion of Imam Hussein Square and connecting 17-Shahrivar Street to a pedestrian plaza in 2016 resulted in adjacent neighborhoods' physical, social, and economic degradation. The project has downgraded the economic and social characteristics of urban life in surrounding neighborhoods, commercialized residential land uses, displaced local people and businesses, and created unprecedented housing modes. This research has been conducted in two stages; first, after the project's implementation between 2017-2018, and second, when the street was reopened after local protests in 2021. In the first phase, 50+ on-site interviews were organized with planners, managers, and dwellers about the decision-making processes, design, and project implementation. We find that the project was based on the immediate political objectives and top-down power exertion of the local government in creating exclusive spaces (for religious ceremonies) without considering locals' knowledge, preferences, lifestyles, and everyday interactions. In the continued research in 2021, we utilized data gathered in facilitation activities and several meetings and interviews with local inhabitants and businesses to explore, design, and implement initiatives for bottom-up planning in these neighborhoods. The top-down and product-oriented (rather than process-oriented) planning, dependency on municipal financing rather than local partnerships, and lack of public participation proved to have continued effects on local participation. The paper concludes that urban regeneration projects must be based on the participation of different private/public actors, sustainable financial resources, and overall social and spatial analysis of the peripheral area before interventions.

Keywords: displacement, urban regeneration, distressed neighborhoods, ideological placemaking, Tehran

Procedia PDF Downloads 86

Authors: Mohamed H. Sorour, Hayam F. Shaalan, Heba A. Hani, Eman S. Sayed, Amany A. El-Mansoup

Abstract:

Heavy metals (HMs) present an increasing threat to aquatic and soil environment. Thus, techniques should be developed for the removal and/or recovery of those HMs from point sources in the generating industries. This paper reports our endeavors concerning the development of in-house developed weak cation exchange polyacrylate hydrogel kaolin composites for heavy metals removal. This type of composite enables desirable characteristics and functions including mechanical strength, bed porosity and cost advantages. This paper emphasizes the effect of varying crosslinker (methylenebis(acrylamide)) concentration. The prepared cation exchanger has been subjected to intensive characterization using X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), X-ray fluorescence (XRF) and Brunauer Emmett and Teller (BET) method. Moreover, the performance was investigated using synthetic and real wastewater for an industrial complex east of Cairo. Simulated and real wastewater compositions addressed; Cr, Co, Ni, and Pb are in the range of (92-115), (91-103), (86-88) and (99-125), respectively. Adsorption experiments have been conducted in both batch and column modes. In general, batch tests revealed enhanced cation exchange capacities of 70, 72, 78.2 and 99.9 mg/g from single synthetic wastes while, removal efficiencies of 82.2, 86.4, 44.4 and 96% were obtained for Cr, Co, Ni and Pb, respectively from mixed synthetic wastes. It is concluded that the mixed synthetic and real wastewaters have lower adsorption capacities than single solutions. It is worth mentioned that Pb attained higher adsorption capacities with comparable results in all tested concentrations of synthetic and real wastewaters. Pilot scale experiments were also conducted for mixed synthetic waste in a fluidized bed column for 48 hour cycle time which revealed 86.4%, 58.5%, 66.8% and 96.9% removal efficiency for Cr, Co, Ni, and Pb, respectively with maximum regeneration was also conducted using saline and acid regenerants. Maximum regeneration efficiencies for the column studies higher than the batch ones about by about 30% to 60%. Studies are currently under way to enhance the regeneration efficiency to enable successful scaling up of the adsorption column.

Keywords: polyacrylate hydrogel kaolin, ultrasonic irradiation, heavy metals, adsorption and regeneration

Procedia PDF Downloads 101

Authors: Rahma Abed, Sahbi Bahroun, Ezzeddine Zagrouba

Abstract:

Due to the huge amount of data in videos, extracting the relevant frames became a necessity and an essential step prior to performing face recognition. In this context, we propose a method for extracting keyframes from videos based on face quality and deep learning for a face recognition task. This method has two steps. We start by generating face quality scores for each face image based on the use of three face feature extractors, including Gabor, LBP, and HOG. The second step consists in training a Deep Convolutional Neural Network in a supervised manner in order to select the frames that have the best face quality. The obtained results show the effectiveness of the proposed method compared to the methods of the state of the art.

Keywords: keyframe extraction, face quality assessment, face in video recognition, convolution neural network

Procedia PDF Downloads 204

Authors: Y. Kourd, D. Lefebvre, N. Guersi

Abstract:

This paper presents a new idea for fault detection and isolation (FDI) technique which is applied to industrial system. This technique is based on Neural Networks fault-free and Faulty behaviors Models (NNFM's). NNFM's are used for residual generation, while decision tree architecture is used for residual evaluation. The decision tree is realized with data collected from the NNFM’s outputs and is used to isolate detectable faults depending on computed threshold. Each part of the tree corresponds to specific residual. With the decision tree, it becomes possible to take the appropriate decision regarding the actual process behavior by evaluating few numbers of residuals. In comparison to usual systematic evaluation of all residuals, the proposed technique requires less computational effort and can be used for on line diagnosis. An application example is presented to illustrate and confirm the effectiveness and the accuracy of the proposed approach.

Keywords: neural networks, decision trees, diagnosis, behaviors

Procedia PDF Downloads 470

Authors: Juan Sebastián Hernández

Abstract:

The Nevado del Ruíz volcano, located between the limits of the Departments of Caldas and Tolima in Colombia, presented an unstable behaviour in the course of the year 2020, this volcanic activity led to secondary effects on the crust, which is why the prediction of deformations becomes the task of geoscientists. In the course of this article, the use of tropospheric variables such as evapotranspiration, UV aerosol index, carbon monoxide, nitrogen dioxide, methane, surface temperature, among others, is used to train a set of neural networks that can predict the behaviour of the resulting phase of an unrolled interferogram with the DInSAR technique, whose main objective is to identify and characterise the behaviour of the crust based on the environmental conditions. For this purpose, variables were collected, a generalised linear model was created, and a set of neural networks was created. After the training of the network, validation was carried out with the test data, giving an MSE of 0.17598 and an associated r-squared of approximately 0.88454. The resulting model provided a dataset with good thematic accuracy, reflecting the behaviour of the volcano in 2020, given a set of environmental characteristics.

Keywords: crustal deformation, Tropomi, neural networks (ANN), volcanic activity, DInSAR

Procedia PDF Downloads 76

Authors: Ahmed G. Mahgoub, Dahlia H. Hafez, Mostafa A. Abu Kiefa

Abstract:

The Cone Penetration Test (CPT) is a common in-situ test which generally investigates a much greater volume of soil more quickly than possible from sampling and laboratory tests. Therefore, it has the potential to realize both cost savings and assessment of soil properties rapidly and continuously. The principle objective of this paper is to demonstrate the feasibility and efficiency of using artificial neural networks (ANNs) to predict the soil angle of internal friction (Φ) and the soil modulus of elasticity (E) from CPT results considering the uncertainties and non-linearities of the soil. In addition, ANNs are used to study the influence of different parameters and recommend which parameters should be included as input parameters to improve the prediction. Neural networks discover relationships in the input data sets through the iterative presentation of the data and intrinsic mapping characteristics of neural topologies. General Regression Neural Network (GRNN) is one of the powerful neural network architectures which is utilized in this study. A large amount of field and experimental data including CPT results, plate load tests, direct shear box, grain size distribution and calculated data of overburden pressure was obtained from a large project in the United Arab Emirates. This data was used for the training and the validation of the neural network. A comparison was made between the obtained results from the ANN's approach, and some common traditional correlations that predict Φ and E from CPT results with respect to the actual results of the collected data. The results show that the ANN is a very powerful tool. Very good agreement was obtained between estimated results from ANN and actual measured results with comparison to other correlations available in the literature. The study recommends some easily available parameters that should be included in the estimation of the soil properties to improve the prediction models. It is shown that the use of friction ration in the estimation of Φ and the use of fines content in the estimation of E considerable improve the prediction models.

Keywords: angle of internal friction, cone penetrating test, general regression neural network, soil modulus of elasticity

Procedia PDF Downloads 402

Authors: Aydin Teymourifar, Gurkan Ozturk, Ozan Bahadir

Abstract:

In this paper, a new neural network based variable neighborhood search is proposed for the multi-objective dynamic, flexible job shop scheduling problems. The neural network controls the problems' constraints to prevent infeasible solutions, while the Variable Neighborhood Search (VNS) applies moves, based on the critical block concept to improve the solutions. Two approaches are used for managing the constraints, in the first approach, infeasible solutions are modified according to the constraints, after the moves application, while in the second one, infeasible moves are prevented. Several neighborhood structures from the literature with some modifications, also new structures are used in the VNS. The suggested neighborhoods are more systematically defined and easy to implement. Comparison is done based on a multi-objective flexible job shop scheduling problem that is dynamic because of the jobs different release time and machines breakdowns. The results show that the presented method has better performance than the compared VNSs selected from the literature.

Keywords: constrained optimization, neural network, variable neighborhood search, flexible job shop scheduling, dynamic multi-objective optimization

Procedia PDF Downloads 326

Authors: Mahboubeh Davoudi Pahnekolayi

Abstract:

Begonia rex cv. DS-EYWA is a rare, unique cultivar of begonia rex with curly colorful leaves. Optimization of an in vitro efficient regeneration protocol by focusing on transverse Thin Cell Layer (tTCL) petiole explants for high-scale production of such a beautiful cultivar was considered as our main purpose in this experiment. Thus, various concentrations of Plant Growth Regulators (PGRs) including 6-Benzylaminopurine (BAP), Thidiazuron (TDY), and –Naphthaleneacetic Acid (NAA), were selected in a Completely Randomized Design (CRD) to establish and optimize the direct organogenesis efficiency of this cultivar. Cultivation of 1 mm tTCL petiole explants in noted treatments showed that 1.5 mgl-1 BAP + 0.5 mgl-1 NAA can induce the highest number of direct regenerated shoots and lower concentration of BAP (0.5 mgl-1) can be suggested for shoot elongation before rooting stage. Elongated shoots were successfully rooted in MS free basal medium and acclimatized in 1:1 peat moss: perlite sterilized pot mixture.

Keywords: begonia rare cultivar, direct organogenesis, explant type, regeneration, thin cell layering (TCL)

Procedia PDF Downloads 45

Authors: Krishan Kumar, Mohit Mittal, Pramod Kumar

Abstract:

Wireless sensor network is one of the most promising communication networks for monitoring remote environmental areas. In this network, all the sensor nodes are communicated with each other via radio signals. The sensor nodes have capability of sensing, data storage and processing. The sensor nodes collect the information through neighboring nodes to particular node. The data collection and processing is done by data aggregation techniques. For the data aggregation in sensor network, clustering technique is implemented in the sensor network by implementing self-organizing feature map (SOFM) neural network. Some of the sensor nodes are selected as cluster head nodes. The information aggregated to cluster head nodes from non-cluster head nodes and then this information is transferred to base station (or sink nodes). The aim of this paper is to manage the huge amount of data with the help of SOM neural network. Clustered data is selected to transfer to base station instead of whole information aggregated at cluster head nodes. This reduces the battery consumption over the huge data management. The network lifetime is enhanced at a greater extent.

Keywords: artificial neural network, data clustering, self organization feature map, wireless sensor network

Procedia PDF Downloads 491

Authors: Stylianos Kampakis

Abstract:

This paper presents a new algorithm for neural networks called “Input Dropout with Aggressive Re-weighting” (IDAR) aimed specifically at datasets with many useless features. IDAR combines two techniques (dropout of input neurons and aggressive re weighting) in order to eliminate the influence of noisy features. The technique can be seen as a generalization of dropout. The algorithm is tested on two different benchmark data sets: a noisy version of the iris dataset and the MADELON data set. Its performance is compared against three other popular techniques for dealing with useless features: L2 regularization, LASSO and random forests. The results demonstrate that IDAR can be an effective technique for handling data sets with many useless features.

Keywords: neural networks, feature selection, regularization, aggressive reweighting

Procedia PDF Downloads 432

Authors: H. Maachou, A. Chagnes, G. Cote

Abstract:

The present work reports the properties of chitosan/hydroxyapatite (Cs/HA: 100/00, 70/30 and 30/70) composite microspheres obtained by emulsification processing route. The morphology of chitosane microspheres was observed by a scanning electron microscope (SEM) which shows an aggregate of spherical microspheres with a particle size, determined by optical microscope, ranged from 4 to 10 µm. Thereafter, a biomimetic approach was used to study the in vitro biomineralization of these composites. It concerns the composites immersion in simulated body fluid (SBF) for different times. The deposited calcium phosphate was studied using X-ray diffraction analysis (XRD), FTIR spectroscopy and ICP analysis of phosphorus. In fact, the mineral formed on Cs/HA microspheres was a mixture of carbonated HA and β-TCP as showed by FTIR peaks at 1419,5 and 871,8 cm-1 and XRD peak at 29,5°. This formation was induced by the presence of HA in chitosan microspheres. These results are confirmed by SEM micrographs which chow the Ca-P crystals growth in form of cauliflowers. So, these materials are of great interest for bone regeneration applications due to their ability to nucleate calcium phosphates in presence of simulated body fluid (SBF).

Keywords: hydroxyapatite, chitosan, microsphere, composite, bone regeneration

Procedia PDF Downloads 315

Authors: Li Hui, Riyadh Hindi

Abstract:

Concrete structures frequently suffer from crack formation, a critical issue that can significantly reduce their lifespan by allowing damaging agents to enter. Traditional methods of crack detection depend on manual visual inspections, which heavily relies on the experience and expertise of inspectors using tools. In this study, a more efficient, computer vision-based approach is introduced by using the lightweight MobileNetV2 neural network. A dataset of 40,000 images was used to develop a specialized crack evaluation algorithm. The analysis indicates that MobileNetV2 matches the accuracy of traditional CNN methods but is more efficient due to its smaller size, making it well-suited for mobile device applications. The effectiveness and reliability of this new method were validated through experimental testing, highlighting its potential as an automated solution for crack detection in concrete structures.

Keywords: Concrete crack, computer vision, deep learning, MobileNetV2 neural network

Procedia PDF Downloads 43

Authors: Yichao Ma, Chengsiong Chin, Wailok Woo

Abstract:

Since the last decade, there has been a rapid growth in digital multimedia, such as high-resolution media files and three-dimentional movies. Hence, there is a need for large digital storage such as Hard Disk Drive (HDD). As such, users expect to have a quieter HDD in their laptop. In this paper, a jury test has been conducted on a group of 34 people where 17 of them are students who is the potential consumer, and the remaining are engineers who know the HDD. A total 13 HDD sound samples have been selected from over hundred HDD noise recordings. These samples are selected based on an agreed subjective feeling. The samples are played to the participants using head acoustic playback system which enabled them to experience as similar as possible the same environment as have been recorded. Analysis has been conducted and the obtained results have indicated different group has different perception over the noises. Two neural network-based acoustic annoyance models are established based on back propagation neural network. Four psychoacoustic metrics, loudness, sharpness, roughness and fluctuation strength, are used as the input of the model, and the subjective evaluation results are taken as the output. The developed models are reasonably accurate in simulating both training and test samples.

Keywords: hdd noise, jury test, neural network model, psychoacoustic annoyance

Procedia PDF Downloads 404

Authors: A. Shebani, C. Pislaru

Abstract:

Wear of materials is an everyday experience and has been observed and studied for long time. The prediction of wear is a fundamental problem in the industrial field, mainly correlated to the planning of maintenance interventions and economy. Pin-on-disc test is the most common test which is used to study the wear behaviour. In this paper, the pin-on-disc (AEROTECH UNIDEX 11) is used for the investigation of the effects of normal load and hardness of material on the wear under dry and sliding conditions. In the pin-on-disc rig, two specimens were used; one, a pin which is made of steel with a tip, is positioned perpendicular to the disc, where the disc is made of aluminium. The pin wear and disc wear were measured by using the following instruments: The Talysurf instrument, a digital microscope, and the alicona instrument; where the Talysurf profilometer was used to measure the pin/disc wear scar depth, and the alicona was used to measure the volume loss for pin and disc. After that, the Archard model, American Society for Testing and Materials model (ASTM), and neural network model were used for pin/disc wear modelling and the simulation results are implemented by using the Matlab program. This paper focuses on how the alicona can be considered as a powerful tool for wear measurements and how the neural network is an effective algorithm for wear estimation.

Keywords: wear modelling, Archard Model, ASTM Model, Neural Networks Model, Pin-on-disc Test, Talysurf, digital microscope, Alicona

Procedia PDF Downloads 429