Search results for: radial network

Authors: Nataša Kalajdžija, Strahinja Kovačević, Davor Lončar, Sanja Podunavac Kuzmanović, Lidija Jevrić

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In order to investigate the relationship between retention and structure, a quantitative Structure Retention Relationships (QSRRs) study was applied for the prediction of retention times of a set of 23 secoestrane derivatives in a reversed-phase thin-layer chromatography. After the calculation of molecular descriptors, a suitable set of molecular descriptors was selected by using step-wise multiple linear regressions. Artificial Neural Network (ANN) method was employed to model the nonlinear structure-activity relationships. The ANN technique resulted in 5-6-1 ANN model with the correlation coefficient of 0.98. We found that the following descriptors: Critical pressure, total energy, protease inhibition, distribution coefficient (LogD) and parameter of lipophilicity (miLogP) have a significant effect on the retention times. The prediction results are in very good agreement with the experimental ones. This approach provided a new and effective method for predicting the chromatographic retention index for the secoestrane derivatives investigated.

Keywords: lipophilicity, QSRR, RP TLC retention, secoestranes

Procedia PDF Downloads 457

Authors: Xiang Shijie, Zhou Dong, Tian Dan

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This paper focuses on the application of real-time semantic segmentation technology in complex road condition recognition, aiming to address the critical issue of how to improve segmentation accuracy while ensuring real-time performance. Semantic segmentation technology has broad application prospects in fields such as autonomous vehicle navigation and remote sensing image recognition. However, current real-time semantic segmentation networks face significant technical challenges and optimization gaps in balancing speed and accuracy. To tackle this problem, this paper conducts an in-depth study and proposes an innovative Guided Image Reconstruction Module. By resampling high-resolution images into a set of low-resolution images, this module effectively reduces computational complexity, allowing the network to more efficiently extract features within limited resources, thereby improving the performance of real-time segmentation tasks. In addition, a dual-branch network structure is designed in this paper to fully leverage the advantages of different feature layers. A novel Hybrid Attention Mechanism is also introduced, which can dynamically capture multi-scale contextual information and effectively enhance the focus on important features, thus improving the segmentation accuracy of the network in complex road condition. Compared with traditional methods, the proposed model achieves a better balance between accuracy and real-time performance and demonstrates competitive results in road condition segmentation tasks, showcasing its superiority. Experimental results show that this method not only significantly improves segmentation accuracy while maintaining real-time performance, but also remains stable across diverse and complex road conditions, making it highly applicable in practical scenarios. By incorporating the Guided Image Reconstruction Module, dual-branch structure, and Hybrid Attention Mechanism, this paper presents a novel approach to real-time semantic segmentation tasks, which is expected to further advance the development of this field.

Keywords: hybrid attention mechanism, image reconstruction, real-time, road status recognition

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Authors: R. Rajabpour, N. Talebbeydokhti, M. H. Ahmadi

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In recent years, new techniques for solving complex problems in engineering are proposed. One of these techniques is JPSO algorithm. With innovative changes in the nature of the jump algorithm JPSO, it is possible to construct a graph-based solution with a new algorithm called G-JPSO. In this paper, a new algorithm to solve the optimal control problem Fletcher-Powell and optimal control of pumps in water distribution network was evaluated. Optimal control of pumps comprise of optimum timetable operation (status on and off) for each of the pumps at the desired time interval. Maximum number of status on and off for each pumps imposed to the objective function as another constraint. To determine the optimal operation of pumps, a model-based optimization-simulation algorithm was developed based on G-JPSO and JPSO algorithms. The proposed algorithm results were compared well with the ant colony algorithm, genetic and JPSO results. This shows the robustness of proposed algorithm in finding near optimum solutions with reasonable computational cost.

Keywords: G-JPSO, operation, optimization, pumping station, water distribution networks

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Authors: Magdi M. Nabi, Ding-Li Yu

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This paper presents a neural network based model predictive control (MPC) strategy to control a strongly exothermic reaction with complicated nonlinear kinetics given by Chylla-Haase polymerization reactor that requires a very precise temperature control to maintain product uniformity. In the benchmark scenario, the operation of the reactor must be guaranteed under various disturbing influences, e.g., changing ambient temperatures or impurity of the monomer. Such a process usually controlled by conventional cascade control, it provides a robust operation, but often lacks accuracy concerning the required strict temperature tolerances. The predictive control strategy based on the RBF neural model is applied to solve this problem to achieve set-point tracking of the reactor temperature against disturbances. The result shows that the RBF based model predictive control gives reliable result in the presence of some disturbances and keeps the reactor temperature within a tight tolerance range around the desired reaction temperature.

Keywords: Chylla-Haase reactor, RBF neural network modelling, model predictive control, semi-batch reactors

Procedia PDF Downloads 468

Authors: Rahele Mesbah, Nic Van Der Wee, Manja Koenders, Erik Giltay, Albert Van Hemert, Max De Leeuw

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Introduction: Patients with bipolar disorder (BD), characterized by depressive and manic episodes, often suffer from cognitive dysfunction. An up-to-date meta-analysis of functional Magnetic Resonance Imaging (fMRI) studies examining cognitive function in BD is lacking. Objective: The aim of the current fMRI meta-analysis is to investigate brain functioning of bipolar patients compared with healthy subjects within three domains of emotion processing, reward processing, and working memory. Method: Differences in brain regions activation were tested within whole-brain analysis using the activation likelihood estimation (ALE) method. Separate analyses were performed for each cognitive domain. Results: A total of 50 fMRI studies were included: 20 studies used an emotion processing (316 BD and 369 HC) task, 9 studies a reward processing task (215 BD and 213 HC), and 21 studies used a working memory task (503 BD and 445 HC). During emotion processing, BD patients hyperactivated parts of the left amygdala and hippocampus as compared to HC’s, but showed hypoactivation in the inferior frontal gyrus (IFG). Regarding reward processing, BD patients showed hyperactivation in part of the orbitofrontal cortex (OFC). During working memory, BD patients showed increased activity in the prefrontal cortex (PFC) and anterior cingulate cortex (ACC). Conclusions: This meta-analysis revealed evidence for activity disturbances in several brain areas involved in the cognitive functioning of BD patients. Furthermore, most of the found regions are part of the so-called fronto-limbic network which is hypothesized to be affected as a result of BD candidate genes' expression.

Keywords: cognitive functioning, fMRI analysis, bipolar disorder, fronto-limbic network

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Authors: Lu Yang, Meiyang Song, Xiang Yu, Wenhao Zhou, Chuntao Feng

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To address the problem that the FM continuous-wave radar (FMCW) extracts human vital sign signals which are susceptible to noise interference and low reconstruction accuracy, a new detection scheme for the sign signals is proposed. Firstly, an improved complete ensemble empirical modal decomposition with adaptive noise (ICEEMDAN) algorithm is applied to decompose the radar-extracted thoracic signals to obtain several intrinsic modal functions (IMF) with different spatial scales, and then the IMF components are optimized by a BP neural network improved by immune genetic algorithm (IGA). The simulation results show that this scheme can effectively separate the noise and accurately extract the respiratory and heartbeat signals and improve the reconstruction accuracy and signal-to-noise ratio of the sign signals.

Keywords: frequency modulated continuous wave radar, ICEEMDAN, BP neural network, vital signs signal

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Authors: J. Nisar, S. Halim

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The additional relationships i.e., start-to-start, finish-to-finish, and start-to-finish, between activity in Precedence Diagram Method (PDM) provides a more flexible schedule than traditional Critical Path Method (CPM). But, changing the duration of critical activities in the PDM network will have an anomalous effect on the critical path and the project completion date. In this study, we classified the critical activities in two groups i.e., 1. activity on single critical path and 2. activity on multi-critical paths, and six classes i.e., normal, reverse, neutral, perverse, decrease-reverse and increase-normal, based on their effects on project duration in PDM. Furthermore, we determined the maximum float of time by which the duration each type of critical activities can be changed without effecting the project duration. This study would help the project manager to clearly understand the behavior of each critical activity on critical path, and he/she would be able to change the project duration by shortening or lengthening activities based on project budget and project deadline.

Keywords: construction management, critical path method, project scheduling network, precedence diagram method

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Authors: Gurukiran Veerapur, Nytik Birudavolu, Seetharam U. N., Chandravva Hebbi, R. Praneeth Reddy

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This work presents a robust approach for the recognition of characters in Telugu and Kannada, two South Indian scripts with structural similarities in characters. To recognize the characters exhaustive datasets are required, but there are only a few publicly available datasets. As a result, we decided to create a dataset for one language (source language),train the model with it, and then test it with the target language.Telugu is the target language in this work, whereas Kannada is the source language. The suggested method makes use of Canny edge features to increase character identification accuracy on pictures with noise and different lighting. A dataset of 45,150 images containing printed Kannada characters was created. The Nudi software was used to automatically generate printed Kannada characters with different writing styles and variations. Manual labelling was employed to ensure the accuracy of the character labels. The deep learning models like CNN (Convolutional Neural Network) and Visual Attention neural network (VAN) are used to experiment with the dataset. A Visual Attention neural network (VAN) architecture was adopted, incorporating additional channels for Canny edge features as the results obtained were good with this approach. The model's accuracy on the combined Telugu and Kannada test dataset was an outstanding 97.3%. Performance was better with Canny edge characteristics applied than with a model that solely used the original grayscale images. The accuracy of the model was found to be 80.11% for Telugu characters and 98.01% for Kannada words when it was tested with these languages. This model, which makes use of cutting-edge machine learning techniques, shows excellent accuracy when identifying and categorizing characters from these scripts.

Keywords: base characters, modifiers, guninthalu, aksharas, vattakshara, VAN

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Authors: Balraju Vadlakonda, Narasimha Mangadoddy

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The hydrodynamic behavior in a laboratory column flotation was analyzed using particle image velocimetry. For complete characterization of column flotation, it is necessary to determine the flow velocity induced by bubbles in the liquid phase, the bubble velocity and bubble characteristics:diameter,shape and bubble size distribution. An experimental procedure for analyzing simultaneous, phase-separated velocity measurements in two-phase flows was introduced. The non-invasive PIV technique has used to quantify the instantaneous flow field, as well as the time averaged flow patterns in selected planes of the column. Using the novel particle velocimetry (PIV) technique by the combination of fluorescent tracer particles, shadowgraphy and digital phase separation with masking technique measured the bubble velocity as well as the Reynolds stresses in the column. Axial and radial mean velocities as well as fluctuating components were determined for both phases by averaging the sufficient number of double images. Bubble size distribution was cross validated with high speed video camera. Average turbulent kinetic energy of bubble were analyzed. Different air flow rates were considered in the experiments.

Keywords: particle image velocimetry (PIV), bubble velocity, bubble diameter, turbulent kinetic energy

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Authors: Akhil Dubey, Rajnesh Singh

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In modern days there occur many rapid modifications in field of ad hoc network. These modifications create many revolutionary changes in the routing. Predictive energy efficient routing is inspired on the bee’s behavior of swarm intelligence. Predictive routing improves the efficiency of routing in the energetic point of view. The main aim of this routing is the minimum energy consumption during communication and maximized intermediate node’s remaining battery power. This routing is based on food searching behavior of bees. There are two types of bees for the exploration phase the scout bees and for the evolution phase forager bees use by this routing. This routing algorithm computes the energy consumption, fitness ratio and goodness of the path. In this paper we review the literature related with predictive routing, presenting modified routing and simulation result of this algorithm comparison with artificial bee colony based routing schemes in MANET and see the results of path fitness and probability of fitness.

Keywords: mobile ad hoc network, artificial bee colony, PEEBR, modified predictive routing

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Authors: R. B. Knudsen, O. T. Rasmussen, R. A. Alphinas

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The reason for conducting this research is to develop an algorithm that is capable of classifying news articles from the automobile industry, according to the competitive actions that they entail, with the use of Text Mining (TM) methods. It is needed to test how to properly preprocess the data for this research by preparing pipelines which fits each algorithm the best. The pipelines are tested along with nine different classification algorithms in the realm of regression, support vector machines, and neural networks. Preliminary testing for identifying the optimal pipelines and algorithms resulted in the selection of two algorithms with two different pipelines. The two algorithms are Logistic Regression (LR) and Artificial Neural Network (ANN). These algorithms are optimized further, where several parameters of each algorithm are tested. The best result is achieved with the ANN. The final model yields an accuracy of 0.79, a precision of 0.80, a recall of 0.78, and an F1 score of 0.76. By removing three of the classes that created noise, the final algorithm is capable of reaching an accuracy of 94%.

Keywords: Artificial Neural network, Competitive dynamics, Logistic Regression, Text classification, Text mining

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Authors: Zhidong Zhang, Yingchen Yang

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In this study, a diagnostic assessment model for Mastery Engineering Learning was established based on a group of undergraduate students who studied in an engineering course. A diagnostic assessment model can examine both students' learning process and report achievement results. One very unique characteristic is that the diagnostic assessment model can recognize the errors and anything blocking students in their learning processes. The feedback is provided to help students to know how to solve the learning problems with alternative strategies and help the instructor to find alternative pedagogical strategies in the instructional designs. Dynamics is a core course in which is a common course being shared by several engineering programs. This course is a very challenging for engineering students to solve the problems. Thus knowledge acquisition and problem-solving skills are crucial for student success. Therefore, developing an effective and valid assessment model for student learning are of great importance. Diagnostic assessment is such a model which can provide effective feedback for both students and instructor in the mastery of engineering learning.

Keywords: diagnostic assessment, mastery learning, engineering, bayesian network model, learning processes

Procedia PDF Downloads 152

Authors: Innocent Uzougbo Onwuegbuzie

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Wireless Sensor Networks (WSNs) are crucial for numerous applications, yet they face significant challenges due to resource constraints such as limited power and memory. Traditional routing algorithms like Dijkstra, Ad hoc On-Demand Distance Vector (AODV), and Bellman-Ford, while effective in path establishment and discovery, are not optimized for the unique demands of WSNs due to their large memory footprint and power consumption. This paper introduces the Adaptive Energy-Aware Routing (AEAR) model, a solution designed to address these limitations. AEAR integrates reactive route discovery, localized decision-making using geographic information, energy-aware metrics, and dynamic adaptation to provide a robust and efficient routing strategy. We present a detailed comparative analysis using a dataset of 50 sensor nodes, evaluating power consumption, memory footprint, and path cost across AEAR, Dijkstra, AODV, and Bellman-Ford algorithms. Our results demonstrate that AEAR significantly reduces power consumption and memory usage while optimizing path weight. This improvement is achieved through adaptive mechanisms that balance energy efficiency and link quality, ensuring prolonged network lifespan and reliable communication. The AEAR model's superior performance underlines its potential as a viable routing solution for energy-constrained WSN environments, paving the way for more sustainable and resilient sensor network deployments.

Keywords: wireless sensor networks (WSNs), adaptive energy-aware routing (AEAR), routing algorithms, energy, efficiency, network lifespan

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Authors: Yan Fang, Jie Yin, Bihu Wu

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The interaction between climate change and tourism is one of the most promising research areas of recent decades. In this paper, a scientometric analysis of 976 academic publications between 1990 and 2015 related to climate change and tourism is presented in order to characterize the intellectual landscape by identifying and visualizing the evolution of the collaboration network, the co-citation network, and emerging trends of citation burst and keyword co-occurrence. The results show that the number of publications in this field has increased rapidly and it has become an interdisciplinary and multidisciplinary topic. The research areas are dominated by Australia, USA, Canada, New Zealand, and European countries, which have the most productive authors and institutions. The hot topics of climate change and tourism research in recent years are further identified, including the consequences of climate change for tourism, necessary adaptations, the vulnerability of the tourism industry, tourist behaviour and demand in response to climate change, and emission reductions in the tourism sector. The work includes an in-depth analysis of a major forum of climate change and tourism to help readers to better understand global trends in this field in the past 25 years.

Keywords: climate change, tourism, scientometrics, CiteSpace

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Authors: Shankaran Sitarama

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New Product Development involves team members coming together and working in teams to come up with innovative solutions to problems, resulting in new products. Thus, a core attribute of a successful NPD team is their creativity and innovation. They need to be creative as a group, generating a breadth of ideas and innovative solutions that solve or address the problem they are targeting and meet the user’s needs. They also need to be very efficient in their teamwork as they work through the various stages of the development of these ideas, resulting in a POC (proof-of-concept) implementation or a prototype of the product. There are two distinctive traits that the teams need to have, one is ideational creativity, and the other is effective and efficient teamworking. There are multiple types of tensions that each of these traits cause in the teams, and these tensions reflect in the team dynamics. Ideational conflicts arising out of debates and deliberations increase the collective knowledge and affect the team creativity positively. However, the same trait of challenging each other’s viewpoints might lead the team members to be disruptive, resulting in interpersonal tensions, which in turn lead to less than efficient teamwork. Teams that foster and effectively manage these creative tensions are successful, and teams that are not able to manage these tensions show poor team performance. In this paper, it explore these tensions as they result in the team communication social network and propose a Creative Tension Balance index along the lines of Degree of Balance in social networks that has the potential to highlight the successful (and unsuccessful) NPD teams. Team communication reflects the team dynamics among team members and is the data set for analysis. The emails between the members of the NPD teams are processed through a semantic analysis algorithm (LSA) to analyze the content of communication and a semantic similarity analysis to arrive at a social network graph that depicts the communication amongst team members based on the content of communication. This social network is subjected to traditional social network analysis methods to arrive at some established metrics and structural balance analysis metrics. Traditional structural balance is extended to include team interaction pattern metrics to arrive at a creative tension balance metric that effectively captures the creative tensions and tension balance in teams. This CTB (Creative Tension Balance) metric truly captures the signatures of successful and unsuccessful (dissonant) NPD teams. The dataset for this research study includes 23 NPD teams spread out over multiple semesters and computes this CTB metric and uses it to identify the most successful and unsuccessful teams by classifying these teams into low, high and medium performing teams. The results are correlated to the team reflections (for team dynamics and interaction patterns), the team self-evaluation feedback surveys (for teamwork metrics) and team performance through a comprehensive team grade (for high and low performing team signatures).

Keywords: team dynamics, social network analysis, new product development teamwork, structural balance, NPD teams

Procedia PDF Downloads 79

Authors: Tushar K. Routh

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If inputs are engineered in certain manners, they can influence deep neural networks’ (DNN) performances by facilitating misclassifications, a phenomenon well-known as adversarial attacks that question networks’ vulnerability. Recent studies have unfolded the relationship between vulnerability of such networks with their complexity. In this paper, the distinctive influence of additional convolutional layers at the decision boundaries of several DNN architectures was investigated. Here, to engineer inputs from widely known image datasets like MNIST, Fashion MNIST, and Cifar 10, we have exercised One Step Spectral Attack (OSSA) and Fast Gradient Method (FGM) techniques. The aftermaths of adding layers to the robustness of the architectures have been analyzed. For reasoning, separation width from linear class partitions and local geometry (curvature) near the decision boundary have been examined. The result reveals that model complexity has significant roles in adjusting relative distances from margins, as well as the local features of decision boundaries, which impact robustness.

Keywords: DNN robustness, decision boundary, local curvature, network complexity

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Authors: Merouane Boudjerda, Bénina Touaibia, Mustapha K Mihoubi

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In Algeria, water resources play a crucial role in economic development. But over the last decades, they are relatively limited and gradually decreasing to the detriment of agriculture. The agricultural irrigation is the primary water consuming sector followed by the domestic and industrial sectors. The research presented in this paper focuses on the optimization of irrigation water demand. Dynamic Programming-Neural Network (DPNN) method is applied to investigate reservoir optimization. The optimal operation rule is formulated to minimize the gap between water release and water irrigation demand. As a case study, Boukerdane dam’s reservoir system in North of Algeria has been selected to examine our proposed optimization model. The application of DPNN method allowed increasing the satisfaction rate (SR) from 34% to 60%. In addition, the operation rule generated showed more reliable and resilience operation for the examined case study.

Keywords: water management, agricultural demand, Boukerdane dam, dynamic programming, artificial neural network

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Authors: Vijay Pal Singh, Yogesh Chandra Kotiyal

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Structures are a combination of various load carrying members which transfer the loads to the foundation from the superstructure safely. At the design stage, the loading of the structure is defined and appropriate material choices are made based upon their properties, mainly related to strength. The strength of materials kept on reducing with time because of many factors like environmental exposure and deformation caused by unpredictable external loads. Hence, to predict the strength of materials used in structures, various techniques are used. Among these techniques, Non-Destructive Techniques (NDT) are the one that can be used to predict the strength without damaging the structure. In the present study, the compressive strength of concrete has been predicted using Artificial Neural Network (ANN). The predicted strength was compared with the experimentally obtained actual compressive strength of concrete and equations were developed for different models. A good co-relation has been obtained between the predicted strength by these models and experimental values. Further, the co-relation has been developed using two NDT techniques for prediction of strength by regression analysis. It was found that the percentage error has been reduced between the predicted strength by using combined techniques in place of single techniques.

Keywords: rebound, ultra-sonic pulse, penetration, ANN, NDT, regression

Procedia PDF Downloads 428

Authors: Aiswarea Mathew, Smita Mohanty, Jr., S. K. Nayak

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Polyurethane networks based on castor oil (CO) as a renewable resource polyol were synthesized. Polyurethane/epoxy resin interpenetrating network nanocomposites containing modified montmorillonite organoclay (C30B-PU/EP nanocomposites) were prepared by an in situ intercalation method. The conventional spectroscopic characterization of the synthesized samples using FT-IR confirms the existence of the proposed castor oil based PU structure and also showed that strong interactions existed between C30B and EP/PU matrix. The dispersion degree of C30B in EP/PU matrix was characterized by X-Ray diffraction (XRD) method. Scanning electronic microscopy analysis showed that the interpenetrating process of PU and EP increases the exfoliation degree of C30B, and it improves the compatibility and the phase structure of polyurethane/epoxy resin interpenetrating polymer networks (PU/EP IPNs). The thermal stability improves compared to the polyurethane when the PU/EP IPN is formed. Mechanical properties including the Young’s modulus and tensile strength reflected marked improvement with addition of C30B.

Keywords: castor oil, epoxy, montmorillonite, polyurethane

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Authors: Ebrahim Safarian, Kadir Bilen, Akif Ceviz

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The turbocharger and turbocharging have been the inherent component of diesel engines, so that critical parameters of such engines, as BSFC(Brake Specific Fuel Consumption) or thermal efficiency, fuel consumption, BMEP(Brake Mean Effective Pressure), the power density output and emission level have been improved extensively. In general, the turbocharger can be considered as the most complex component of diesel engines, because it has closely interrelated turbomachinery concepts of the turbines and the compressors to thermodynamic fundamentals of internal combustion engines and stress analysis of all components. In this paper, a waste gate for a conventional single stage radial turbine is investigated by consideration of turbochargers operation constrains and engine operation conditions, without any detail designs in the turbine and the compressor. Amount of opening waste gate which extended between the ranges of full opened and closed valve, is demonstrated by limiting compressor boost pressure ratio. Obtaining of an optimum point by regard above mentioned items is surveyed by three linked meanline modeling programs together which consist of Turbomatch®, Compal®, Rital®madules in concepts NREC® respectively.

Keywords: turbocharger, wastegate, diesel engine, concept NREC programs

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Authors: R. Biondi, G. Dys, G. Ferone, T. Renard, M. Zysman

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This paper describes an automated implementable system for impulsive signals detection and recognition. The system uses a Digital Signal Processing device for the detection and identification process. Here the system analyses the signals in real time in order to produce a particular response if needed. The system analyses the signals in real time in order to produce a specific output if needed. Detection is achieved through normalizing the inputs and comparing the read signals to a dynamic threshold and thus avoiding detections linked to loud or fluctuating environing noise. Identification is done through neuronal network algorithms. As a setup our system can receive signals to “learn” certain patterns. Through “learning” the system can recognize signals faster, inducing flexibility to new patterns similar to those known. Sound is captured through a simple jack input, and could be changed for an enhanced recording surface such as a wide-area recorder. Furthermore a communication module can be added to the apparatus to send alerts to another interface if needed.

Keywords: sound detection, impulsive signal, background noise, neural network

Procedia PDF Downloads 320

Authors: Sara Bahariderakhshan, Morteza Ahmadifar

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Leakage is one of the most important problems that water distribution networks face which first reason is high-pressure existence. There are many approaches to control this excess pressure, which using pressure reducing valves (PRVs) or reducing pipe diameter are ones. In the other hand, Pumps are using electricity or fossil fuels to supply needed pressure in distribution networks but excess pressure are made in some branches due to topology problems and water networks’ variables therefore using pressure valves will be inevitable. Although using PRVs is inevitable but it leads to waste electricity or fuels used by pumps because PRVs just waste excess hydraulic pressure to lower it. Pumps working in reverse or Pumps as Turbine (called PaT in this article) are easily available and also effective sources of reducing the equipment cost in small hydropower plants. Urban areas of developing countries are facing increasing in area and maybe water scarcity in near future. These cities need wider water networks which make it hard to predict, control and have a better operation in the urban water cycle. Using more energy and, therefore, more pollution, slower repairing services, more user dissatisfaction and more leakage are these networks’ serious problems. Therefore, more effective systems are needed to monitor and act in these complicated networks than what is used now. In this article a new approach is proposed and evaluated: Using PAT to produce enough energy for remote valves and sensors in the water network. These sensors can be used to determine the discharge, pressure, water quality and other important network characteristics. With the help of remote valves pipeline discharge can be controlled so Instead of wasting excess hydraulic pressure which may be destructive in some cases, obtaining extra pressure from pipeline and producing clean electricity used by remote instruments is this articles’ goal. Furthermore due to increasing the area of the network there is unwanted high pressure in some critical points which is not destructive but lowering the pressure results to longer lifetime for pipeline networks without users’ dissatisfaction. This strategy proposed in this article, leads to use PaT widely for pressure containment and producing energy needed for remote valves and sensors like what happens in supervisory control and data acquisition (SCADA) systems which make it easy for us to monitor, receive data from urban water cycle and make any needed changes in discharge and pressure of pipelines easily and remotely. This is a clean project of energy production without significant environmental impacts and can be used in urban drinking water networks, without any problem for consumers which leads to a stable and dynamic network which lowers leakage and pollution.

Keywords: new energies, pump as turbine, drinking water, distribution network, remote control equipments

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Authors: Li Hui, Riyadh Hindi

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In the United States, bridge construction often employs overhang brackets to support the deck overhang, the weight of fresh concrete, and loads from construction equipment. This approach, however, can lead to significant torsional moments on the exterior girders, potentially causing excessive girder rotation. Such rotations can result in various safety and maintenance issues, including thinning of the deck, reduced concrete cover, and cracking during service. Traditionally, these issues are addressed by installing temporary lateral bracing systems and conducting comprehensive torsional analysis through detailed finite element analysis for the construction of bridge deck overhang. However, this process is often intricate and time-intensive, with the spacing between temporary lateral bracing systems usually relying on the field engineers’ expertise. In this study, a deep neural network model is introduced to limit exterior girder rotation during bridge deck construction. The model predicts the optimal spacing between temporary bracing systems. To train this model, over 10,000 finite element models were generated in SAP2000, incorporating varying parameters such as girder dimensions, span length, and types and spacing of lateral bracing systems. The findings demonstrate that the deep neural network provides an effective and efficient alternative for limiting the exterior girder rotation for bridge deck construction. By reducing dependence on extensive finite element analyses, this approach stands out as a significant advancement in improving safety and maintenance effectiveness in the construction of bridge decks.

Keywords: bridge deck construction, exterior girder rotation, deep learning, finite element analysis

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Authors: S. Shakibaei, P. Alpkokin

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The railway network is one of the major components of a transportation system in a country which may be an indicator of the country’s level of economic improvement. Since 2000s on, revival of national railways and development of High Speed Rail (HSR) lines are one of the most remarkable policies of Turkish government in railway sector. Within this trend, the railway age is to be revived and coming decades will be a golden opportunity. Indubitably, major infrastructures such as road and railway networks require sizeable investment capital, precise maintenance and reparation. Traditionally, governments are held responsible for funding, operating and maintaining these infrastructures. However, lack or shortage of financial resources, risk responsibilities (particularly cost and time overrun), and in some cases inefficacy in constructional, operational and management phases persuade governments to find alternative options. Financial power, efficient experiences and background of private sector are the factors convincing the governments to make a collaboration with private parties to develop infrastructures. Public-Private Partnerships (PPP or 3P or P3) and related regulatory issues are born considering these collaborations. In Turkey, PPP approaches have attracted attention particularly during last decade and these types of investments have been accelerated by government to overcome budget limitations and cope with inefficacy of public sector in improving transportation network and its operation. This study mainly tends to present a comprehensive overview of PPP concept, evaluate the regulatory procedure in Europe and propose a general framework for Turkish State Railways (TCDD) as an outlook on privatization, liberalization and deregulation of railway network.

Keywords: deregulation, high-speed railway, liberalization, privatization, public-private partnership

Procedia PDF Downloads 171

Authors: Andrew N. Saylor, James R. Peters

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Scoliosis is a complex 3D deformity of the thoracic and lumbar spines, clinically diagnosed by measurement of a Cobb angle of 10 degrees or more on a coronal X-ray. The Cobb angle is the angle made by the lines drawn along the proximal and distal endplates of the respective proximal and distal vertebrae comprising the curve. Traditionally, Cobb angles are measured manually using either a marker, straight edge, and protractor or image measurement software. The task of measuring the Cobb angle can also be represented by a function taking the spine geometry rendered using X-ray imaging as input and returning the approximate angle. Although the form of such a function may be unknown, it can be approximated using artificial neural networks (ANNs). The performance of ANNs is affected by many factors, including the choice of activation function and network architecture; however, the effects of these parameters on the accuracy of scoliotic deformity measurements are poorly understood. Therefore, the objective of this study was to systematically investigate the effect of ANN architecture and activation function on Cobb angle measurement from the coronal X-rays of scoliotic subjects. The data set for this study consisted of 609 coronal chest X-rays of scoliotic subjects divided into 481 training images and 128 test images. These data, which included labeled Cobb angle measurements, were obtained from the SpineWeb online database. In order to normalize the input data, each image was resized using bi-linear interpolation to a size of 500 × 187 pixels, and the pixel intensities were scaled to be between 0 and 1. A fully connected (dense) ANN with a fixed cost function (mean squared error), batch size (10), and learning rate (0.01) was developed using Python Version 3.7.3 and TensorFlow 1.13.1. The activation functions (sigmoid, hyperbolic tangent [tanh], or rectified linear units [ReLU]), number of hidden layers (1, 3, 5, or 10), and number of neurons per layer (10, 100, or 1000) were varied systematically to generate a total of 36 network conditions. Stochastic gradient descent with early stopping was used to train each network. Three trials were run per condition, and the final mean squared errors and mean absolute errors were averaged to quantify the network response for each condition. The network that performed the best used ReLU neurons had three hidden layers, and 100 neurons per layer. The average mean squared error of this network was 222.28 ± 30 degrees2, and the average mean absolute error was 11.96 ± 0.64 degrees. It is also notable that while most of the networks performed similarly, the networks using ReLU neurons, 10 hidden layers, and 1000 neurons per layer, and those using Tanh neurons, one hidden layer, and 10 neurons per layer performed markedly worse with average mean squared errors greater than 400 degrees2 and average mean absolute errors greater than 16 degrees. From the results of this study, it can be seen that the choice of ANN architecture and activation function has a clear impact on Cobb angle inference from coronal X-rays of scoliotic subjects.

Keywords: scoliosis, artificial neural networks, cobb angle, medical imaging

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Authors: Morteza Ahmadifar, Sarah Bahari Derakhshan

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Leakage is one of the most important problems that water distribution networks face which first reason is high-pressure existence. There are many approaches to control this excess pressure, which using pressure reducing valves (PRVs) or reducing pipe diameter are ones. On the other hand, Pumps are using electricity or fossil fuels to supply needed pressure in distribution networks but excess pressure are made in some branches due to topology problems and water networks’ variables, therefore using pressure valves will be inevitable. Although using PRVs is inevitable but it leads to waste electricity or fuels used by pumps because PRVs just waste excess hydraulic pressure to lower it. Pumps working in reverse or Pumps as Turbine (called PAT in this article) are easily available and also effective sources of reducing the equipment cost in small hydropower plants. Urban areas of developing countries are facing increasing in area and maybe water scarcity in near future. These cities need wider water networks which make it hard to predict, control and have a better operation in the urban water cycle. Using more energy and therefore more pollution, slower repairing services, more user dissatisfaction and more leakage are these networks’ serious problems. Therefore, more effective systems are needed to monitor and act in these complicated networks than what is used now. In this article a new approach is proposed and evaluated: Using PAT to produce enough energy for remote valves and sensors in the water network. These sensors can be used to determine the discharge, pressure, water quality and other important network characteristics. With the help of remote valves pipeline discharge can be controlled so Instead of wasting excess hydraulic pressure which may be destructive in some cases, obtaining extra pressure from pipeline and producing clean electricity used by remote instruments is this articles’ goal. Furthermore, due to increasing the area of network there is unwanted high pressure in some critical points which is not destructive but lowering the pressure results to longer lifetime for pipeline networks without users’ dissatisfaction. This strategy proposed in this article, leads to use PAT widely for pressure containment and producing energy needed for remote valves and sensors like what happens in supervisory control and data acquisition (SCADA) systems which make it easy for us to monitor, receive data from urban water cycle and make any needed changes in discharge and pressure of pipelines easily and remotely. This is a clean project of energy production without significant environmental impacts and can be used in urban drinking water networks, without any problem for consumers which leads to a stable and dynamic network which lowers leakage and pollution.

Keywords: clean energies, pump as turbine, remote control, urban water distribution network

Procedia PDF Downloads 394

Authors: Bander Alzahrani, Mohammed Alreshoodi

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Information-centric networking (ICN) using architectures such as the Publish-Subscribe Internet Technology (PURSUIT) has been proposed as a new networking model that aims at replacing the current used end-centric networking model of the Internet. This emerged model focuses on what is being exchanged rather than which network entities are exchanging information, which gives the control plane functions such as routing and host location the ability to be specified according to the content items. The forwarding plane of the PURSUIT ICN architecture uses a simple and light mechanism based on Bloom filter technologies to forward the packets. Although this forwarding scheme solve many problems of the today’s Internet such as the growth of the routing table and the scalability issues, it is vulnerable to brute force attacks which are starting point to distributed- denial-of-service (DDoS) attacks. In this work, we design and analyze a novel source-routing and information delivery technique that keeps the simplicity of using Bloom filter-based forwarding while being able to deter different attacks such as denial of service attacks at the ingress of the network. To achieve this, special forwarding nodes called Edge-FW are directly attached to end user nodes and used to perform a security test for malicious injected random packets at the ingress of the path to prevent any possible attack brute force attacks at early stage. In this technique, a core entity of the PURSUIT ICN architecture called topology manager, that is responsible for finding shortest path and creating a forwarding identifiers (FId), uses a cryptographically secure hash function to create a 64-bit hash, h, over the formed FId for authentication purpose to be included in the packet. Our proposal restricts the attacker from injecting packets carrying random FIds with a high amount of filling factor ρ, by optimizing and reducing the maximum allowed filling factor ρm in the network. We optimize the FId to the minimum possible filling factor where ρ ≤ ρm, while it supports longer delivery trees, so the network scalability is not affected by the chosen ρm. With this scheme, the filling factor of any legitimate FId never exceeds the ρm while the filling factor of illegitimate FIds cannot exceed the chosen small value of ρm. Therefore, injecting a packet containing an FId with a large value of filling factor, to achieve higher attack probability, is not possible anymore. The preliminary analysis of this proposal indicates that with the designed scheme, the forwarding function can detect and prevent malicious activities such DDoS attacks at early stage and with very high probability.

Keywords: forwarding identifier, filling factor, information centric network, topology manager

Procedia PDF Downloads 154

Authors: Gaurav Bansod, Swapnil Sutar, Abhijit Patil, Jagdish Patil

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This paper proposes an ultra-lightweight cipher NUX. NUX is a generalized Feistel network. It supports 128/80 bit key length and block length of 64 bit. For 128 bit key length, NUX needs only 1022 GEs which is less as compared to all existing cipher design. NUX design results into less footprint area and minimal memory size. This paper presents security analysis of NUX cipher design which shows cipher’s resistance against basic attacks like Linear and Differential Cryptanalysis. Advanced attacks like Biclique attack is also mounted on NUX cipher design. Two different F function in NUX cipher design results in high diffusion mechanism which generates large number of active S-boxes in minimum number of rounds. NUX cipher has total 31 rounds. NUX design will be best-suited design for critical application like smart grid, IoT, wireless sensor network, where memory size, footprint area and the power dissipation are the major constraints.

Keywords: lightweight cryptography, Feistel cipher, block cipher, IoT, encryption, embedded security, ubiquitous computing

Procedia PDF Downloads 373

Authors: Dehini Rachid, Ferdi Brahim

Abstract:

The shunt active power filter (SAPF) is not destined only to improve the power factor, but also to compensate the unwanted harmonic currents produced by nonlinear loads. This paper presents a SAPF with identification and control method based on artificial neural network (ANN). To identify harmonics, many techniques are used, among them the conventional p-q theory and the relatively recent one the artificial neural network method. It is difficult to get satisfied identification and control characteristics by using a normal (ANN) due to the nonlinearity of the system (SAPF + fast nonlinear load variations). This work is an attempt to undertake a systematic study of the problem to equip the (SAPF) with the harmonics identification and DC link voltage control method based on (ANN). The latter has been applied to the (SAPF) with fast nonlinear load variations. The results of computer simulations and experiments are given, which can confirm the feasibility of the proposed active power filter.

Keywords: artificial neural networks (ANN), p-q theory, harmonics, total harmonic distortion

Procedia PDF Downloads 386

Authors: Anujendra, Parag Kumar Guha Thakurta

Abstract:

The data transmission between mobile hosts and base stations (BSs) in Mobile networks are often vulnerable to failure. Thus, efficient link connectivity, in terms of the services of both base stations and communication channels of the network, is required in wireless mobile networks to achieve highly reliable data transmission. In addition, it is observed that the number of blocked hosts is increased due to insufficient number of channels during heavy load in the network. Under such scenario, the channels are allocated accordingly to offer a reliable communication at any given time. Therefore, a reliability-based channel allocation model with acceptable system performance is proposed as a MOO problem in this paper. Two conflicting parameters known as Resource Reuse factor (RRF) and the number of blocked calls are optimized under reliability constraint in this problem. The solution to such MOO problem is obtained through NSGA-II (Non-dominated Sorting Genetic Algorithm). The effectiveness of the proposed model in this work is shown with a set of experimental results.

Keywords: base station, channel, GA, pareto-optimal, reliability

Procedia PDF Downloads 408