Search results for: skewed generalized error distribution

Authors: Heba Esaily, Rawhia Eledl, Daila Aboelela, Rasha Noreldin

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Objective: Assess serum level of Transforming growth factor beta 2 (TGF-β2) and Matrilin-3 (MATN3) SNP6 polymorphism in osteoarthritic patients Background: Osteoarthritis (OA) is a musculoskeletal disease characterized by pain and joint stiffness. TGF-β 2 is involved in chondrogenesis and osteogenesis, It has found that MATN3 gene and protein expression was correlated with the extent of tissue damage in OA. Findings suggest that regulation of MATN3 expression is essential for maintenance of the cartilage extracellular matrix microenvironment Subjects and Methods: 72 cases of primary OA (56 with knee OA and 16 with generalized OA were compared with that of 18 healthy controls. Radiographs were scored with the Kellgren-Lawrence scale. Serum TGF-β2 was measured by using (ELISA), levels of marker were correlated to radiographic grading of disease and MATN3 SNP6 polymorphism was determined by (PCR-RFLP). Results: MATN3 SNP6 polymorphism and serum level of TGF-β2 were higher in OA compared with controls. Genotype, NN and N allele frequency were higher in patients with OA compared with controls. NN genotype and N allele frequency were higher in knee osteoarthritis than generalized OA. Significant positive correlation between level of TGFβ2 and radiographic grading in group with knee OA, but no correlation between serum level of TGFβ2 and radiographic grading in generalized OA. Conclusion: MATN3 SNP6 polymorphism and TGF-β2 implicated in the pathogenesis of osteoarthritis. Association of N/N genotype with primary osteoarthritis emphasizes on the need for prospective study include larger sample size to confirm the results of the present study.

Keywords: Matrilin-3, transforming growth factor beta 2, primary osteoarthritis, knee osteoarthritis

Procedia PDF Downloads 269

Authors: C. F. Kumru, C. Kocatepe, O. Arikan

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In this study, electric field distribution analyses for three pylon models are carried out by a Finite Element Method (FEM) based software. Analyses are performed in both stationary and time domains to observe instantaneous values along with the effective ones. Considering the results of the study, different line geometries is considerably affecting the magnitude and distribution of electric field although the line voltages are the same. Furthermore, it is observed that maximum values of instantaneous electric field obtained in time domain analysis are quite higher than the effective ones in stationary mode. In consequence, electric field distribution analyses should be individually made for each different line model and the limit exposure values or distances to residential buildings should be defined according to the results obtained.

Keywords: electric field, energy transmission line, finite element method, pylon

Procedia PDF Downloads 728

Authors: Chaitanya Varma, Arpan Mehar

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The present study demonstrates the procedure to analyse speed data collected on various four-lane divided sections in India. Field data for the study was collected at different straight and curved sections on rural highways with the help of radar speed gun and video camera. The data collected at the sections were analysed and parameters pertain to speed distributions were estimated. The different statistical distribution was analysed on vehicle type speed data and for mixed traffic speed data. It was found that vehicle type speed data was either follows the normal distribution or Log-normal distribution, whereas the mixed traffic speed data follows more than one type of statistical distribution. The most common fit observed on mixed traffic speed data were Beta distribution and Weibull distribution. The separate operating speed model based on traffic and roadway geometric parameters were proposed in the present study. The operating speed model with traffic parameters and curve geometry parameters were established. Two different operating speed models were proposed with variables 1/R and Ln(R) and were found to be realistic with a different range of curve radius. The models developed in the present study are simple and realistic and can be used for forecasting operating speed on four-lane highways.

Keywords: highway, mixed traffic flow, modeling, operating speed

Procedia PDF Downloads 460

Authors: Todd Zhou, Mikhail Yurochkin

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Out-of-distribution (OOD) detection is receiving increasing amounts of attention in the machine learning research community, boosted by recent technologies, such as autonomous driving and image processing. This newly-burgeoning field has called for the need for more effective and efficient methods for out-of-distribution generalization methods. Without accessing the label information, deploying machine learning models to out-of-distribution domains becomes extremely challenging since it is impossible to evaluate model performance on unseen domains. To tackle this out-of-distribution detection difficulty, we designed a model selection pipeline algorithm and developed a model selection framework with different impurity and randomness measurements to evaluate and choose the best-performing models for out-of-distribution data. By exploring different randomness scores based on predicted probabilities, we adopted the out-of-distribution entropy and developed a custom-designed score, ”CombinedScore,” as the evaluation criterion. This proposed score was created by adding labeled source information into the judging space of the uncertainty entropy score using harmonic mean. Furthermore, the prediction bias was explored through the equality of opportunity violation measurement. We also improved machine learning model performance through model calibration. The effectiveness of the framework with the proposed evaluation criteria was validated on the Folktables American Community Survey (ACS) datasets.

Keywords: model selection, domain generalization, model fairness, randomness measurements, bias index

Procedia PDF Downloads 124

Authors: Monireh Ahmadi Bani, Adel Khorramrouz, Lalenoor Morvarid, Bagheri Mahtab

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Background:- Foot disorders are common in musculoskeletal problems. Plantar pressure distribution measurement is one the most important part of foot disorders diagnosis for quantitative analysis. However, the association of plantar pressure and foot disorders is not clear. With the growth of dataset and machine learning methods, the relationship between foot disorders and plantar pressures can be detected. Significance of the study:- The purpose of this study was to predict the probability of common foot disorders based on peak plantar pressure distribution and center of pressure during walking. Methodologies:- 2323 participants were assessed in a foot therapy clinic between 2015 and 2021. Foot disorders were diagnosed by an experienced physician and then they were asked to walk on a force plate scanner. After the data preprocessing, due to the difference in walking time and foot size, we normalized the samples based on time and foot size. Some of force plate variables were selected as input to a deep neural network (DNN), and the probability of any each foot disorder was measured. In next step, we used support vector machine (SVM) and run dataset for each foot disorder (classification of yes or no). We compared DNN and SVM for foot disorders prediction based on plantar pressure distributions and center of pressure. Findings:- The results demonstrated that the accuracy of deep learning architecture is sufficient for most clinical and research applications in the study population. In addition, the SVM approach has more accuracy for predictions, enabling applications for foot disorders diagnosis. The detection accuracy was 71% by the deep learning algorithm and 78% by the SVM algorithm. Moreover, when we worked with peak plantar pressure distribution, it was more accurate than center of pressure dataset. Conclusion:- Both algorithms- deep learning and SVM will help therapist and patients to improve the data pool and enhance foot disorders prediction with less expense and error after removing some restrictions properly.

Keywords: deep neural network, foot disorder, plantar pressure, support vector machine

Procedia PDF Downloads 358

Authors: Majid Habib Khan, Jinwei Zhao, Xinhong Hei, Liu Jiedong, Rana Shahzad Noor, Muhammad Imran

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Deep learning is increasingly employed in image classification, yet the scarcity and high cost of labeled data for training remain a challenge. Limited samples often lead to overfitting due to biased sample distribution. This paper introduces a dynamic distribution calibration method for few-shot learning. Initially, base and new class samples undergo normalization to mitigate disparate feature magnitudes. A pre-trained model then extracts feature vectors from both classes. The method dynamically selects distribution characteristics from base classes (both adjacent and remote) in the embedding space, using a threshold value approach for new class samples. Given the propensity of similar classes to share feature distributions like mean and variance, this research assumes a Gaussian distribution for feature vectors. Subsequently, distributional features of new class samples are calibrated using a corrected hyperparameter, derived from the distribution features of both adjacent and distant base classes. This calibration augments the new class sample set. The technique demonstrates significant improvements, with up to 4% accuracy gains in few-shot classification challenges, as evidenced by tests on miniImagenet and CUB datasets.

Keywords: deep learning, computer vision, image classification, few-shot learning, threshold

Procedia PDF Downloads 67

Authors: Jularat Chumnaul

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In forensic science, corpses from various homicides are different; there are both complete and incomplete, depending on causes of death or forms of homicide. For example, some corpses are cut into pieces, some are camouflaged by dumping into the river, some are buried, some are burned to destroy the evidence, and others. If the corpses are incomplete, it can lead to the difficulty of personally identifying because some tissues and bones are destroyed. To specify gender of the corpses from skeletal remains, the most precise method is DNA identification. However, this method is costly and takes longer so that other identification techniques are used instead. The first technique that is widely used is considering the features of bones. In general, an evidence from the corpses such as some pieces of bones, especially the skull and pelvis can be used to identify their gender. To use this technique, forensic scientists are required observation skills in order to classify the difference between male and female bones. Although this technique is uncomplicated, saving time and cost, and the forensic scientists can fairly accurately determine gender by using this technique (apparently an accuracy rate of 90% or more), the crucial disadvantage is there are only some positions of skeleton that can be used to specify gender such as supraorbital ridge, nuchal crest, temporal lobe, mandible, and chin. Therefore, the skeletal remains that will be used have to be complete. The other technique that is widely used for gender specifying in forensic science and archeology is skeletal measurements. The advantage of this method is it can be used in several positions in one piece of bones, and it can be used even if the bones are not complete. In this study, the classification and cluster analysis are applied to this technique, including the Kth Nearest Neighbor Classification, Classification Tree, Ward Linkage Cluster, K-mean Cluster, and Two Step Cluster. The data contains 507 particular individuals and 9 skeletal measurements (diameter measurements), and the performance of five methods are investigated by considering the apparent error rate (APER). The results from this study indicate that the Two Step Cluster and Kth Nearest Neighbor method seem to be suitable to specify gender from human skeletal remains because both yield small apparent error rate of 0.20% and 4.14%, respectively. On the other hand, the Classification Tree, Ward Linkage Cluster, and K-mean Cluster method are not appropriate since they yield large apparent error rate of 10.65%, 10.65%, and 16.37%, respectively. However, there are other ways to evaluate the performance of classification such as an estimate of the error rate using the holdout procedure or misclassification costs, and the difference methods can make the different conclusions.

Keywords: skeletal measurements, classification, cluster, apparent error rate

Procedia PDF Downloads 252

Authors: Norazlin Mohamad, Saiful Adli Bukry, Zarina Zahari, Haidzir Manaf, Hanafi Sawalludin

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Background: Deficit in neuromuscular recruitment and decrease force distribution were the common problems among ankle instability patients due to altered joint kinematics that lead to recurrent ankle injuries. Rigid Tape and KT Tape had widely been used as therapeutic and performance enhancement tools in ankle stability. However the difference effect between this two tapes is still controversial. Objective: To investigate the different effect between Rigid Tape and KT Tape on force distribution and muscle activation among ankle instability patients while standing. Study design: Crossover trial. Participants: 27 patients, age between 18 to 30 years old participated in this study. All the subjects were applied with KT Tape & Rigid Tape on their affected ankle with 3 days of interval for each intervention. The subjects were tested with their barefoot (without tape) first to act as a baseline before proceeding with KT Tape, and then with Rigid Tape. Result: There were no significant difference on force distribution at forefoot and back-foot for both tapes while standing. However the mean data shows that Rigid Tape has the highest force distribution at back-foot rather than forefoot when compared with KT Tape that had more force distribution at forefoot while standing. Regarding muscle activation (Peroneus Longus), results showed significant difference between Rigid Tape and KT Tape (p= 0.048). However, there was no significant difference on Tibialis Anterior muscle activation between both tapes while standing. Conclusion: The results indicated that Peroneus longus muscle was more active when applied Rigid Tape rather than KT Tape in ankle instability patients while standing.

Keywords: ankle instability, kinematic, muscle activation, force distribution, Rigid Tape, KT tape

Procedia PDF Downloads 418

Authors: Tomoaki Hashimoto

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In recent decades, probabilistic constrained optimal control problems have attracted much attention in many research field. Although probabilistic constraints are generally intractable in an optimization problem, several tractable methods haven been proposed to handle probabilistic constraints. In most methods, probabilistic constraints are reduced to deterministic constraints that are tractable in an optimization problem. However, there is a gap between the transformed deterministic constraints in case of known and unknown probability distribution. This paper examines the conservativeness of probabilistic constrained optimization method with the unknown probability distribution. The objective of this paper is to provide a quantitative assessment of the conservatism for tractable constraints in probabilistic constrained optimization with the unknown probability distribution.

Keywords: optimal control, stochastic systems, discrete time systems, probabilistic constraints

Procedia PDF Downloads 581

Authors: Derjew Ayele Ejigu, Houde Song, Xiaojing Liu

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This work presents the particle swarm optimization trained neural network (PSO-NN) supervisory proportional integral derivative (PID) control method to monitor the pressurized water reactor (PWR) core power for safe operation. The proposed control approach is implemented on the transfer function of the PWR core, which is computed from the state-space model. The PWR core state-space model is designed from the neutronics, thermal-hydraulics, and reactivity models using perturbation around the equilibrium value. The proposed control approach computes the control rod speed to maneuver the core power to track the reference in a closed-loop scheme. The particle swarm optimization (PSO) algorithm is used to train the neural network (NN) and to tune the PID simultaneously. The controller performance is examined using integral absolute error, integral time absolute error, integral square error, and integral time square error functions, and the stability of the system is analyzed by using the Bode diagram. The simulation results indicated that the controller shows satisfactory performance to control and track the load power effectively and smoothly as compared to the PSO-PID control technique. This study will give benefit to design a supervisory controller for nuclear engineering research fields for control application.

Keywords: machine learning, neural network, pressurized water reactor, supervisory controller

Procedia PDF Downloads 156

Authors: Abdel Hadi Ebraheim

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This paper introduces a new extension of the Inverse Pareto distribution in the framework of Marshal-Olkin (1997) family of distributions. This model is capable of modeling various shapes of aging and failure data. The statistical properties of the new model are discussed. Several methods are used to estimate the parameters involved. Explicit expressions are derived for different types of moments of value in reliability analysis are obtained. Besides, the order statistics of samples from the new proposed model have been studied. Finally, the usefulness of the new model for modeling reliability data is illustrated using two real data sets with simulation study.

Keywords: pareto distribution, marshal-Olkin, reliability, hazard functions, moments, estimation

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Authors: Ignatio Madanhire, Charles Mbohwa

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This study looks at the best model of aggregate planning activity in an industrial entity and uses the trial and error method on spreadsheets to solve aggregate production planning problems. Also linear programming model is introduced to optimize the aggregate production planning problem. Application of the models in a furniture production firm is evaluated to demonstrate that practical and beneficial solutions can be obtained from the models. Finally some benchmarking of other furniture manufacturing industries was undertaken to assess relevance and level of use in other furniture firms

Keywords: aggregate production planning, trial and error, linear programming, furniture industry

Procedia PDF Downloads 556

Authors: Lina Wu, Ye Li

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An equivalent relation between a harmonic form and a closed co-closed form is established on a complete non-compact manifold. This equivalence has been generalized for a differential k-form ω from Lq spaces to non-Lq spaces when q=2 in the context of p-balanced growth where p=2. Especially for a simple differential k-form on a complete non-compact manifold, the equivalent relation has been verified with the extended scope of q for from finite q-energy in Lq spaces to infinite q-energy in non-Lq spaces when with 2-balanced growth. Generalized Hadamard Theorem, Cauchy-Schwarz Inequality, and Calculus skills including Integration by Parts as well as Convergent Series have been applied as estimation techniques to evaluate growth rates for a differential form. In particular, energy growth rates as indicated by an appropriate power range in a selected test function lead to a balance between a harmonic differential form and a closed co-closed differential form. Research ideas and computational methods in this paper could provide an innovative way in the study of broadening Lq spaces to non-Lq spaces with a wide variety of infinite energy growth for a differential form.

Keywords: closed forms, co-closed forms, harmonic forms, L^q spaces, p-balanced growth, simple differential k-forms

Procedia PDF Downloads 451

Authors: A. D. Zikiryahodjaev, L. V. Bolotina, A. S. Sukhotko

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Purpose. To evaluate the expediency and timeliness of performance of surgical treatment as a component of multi-therapy treatment of patients with stage IV breast cancers. Materials and Methods. This investigation comparatively analyzed the results of complex treatment with or without surgery in patients with metastatic breast cancer. We analyzed retrospectively treatment experience of 196 patients with generalized breast cancer in the department of oncology and breast reconstructive surgery of P.A. Herzen Moscow Cancer Research Institute from 2000 to 2012. The average age was (58±1,1) years. Invasive ductul carcinoma was verified in128 patients (65,3%), invasive lobular carcinoma-33 (16,8%), complex form - 19 (9,7%). Complex palliative care involving drug and radiation therapies was performed in two patient groups. The first group includes 124 patients who underwent surgical intervention as complex treatment, the second group includes 72 patients with only medical therapy. Standard systemic therapy was given to all patients. Results. Overall, 3-and 5-year survival in fist group was 43,8 and 21%, in second - 15,1 and 9,3% respectively [p=0,00002 log-rank]. Median survival in patients with surgical treatment composed 32 months, in patients with only systemic therapy-21. The factors having influencing an influence on the prognosis and the quality of life outcomes for of patients with generalized breast cancer were are also studied: hormone-dependent tumor, Her2/neu hyper-expression, reproductive function status (age, menopause existence). Conclusion.Removing primary breast tumor in patients with generalized breast cancer improve long-term outcomes. Three- and five-year survival increased by 28,7 and 16,3% respectively, and median survival–for 11 months. These patients may benefit from resection of the breast tumor. One explanation for the effect of this resection is that reducing the tumor load influences metastatic growth.

Keywords: breast cancer, combination therapy, factors of prognosis, primary tumor

Procedia PDF Downloads 416

Authors: Geeta Kasana, Kulbir Singh, Satvinder Singh

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In this paper, a steganographic scheme for digital images using Integer Wavelet Transform (IWT) is proposed. The cover image is decomposed into wavelet sub bands using IWT. Each of the subband is divided into blocks of equal size and secret data is embedded into the largest and smallest pixel values of each block of the subband. Visual quality of stego images is acceptable as PSNR between cover image and stego is above 40 dB, imperceptibility is maintained. Experimental results show better tradeoff between capacity and visual perceptivity compared to the existing algorithms. Maximum possible error analysis is evaluated for each of the wavelet subbands of an image.

Keywords: DWT, IWT, MSE, PSNR

Procedia PDF Downloads 504

Authors: Arash Golzari Koloor

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Feedback seems to be an inseparable part of teaching a second/foreign language. One type of feedback is corrective feedback which is one type of error treatment in second language classrooms. This study is a report on the types of corrective feedback employed in an IELTS preparation course. The types of feedback, their frequencies, and their effectiveness are enlisted, enumerated, and interpreted. The results showed that explicit correction and recast were the most frequent types of feedback while repetition and elicitation were the least. The results also revealed that metalinguistic feedback, elicitation, and explicit correction were the most effective types of feedback and affected learners performance greatly.

Keywords: classroom interaction, corrective feedback, error treatment, oral performance

Procedia PDF Downloads 334

Authors: Nirmal Yadav, Tanuja Srivastava

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Shannon theory is an exact method to recover a band limited signals from its sampled values in discrete implementation, using sinc interpolators. But sinc based results are not much satisfactory for band-limited calculations so that convolution with window function, having compact support, has been introduced. Convolution Backprojection algorithm with window function is an approximation algorithm. In this paper, the error has been calculated, arises due to this approximation nature of reconstruction algorithm. This result will be defined for fan beam projection data which is more faster than parallel beam projection.

Keywords: computed tomography, convolution backprojection, radon transform, fan beam

Procedia PDF Downloads 492

Authors: Ramin Rostamkhani, Thurasamy Ramayah

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One of the most important topics of today’s industrial organizations is the challenging issue of supply chain management. In this field, scientists and researchers have published numerous practical articles and models, especially in the last decade. In this research, to our best knowledge, the discussion of data modeling of supply chain management components using well-known statistical distribution functions has been considered. The world of science owns mathematics, and showing the behavior of supply chain data based on the characteristics of statistical distribution functions is innovative research that has not been published anywhere until the moment of doing this research. In an analytical process, describing different aspects of functions including probability density, cumulative distribution, reliability, and failure function can reach the suitable statistical distribution function for each of the components of the supply chain management. It can be applied to predict the behavior data of the relevant component in the future. Providing a model to adapt the best statistical distribution function in the supply chain management components will be a big revolution in the field of the behavior of the supply chain management elements in today's industrial organizations. Demonstrating the final results of the proposed model by introducing the process capability indices before and after implementing it alongside verifying the approach through the relevant assessment as an acceptable verification is a final step. The introduced approach can save the required time and cost to achieve the organizational goals. Moreover, it can increase added value in the organization.

Keywords: analyzing, process capability indices, statistical distribution functions, supply chain management components

Procedia PDF Downloads 87

Authors: Hanmei Peng, Yiqun Wang, Mao Tan, Zhuocen Dai, Yongxin Su

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Efficient energy management in multi-microgrid distribution systems holds significant importance for enhancing the economic benefits of regional power grids. To better balance conflicts among various stakeholders, a two-stage game-based collaborative optimization approach is proposed in this paper, effectively addressing the realistic scenario involving both competition and collaboration among stakeholders. The first stage, aimed at maximizing individual benefits, involves constructing a non-cooperative tariff game model for the distribution network and surplus microgrid. In the second stage, considering power flow and physical line capacity constraints we establish a cooperative P2P game model for the multi-microgrid distribution system, and the optimization involves employing the Lagrange method of multipliers to handle complex constraints. Simulation results demonstrate that the proposed approach can effectively improve the system economics while harmonizing individual and collective rationality.

Keywords: cooperative game, collaborative optimization, multi-microgrid distribution system, non-cooperative game

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Authors: Yagya Raj Pandeya, Joonwhoan Lee

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The dynamic behavior of music and video makes it difficult to evaluate musical instrument playing in a video by computer system. Any television or film video clip with music information are rich sources for analyzing musical instruments using modern machine learning technologies. In this research, we integrate the audio and video information sources using convolutional neural network (CNN) and pass network learned features through recurrent neural network (RNN) to preserve the dynamic behaviors of audio and video. We use different pre-trained CNN for music and video feature extraction and then fine tune each model. The music network use 2D convolutional network and video network use 3D convolution (C3D). Finally, we concatenate each music and video feature by preserving the time varying features. The long short term memory (LSTM) network is used for long-term dynamic feature characterization and then use late fusion with generalized mean. The proposed network performs better performance to recognize the musical instrument using audio-video multimodal neural network.

Keywords: multimodal, 3D convolution, music-video feature extraction, generalized mean

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Authors: David Hasurungan

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This paper objective is to analyze the impact from photovoltaic system integration to power distribution network. The case study in Badak Liquefied Natural Gas (LNG) plant is presented in this paper. Badak LNG electricity network is operated in islanded mode. The total power generation in Badak LNG plant is significantly affected to feed gas supply. Meanwhile, to support the Government regulation, Badak LNG continuously implemented the grid-connected photovoltaic system in existing power distribution network. The impact between train operational mode change in Badak LNG plant and the growth of photovoltaic system is also encompassed in analysis. The analysis and calculation are performed using software Power Factory 15.1.

Keywords: power quality, distribution network, grid-connected photovoltaic system, power management system

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Authors: Jiin-Yuh Jang, Yu-Feng Gan

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In order to improve the comprehensive mechanical properties of the steel, the cooling rate, and the temperature distribution must be controlled in the cooling process. A three-dimensional numerical model for the prediction of the heat transfer coefficient distribution of H-beam in the controlled cooling process was performed in order to obtain the uniform temperature distribution and minimize the maximum stress and the maximum deformation after the controlled cooling. An algorithm developed with a simplified conjugated-gradient method was used as an optimizer to optimize the heat transfer coefficient distribution. The numerical results showed that, for the case of air cooling 5 seconds followed by water cooling 6 seconds with uniform the heat transfer coefficient, the cooling rate is 15.5 (℃/s), the maximum temperature difference is 85℃, the maximum the stress is 125 MPa, and the maximum deformation is 1.280 mm. After optimize the heat transfer coefficient distribution in control cooling process with the same cooling time, the cooling rate is increased to 20.5 (℃/s), the maximum temperature difference is decreased to 52℃, the maximum stress is decreased to 82MPa and the maximum deformation is decreased to 1.167mm.

Keywords: controlled cooling, H-Beam, optimization, thermal stress

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Authors: Nadish Anand, Richard D. Gould

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A simplistic model is introduced for determining the thermal characteristics of a Low-rise Residential (LRR) building and then predicts the energy usage by its Heating Ventilation & Air Conditioning (HVAC) system according to changes in weather conditions which are reflected in the Ambient Temperature (Outside Air Temperature). The LRR buildings are treated as a simple lump for solving the heat transfer problem and the model is derived using the lumped capacitance model of transient conduction heat transfer from bodies. Since most contemporary HVAC systems have a thermostat control which will have an offset temperature and user defined set point temperatures which define when the HVAC system will switch on and off. The aim is to predict without any error the Body Temperature (i.e. the Inside Air Temperature) which will estimate the switching on and off of the HVAC system. To validate the mathematical model derived from lumped capacitance we have used EnergyPlus simulation engine, which simulates Buildings with considerable accuracy. We have predicted through the low order model the Inside Air Temperature of a single house kept in three different climate zones (Detroit, Raleigh & Austin) and different orientations for summer and winter seasons. The prediction error from the model for the same day as that of model parameter calculation has showed an error of < 10% in winter for almost all the orientations and climate zones. Whereas the prediction error is only <10% for all the orientations in the summer season for climate zone at higher latitudes (Raleigh & Detroit). Possible factors responsible for the large variations are also noted in the work, paving way for future research.

Keywords: building energy, energy consumption, energy+, HVAC, low order model, lumped capacitance

Procedia PDF Downloads 266

Authors: Sina Moradi, Christopher W. K. Chow, John Van Leeuwen, David Cook, Mary Drikas, Patrick Hayde, Rose Amal

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Chloramine is commonly used as a disinfectant in drinking water distribution systems (DWDSs), particularly in Australia and the USA. Maintaining a chloramine residual throughout the DWDS is important in ensuring microbiologically safe water is supplied at the customer’s tap. In order to simulate how chloramine behaves when it moves through the distribution system, a water quality network model (WQNM) can be applied. In this work, the WQNM was based on mono-chloramine decomposition reactions, which enabled prediction of mono-chloramine residual at different locations through a DWDS in Australia, using the Bentley commercial hydraulic package (Water GEMS). The accuracy of WQNM predictions is influenced by a number of water quality parameters. Optimization of these parameters in order to obtain the closest results in comparison with actual measured data in a real DWDS would result in both cost reduction as well as reduction in consumption of valuable resources such as energy and materials. In this work, the optimum operating conditions of water quality parameters (i.e. temperature, pH, and initial mono-chloramine concentration) to maximize the accuracy of mono-chloramine residual predictions for two water supply scenarios in an entire network were determined using response surface methodology (RSM). To obtain feasible and economical water quality parameters for highest model predictability, Design Expert 8.0 software (Stat-Ease, Inc.) was applied to conduct the optimization of three independent water quality parameters. High and low levels of the water quality parameters were considered, inevitably, as explicit constraints, in order to avoid extrapolation. The independent variables were pH, temperature and initial mono-chloramine concentration. The lower and upper limits of each variable for two water supply scenarios were defined and the experimental levels for each variable were selected based on the actual conditions in studied DWDS. It was found that at pH of 7.75, temperature of 34.16 ºC, and initial mono-chloramine concentration of 3.89 (mg/L) during peak water supply patterns, root mean square error (RMSE) of WQNM for the whole network would be minimized to 0.189, and the optimum conditions for averaged water supply occurred at pH of 7.71, temperature of 18.12 ºC, and initial mono-chloramine concentration of 4.60 (mg/L). The proposed methodology to predict mono-chloramine residual can have a great potential for water treatment plant operators in accurately estimating the mono-chloramine residual through a water distribution network. Additional studies from other water distribution systems are warranted to confirm the applicability of the proposed methodology for other water samples.

Keywords: chloramine decay, modelling, response surface methodology, water quality parameters

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Authors: Jaime Martín-de-Nicolás, David Mata-Moya, Nerea del-Rey-Maestre, Pedro Gómez-del-Hoyo, María-Pilar Jarabo-Amores

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Ship detection is nowadays quite an important issue in tasks related to sea traffic control, fishery management and ship search and rescue. Although it has traditionally been carried out by patrol ships or aircrafts, coverage and weather conditions and sea state can become a problem. Synthetic aperture radars can surpass these coverage limitations and work under any climatological condition. A fast CFAR ship detector based on a robust statistical modeling of sea clutter with respect to sea states in SAR images is used. In this paper, the minimum SNR required to obtain a given detection probability with a given false alarm rate for any sea state is determined. A Gaussian target model using real SAR data is considered. Results show that SNR does not depend heavily on the class considered. Provided there is some variation in the backscattering of targets in SAR imagery, the detection probability is limited and a post-processing stage based on morphology would be suitable.

Keywords: SAR, generalized gamma distribution, detection curves, radar detection

Procedia PDF Downloads 453

Authors: Christian Meinecke, Bernd Scholz-Reiter

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The integrated problem of production and distribution scheduling is relevant in many industrial applications. Thus, many heuristics to solve this integrated problem have been developed in the last decade. Most of these heuristics use a sequential working principal or a single decomposition and integration approach to separate and solve sub-problems. A heuristic using a multi-step decomposition and integration approach is presented in this paper and evaluated in a case study. The result show significant improved results compared with sequential scheduling heuristics.

Keywords: production and outbound distribution, integrated planning, heuristic, decomposition, integration

Procedia PDF Downloads 429

Authors: Jie Xu, Zengshan Tian, Ze Li

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Previous indoor ranging or localization systems achieving high accuracy time of flight (ToF) estimation relied on two key points. One is to do strict time and frequency synchronization between the transmitter and receiver to eliminate equipment asynchronous errors such as carrier frequency offset (CFO), but this is difficult to achieve in a practical communication system. The other one is to extend the total bandwidth of the communication because the accuracy of ToF estimation is proportional to the bandwidth, and the larger the total bandwidth, the higher the accuracy of ToF estimation obtained. For example, ultra-wideband (UWB) technology is implemented based on this theory, but high precision ToF estimation is difficult to achieve in common WiFi or Bluetooth systems with lower bandwidth compared to UWB. Therefore, it is meaningful to study how to achieve high-precision ranging with lower bandwidth when the transmitter and receiver are asynchronous. To tackle the above problems, we propose a two-way channel error elimination theory and a frequency hopping-based carrier phase ranging algorithm to achieve high accuracy ranging under asynchronous conditions. The two-way channel error elimination theory uses the symmetry property of the two-way channel to solve the asynchronous phase error caused by the asynchronous transmitter and receiver, and we also study the effect of the two-way channel generation time difference on the phase according to the characteristics of different hardware devices. The frequency hopping-based carrier phase ranging algorithm uses frequency hopping to extend the equivalent bandwidth and incorporates a carrier phase ranging algorithm with multipath resolution to achieve a ranging accuracy comparable to that of UWB at 400 MHz bandwidth in the typical 80 MHz bandwidth of commercial WiFi. Finally, to verify the validity of the algorithm, we implement this theory using a software radio platform, and the actual experimental results show that the method proposed in this paper has a median ranging error of 5.4 cm in the 5 m range, 7 cm in the 10 m range, and 10.8 cm in the 20 m range for a total bandwidth of 80 MHz.

Keywords: frequency hopping, phase error elimination, carrier phase, ranging

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Authors: Waddah S. Abdullah, Khaled F. Al-Omari

Abstract:

Electrokinetic remediation is one of the most influential and effective methods to decontaminate contaminated soils. Electroosmosis and electromigration are the processes of electrochemical extraction of contaminants from soils. The driving force that causes removing contaminants from soils (electroosmosis process or electromigration process) is voltage gradient. Therefore, the electric field distribution throughout the soil domain is extremely important to investigate and to determine the factors that help to establish a uniform electric field distribution in order to make the clean-up process work properly and efficiently. In this study, small-sized passive electrodes (made of graphite) were placed at predetermined locations within the soil specimen, and the voltage drop between these passive electrodes was measured in order to observe the electrical distribution throughout the tested soil specimens. The electrokinetic test was conducted on two types of soils; a sandy soil and a clayey soil. The electrical distribution throughout the soil domain was conducted with different tests properties; and the electrical field distribution was observed in three-dimensional pattern in order to establish the electrical distribution within the soil domain. The effects of density, applied voltages, and degree of saturation on the electrical distribution within the remediated soil were investigated. The distribution of the moisture content, concentration of the sodium ions, and the concentration of the calcium ions were determined and established in three-dimensional scheme. The study has shown that the electrical conductivity within soil domain depends on the moisture content and concentration of electrolytes present in the pore fluid. The distribution of the electrical field in the saturated soil was found not be affected by its density. The study has also shown that high voltage gradient leads to non-uniform electric field distribution within the electroremediated soil. Very importantly, it was found that even when the electric field distribution is uniform globally (i.e. between the passive electrodes), local non-uniformity could be established within the remediated soil mass. Cracks or air gaps formed due to temperature rise (because of electric flow in low conductivity regions) promotes electrical tortuosity. Thus, fracturing or cracking formed in the remediated soil mass causes disconnection of electric current and hence, no removal of contaminant occur within these areas.

Keywords: contaminant removal, electrical tortuousity, electromigration, electroosmosis, voltage distribution

Procedia PDF Downloads 421

Authors: Abhijith Asok

Abstract:

This paper presents a generalized approach for the simple linear data transformation, Y=bX, through an integration of multidimensional coordinate geometry, vector space theory and polygonal geometry. The scaling is performed by adding an additional ’Dummy Dimension’ to the n-dimensional data, which helps plot two dimensional component-wise straight lines on pairs of dimensions. The end result is a set of scaled extensions of observations in any of the 2n spatial divisions, where n is the total number of applicable dimensions/dataset variables, created by shifting the n-dimensional plane along the ’Dummy Axis’. The derived scaling factor was found to be dependent on the coordinates of the common point of origin for diverging straight lines and the plane of extension, chosen on and perpendicular to the ’Dummy Axis’, respectively. This result indicates the geometrical interpretation of a linear data transformation and hence, opportunities for a more informed choice of the factor ’b’, based on a better choice of these coordinate values. The paper follows on to identify the effect of this transformation on certain popular distance metrics, wherein for many, the distance metric retained the same scaling factor as that of the features.

Keywords: data transformation, dummy dimension, linear transformation, scaling

Procedia PDF Downloads 297

Authors: Dmitry Dikarev, Ajit Nimbalker, Alexei Davydov

Abstract:

Polar coding is a novel example of error correcting codes, which can achieve Shannon limit at block length N→∞ with log-linear complexity. Active research is being carried to adopt this theoretical concept for using in practical applications such as 5th generation wireless communication systems. Cyclic redundancy check (CRC) error detection code is broadly used in conjunction with successive cancellation list (SCL) decoding algorithm to improve finite-length polar code performance. However, there are two issues: increase of code block payload overhead by CRC bits and decrease of CRC error-detection capability. This paper proposes a method to control CRC overhead and false alarm rate of polar decoding. As shown in the computer simulations results, the proposed method provides the ability to use any set of CRC polynomials with any list size while maintaining the desired level of false alarm rate. This level of flexibility allows using polar codes in 5G New Radio standard.

Keywords: 5G New Radio, channel coding, cyclic redundancy check, list decoding, polar codes

Procedia PDF Downloads 238