Search results for: iterated function system

Authors: J-B. Clavel, N. Thiollière, B. Mouginot

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The presented work is motivated by a French law regarding nuclear waste management. A new conceptual Accelerator Driven System (ADS) designed for the Minor Actinides (MA) transmutation has been assessed by numerical simulation. The MUltiple Spallation Target (MUST) ADS combines high thermal power (up to 1.4 GWth) and high specific power. A 30 mA and 1 GeV proton beam is divided into three secondary beams transmitted on three liquid lead-bismuth spallation targets. Neutron and thermalhydraulic simulations have been performed with the code MURE, based on the Monte-Carlo transport code MCNPX. A methodology has been developed to define characteristic of the MUST ADS concept according to a specific transmutation scenario. The reference scenario is based on a MA flux (neptunium, americium and curium) providing from European Fast Reactor (EPR) and a plutonium multireprocessing strategy is accounted for. The MUST ADS reference concept is a sodium cooled fast reactor. The MA fuel at equilibrium is mixed with MgO inert matrix to limit the core reactivity and improve the fuel thermal conductivity. The fuel is irradiated over five years. Five years of cooling and two years for the fuel fabrication are taken into account. The MUST ADS reference concept burns about 50% of the initial MA inventory during a complete cycle. In term of mass, up to 570 kg/year are transmuted in one concept. The methodology to design the MUST ADS and to calculate fuel composition at equilibrium is precisely described in the paper. A detailed fuel evolution analysis is performed and the reference scenario is compared to a scenario where only americium transmutation is performed.

Keywords: Accelerator Driven System, double strata scenario, minor actinides, MUST, transmutation.

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Authors: Chong-Yang Qiao

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Mobile augmented reality (MAR) tracking targets from the surroundings and aids operators for interactive data and procedures visualization, potential equipment and system understandably. Operators remotely communicate and coordinate with each other for the continuous tasks, information and data exchange between control room and work-site. In the routine work, distributed control system (DCS) monitoring and work-site manipulation require operators interact in real-time manners. The critical question is the improvement of user experience in cooperative works through applying Augmented Reality in the traditional industrial field. The purpose of this exploratory study is to find the cognitive model for the multiple task performance by MAR. In particular, the focus will be on the comparison between different tasks and environment factors which influence information processing. Three experiments use interface and interaction design, the content of start-up, maintenance and stop embedded in the mobile application. With the evaluation criteria of time demands and human errors, and analysis of the mental process and the behavior action during the multiple tasks, heuristic evaluation was used to find the operators performance with different situation factors, and record the information processing in recognition, interpretation, judgment and reasoning. The research will find the functional properties of MAR and constrain the development of the cognitive model. Conclusions can be drawn that suggest MAR is easy to use and useful for operators in the remote collaborative works.

Keywords: Mobile augmented reality, remote collaboration, user experience, cognitive model.

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Authors: Hussain Syed Asad, Richard Kwok Kit Yuen, Gongsheng Huang

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Real-time optimization has been considered an effective approach for improving energy efficient operation of heating, ventilation, and air-conditioning (HVAC) systems. In model-based real-time optimization, model mismatches cannot be avoided. When model mismatches are significant, the performance of the real-time optimization will be impaired and hence the expected energy saving will be reduced. In this paper, the model mismatches for chiller plant on real-time optimization are considered. In the real-time optimization of the chiller plant, simplified semi-physical or grey box model of chiller is always used, which should be identified using available operation data. To overcome the model mismatches associated with the chiller model, hybrid Genetic Algorithms (HGAs) method is used for online real-time training of the chiller model. HGAs combines Genetic Algorithms (GAs) method (for global search) and traditional optimization method (i.e. faster and more efficient for local search) to avoid conventional hit and trial process of GAs. The identification of model parameters is synthesized as an optimization problem; and the objective function is the Least Square Error between the output from the model and the actual output from the chiller plant. A case study is used to illustrate the implementation of the proposed method. It has been shown that the proposed approach is able to provide reliability in decision making, enhance the robustness of the real-time optimization strategy and improve on energy performance.

Keywords: Energy performance, hybrid adaptive modeling, hybrid genetic algorithms, real-time optimization, heating, ventilation, and air-conditioning.

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Authors: Yuri Boiko, Ci Lin, Iluju Kiringa, Tet Yeap

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The performance of a permanent magnet brushless direct current (BLDC) motor controlled by the Kalman filter based position-sensorless drive is studied in terms of its dependence from the system’s parameters variations. The effects of the system’s parameters changes on the dynamic behavior of state variables are verified. Simulated is the closed loop control scheme with Kalman filter in the feedback line. Distinguished are two separate data sampling modes in analyzing feedback output from the BLDC motor: (1) equal angular separation and (2) equal time intervals. In case (1), the data are collected via equal intervals  of rotor’s angular position i, i.e. keeping  = const. In case (2), the data collection time points ti are separated by equal sampling time intervals t = const. Demonstrated are the effects of the parameters changes on the sensorless control flow, in particular, reduction of the instability torque ripples, switching spikes, and torque load balancing. It is specifically shown that an efficient suppression of commutation induced instability torque ripples is an achievable selection of the sampling rate in the Kalman filter settings above a certain critical value. The computational cost of such suppression is shown to be higher for the motors with lower induction values of the windings.

Keywords: BLDC motor, Kalman filter, sensorless drive, state variables, instability torque ripples reduction, sampling rate.

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Authors: Mohammed Sani Abdulai

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Ghana’s Public Financial Management reforms have been going on for some two decades now (1997/98 to 2017/18). Given this long period of reforms, Ghana in 2019 is putting together both a Public Financial Management (PFM) strategy and a Ghana Integrated Financial Management Information System (GIFMIS) strategy for the next 5-years (2020-2024). The primary aim of these dual strategies is assisting the country in moving beyond reforms to consolidation and sustainability. In this paper we, first, examined the evolution of Ghana’s PFM reforms. We, secondly, reviewed the legal and institutional reforms undertaken to strengthen the country’s key PFM institutions. Thirdly, we summarized the strengths and weaknesses identified by the 2018 Public Expenditure and Financial Accountability (PEFA) assessment of Ghana’s PFM system relating to its macro-fiscal framework, budget preparation and approval, budget execution, accounting and fiscal reporting as well as external scrutiny and audit. We, finally, considered what the country should be doing to achieve its intended goal of PFM consolidation and sustainability. Using a qualitative method of review and analysis of existing documents, we, through this paper, brought to the fore the lessons that could be learnt by other developing countries from Ghana’s PFM reforms experiences. These lessons included the need to: (a) undergird any PFM reform with a comprehensive PFM reform strategy; (b) undertake a legal and institutional reforms of the key PFM institutions; (c) assess the strengths and weaknesses of those reforms using PFM performance evaluation tools such as PEFA framework; and (d) move beyond reforms to consolidation and sustainability.

Keywords: Public financial management, public expenditure and financial accountability (PEFA), reforms, consolidation, sustainability.

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Authors: Francis Chinweuba Eboh, Peter Ahlström, Tobias Richards

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Entropy, as an outcome of the second law of thermodynamics, measures the level of irreversibility associated with any process. The identification and reduction of irreversibility in the energy conversion process helps to improve the efficiency of the system. The entropy of pure substances known as absolute entropy is determined at an absolute reference point and is useful in the thermodynamic analysis of chemical reactions; however, municipal solid waste (MSW) is a structurally complicated material with unknown absolute entropy. In this work, an empirical model to calculate the absolute entropy of MSW based on the content of carbon, hydrogen, oxygen, nitrogen, sulphur, and chlorine on a dry ash free basis (daf) is presented. The proposed model was derived from 117 relevant organic substances which represent the main constituents in MSW with known standard entropies using statistical analysis. The substances were divided into different waste fractions; namely, food, wood/paper, textiles/rubber and plastics waste and the standard entropies of each waste fraction and for the complete mixture were calculated. The correlation of the standard entropy of the complete waste mixture derived was found to be s^o_msw= 0.0101C + 0.0630H + 0.0106O + 0.0108N + 0.0155S + 0.0084Cl (kJ.K^-1.kg) and the present correlation can be used for estimating the absolute entropy of MSW by using the elemental compositions of the fuel within the range of 10.3% ≤ C ≤ 95.1%, 0.0% ≤ H ≤ 14.3%, 0.0% ≤ O ≤ 71.1%, 0.0 ≤ N ≤ 66.7%, 0.0% ≤ S ≤ 42.1%, 0.0% ≤ Cl ≤ 89.7%. The model is also applicable for the efficient modelling of a combustion system in a waste-to-energy plant.

Keywords: Absolute entropy, irreversibility, municipal solid waste, waste-to-energy.

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Authors: Camelia Frigioiu, Katica (Stevanovic) Hedrih, Iulian Gabriel Birsan

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In this paper we study the rheonomic mechanical systems from the point of view of Lagrange geometry, by means of its canonical semispray. We present an example of the constraint motion of a material point, in the rheonomic case.

Keywords: Lagrange's equations, mechanical system, non-linear connection, rheonomic Lagrange space.

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Authors: H K Lakshminarayana, J S Bhat, H M Mahesh

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A new estimator for evolutionary spectrum (ES) based on short time Fourier transform (STFT) and modified group delay function (MGDF) by signal decomposition (SD) is proposed. The STFT due to its built-in averaging, suppresses the cross terms and the MGDF preserves the frequency resolution of the rectangular window with the reduction in the Gibbs ripple. The present work overcomes the magnitude distortion observed in multi-component non-stationary signals with STFT and MGDF estimation of ES using SD. The SD is achieved either through discrete cosine transform based harmonic wavelet transform (DCTHWT) or perfect reconstruction filter banks (PRFB). The MGDF also improves the signal to noise ratio by removing associated noise. The performance of the present method is illustrated for cross chirp and frequency shift keying (FSK) signals, which indicates that its performance is better than STFT-MGDF (STFT-GD) alone. Further its noise immunity is better than STFT. The SD based methods, however cannot bring out the frequency transition path from band to band clearly, as there will be gap in the contour plot at the transition. The PRFB based STFT-SD shows good performance than DCTHWT decomposition method for STFT-GD.

Keywords: Evolutionary Spectrum, Modified Group Delay, Discrete Cosine Transform, Harmonic Wavelet Transform, Perfect Reconstruction Filter Banks, Short Time Fourier Transform.

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Authors: Abobaker Mohammed Qasem Farhan, Shangming Yang, Mohammed Al-Nehari

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Pneumonia is a kind of lung disease that creates congestion in the chest. Such pneumonic conditions lead to loss of life due to the severity of high congestion. Pneumonic lung disease is caused by viral pneumonia, bacterial pneumonia, or COVID-19 induced pneumonia. The early prediction and classification of such lung diseases help reduce the mortality rate. We propose the automatic Computer-Aided Diagnosis (CAD) system in this paper using the deep learning approach. The proposed CAD system takes input from raw computerized tomography (CT) scans of the patient's chest and automatically predicts disease classification. We designed the Hybrid Deep Learning Algorithm (HDLA) to improve accuracy and reduce processing requirements. The raw CT scans are pre-processed first to enhance their quality for further analysis. We then applied a hybrid model that consists of automatic feature extraction and classification. We propose the robust 2D Convolutional Neural Network (CNN) model to extract the automatic features from the pre-processed CT image. This CNN model assures feature learning with extremely effective 1D feature extraction for each input CT image. The outcome of the 2D CNN model is then normalized using the Min-Max technique. The second step of the proposed hybrid model is related to training and classification using different classifiers. The simulation outcomes using the publicly available dataset prove the robustness and efficiency of the proposed model compared to state-of-art algorithms.

Keywords: CT scans, COVID-19, deep learning, image processing, pneumonia, lung disease.

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Authors: A. Suñol, D. Vucinic, S.Vanlanduit, T. Markova, A. Aksenov, I. Moskalyov

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Feeder is one of the airships of the Multibody Advanced Airship for Transport (MAAT) system, under development within the EU FP7 project. MAAT is based on a modular concept composed of two different parts that have the possibility to join; respectively they are the so-called Cruiser and Feeder, designed on the lighter than air principle. Feeder, also named ATEN (Airship Transport Elevator Network), is the smaller one which joins the bigger one, Cruiser, also named PTAH (Photovoltaic modular Transport Airship for High altitude),envisaged to happen at 15km altitude. During the MAAT design phase, the aerodynamic studies of the both airships and their interactions are analyzed. The objective of these studies is to understand the aerodynamic behavior of all the preselected configurations, as an important element in the overall MAAT system design. The most of these configurations are only simulated by CFD, while the most feasible one is experimentally analyzed in order to validate and thrust the CFD predictions. This paper presents the numerical and experimental investigation of the Feeder “conical like" shape configuration. The experiments are focused on the aerodynamic force coefficients and the pressure distribution over the Feeder outer surface, while the numerical simulation cover also the analysis of the velocity and pressure distribution. Finally, the wind tunnel experiment is compared with its CFD model in order to validate such specific simulations with respective experiments and to better understand the difference between the wind tunnel and in-flight circumstances.

Keywords: MAAT project Feeder, CFD simulations, wind tunnel experiments, lateral wind influence.

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Authors: P. Mounnarath, U. Schmitz, Ch. Zhang

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Fragility analysis is an effective tool for the seismic vulnerability assessment of civil structures in the last several years. The design of the expansion joints according to various bridge design codes is almost inconsistent, and only a few studies have focused on this problem so far. In this study, the influence of the expansion joint clearances between the girder ends and the abutment backwalls on the seismic fragility assessment of continuous integral bridge frames is investigated. The gaps (ranging from 60 mm, 150 mm, 250 mm and 350 mm) are designed by following two different bridge design code specifications, namely, Caltrans and Eurocode 8-2. Five bridge models are analyzed and compared. The first bridge model serves as a reference. This model uses three-dimensional reinforced concrete fiber beam-column elements with simplified supports at both ends of the girder. The other four models also employ reinforced concrete fiber beam-column elements but include the abutment backfill stiffness and four different gap values. The nonlinear time history analysis is performed. The artificial ground motion sets, which have the peak ground accelerations (PGAs) ranging from 0.1 g to 1.0 g with an increment of 0.05 g, are taken as input. The soil-structure interaction and the P-Δ effects are also included in the analysis. The component fragility curves in terms of the curvature ductility demand to the capacity ratio of the piers and the displacement demand to the capacity ratio of the abutment sliding bearings are established and compared. The system fragility curves are then obtained by combining the component fragility curves. Our results show that in the component fragility analysis, the reference bridge model exhibits a severe vulnerability compared to that of other sophisticated bridge models for all damage states. In the system fragility analysis, the reference curves illustrate a smaller damage probability in the earlier PGA ranges for the first three damage states, they then show a higher fragility compared to other curves in the larger PGA levels. In the fourth damage state, the reference curve has the smallest vulnerability. In both the component and the system fragility analysis, the same trend is found that the bridge models with smaller clearances exhibit a smaller fragility compared to that with larger openings. However, the bridge model with a maximum clearance still induces a minimum pounding force effect.

Keywords: Expansion joint clearance, fiber beam-column element, fragility assessment, time history analysis.

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Authors: M. Rossini, A. Portioli Studacher

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The market competition is moving from the single firm to the whole supply chain because of increasing competition and growing need for operational efficiencies and customer orientation. Supply chain management allows companies to look beyond their organizational boundaries to develop and leverage resources and capabilities of their supply chain partners. This creates competitive advantages in the marketplace and because of this SCM has acquired strategic importance. Lean Approach is a management strategy that focuses on reducing every type of waste present in an organization. This approach is becoming more and more popular among supply chain managers. The supply chain application of lean approach is not frequent. In particular, it is not well studied which are the impacts of lean approach principles in a supply chain context. In literature there are only few studies aimed at understanding the qualitative impact of the lean approach in supply chains. Therefore, the goal of this research work is to study the impacts of lean principles implementation along a supply chain. To achieve this, a simulation model of a threeechelon multi-product supply chain has been built. Kanban system (and several priority policies) and setup time reduction degrees are implemented in the lean-configured supply chain to apply pull and lot-sizing decrease principles respectively. To evaluate the benefits of lean approach, lean supply chain is compared with an EOQ-configured supply chain. The simulation results show that Kanban system and setup-time reduction improve inventory stock level. They also show that logistics efforts are affected to lean implementation degree. The paper concludes describing performances of lean supply chain in different contexts.

Keywords: Inventory policy, Kanban, lean supply chain, simulation study, supply chain management, planning.

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Authors: Astiaj Khoramshahi, Hossein Ahmadi Danesh Ashtiani, Ahmad Khoshgard, Hamidreza Damghani, Leila Damghani

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Rural electrification rates are generally low in Iran and many parts of the world that lack sustainable renewable energy resources. Many homes are based on polluting solutions such as crude oil and diesel generators for lighting, heating, and charging electrical gadgets. Small-scale portable solar battery packs are accessible to the public; however, they have low capacity and are challenging to be distributed in developing countries. To design a battery-based microgrid power systems, the load profile is one of the key parameters. Additionally, the reliability of the system should be taken into account. A conventional microgrid system can be either AC or coupling DC. Both AC and DC microgrids have advantages and disadvantages depending on their application and can be either connected to the main grid or perform independently. This article proposes a tool for optimal sizing of microgrid-independent systems via respective analysis. To show such an analysis, the type of power generation, number of panels, battery capacity, microgrid size, and group of available consumers should be considered. Therefore, the optimization of different design scenarios is based on number of solar panels and super saving sources, ranges of the depth of discharges, to calculate size and estimate the overall cost. Generally, it is observed that there is an inverse relationship between the depth spectrum of discharge and the solar microgrid costs.

Keywords: Storage, super-storage, grid-independent, economic factors, microgrid.

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Authors: Ayub Khan, Net Ram Garg, Geeta Jain

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In this paper, Backstepping method is proposed to synchronize two fractional-order systems. The simulation results show that this method can effectively synchronize two chaotic systems.

Keywords: Backstepping method, Fractional order, Synchronization.

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Authors: Jimeng Shi, Mahek Jain, Giri Narasimhan

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Time Series Forecasting (TSF) is used to predict the target variables at a future time point based on the learning from previous time points. To keep the problem tractable, learning methods use data from a fixed length window in the past as an explicit input. In this paper, we study how the performance of predictive models change as a function of different look-back window sizes and different amounts of time to predict into the future. We also consider the performance of the recent attention-based transformer models, which had good success in the image processing and natural language processing domains. In all, we compare four different deep learning methods (Recurrent Neural Network (RNN), Long Short-term Memory (LSTM), Gated Recurrent Units (GRU), and Transformer) along with a baseline method. The dataset (hourly) we used is the Beijing Air Quality Dataset from the website of University of California, Irvine (UCI), which includes a multivariate time series of many factors measured on an hourly basis for a period of 5 years (2010-14). For each model, we also report on the relationship between the performance and the look-back window sizes and the number of predicted time points into the future. Our experiments suggest that Transformer models have the best performance with the lowest Mean   Absolute Errors (MAE = 14.599, 23.273) and Root Mean Square Errors (RSME = 23.573, 38.131) for most of our single-step and multi-steps predictions. The best size for the look-back window to predict 1 hour into the future appears to be one day, while 2 or 4 days perform the best to predict 3 hours into the future.

Keywords: Air quality prediction, deep learning algorithms, time series forecasting, look-back window.

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Authors: Nicolò Vaiana, Giorgio Serino

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In this paper, a one-dimensional (1d) Parallel Elasto- Plastic Model (PEPM), able to simulate the uniaxial dynamic behavior of seismic isolators having a continuously decreasing tangent stiffness with increasing displacement, is presented. The parallel modeling concept is applied to discretize the continuously decreasing tangent stiffness function, thus allowing to simulate the dynamic behavior of seismic isolation bearings by putting linear elastic and nonlinear elastic-perfectly plastic elements in parallel. The mathematical model has been validated by comparing the experimental force-displacement hysteresis loops, obtained testing a helical wire rope isolator and a recycled rubber-fiber reinforced bearing, with those predicted numerically. Good agreement between the simulated and experimental results shows that the proposed model can be an effective numerical tool to predict the forcedisplacement relationship of seismic isolators within relatively large displacements. Compared to the widely used Bouc-Wen model, the proposed one allows to avoid the numerical solution of a first order ordinary nonlinear differential equation for each time step of a nonlinear time history analysis, thus reducing the computation effort, and requires the evaluation of only three model parameters from experimental tests, namely the initial tangent stiffness, the asymptotic tangent stiffness, and a parameter defining the transition from the initial to the asymptotic tangent stiffness.

Keywords: Base isolation, earthquake engineering, parallel elasto-plastic model, seismic isolators, softening hysteresis loops.

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Authors: Lukas Horny, Rudolf Zitny, Hynek Chlup, Tomas Adamek, Michal Sara

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An inflation–extension test with human vena cava inferior was performed with the aim to fit a material model. The vein was modeled as a thick–walled tube loaded by internal pressure and axial force. The material was assumed to be an incompressible hyperelastic fiber reinforced continuum. Fibers are supposed to be arranged in two families of anti–symmetric helices. Considered anisotropy corresponds to local orthotropy. Used strain energy density function was based on a concept of limiting strain extensibility. The pressurization was comprised by four pre–cycles under physiological venous loading (0 – 4kPa) and four cycles under nonphysiological loading (0 – 21kPa). Each overloading cycle was performed with different value of axial weight. Overloading data were used in regression analysis to fit material model. Considered model did not fit experimental data so good. Especially predictions of axial force failed. It was hypothesized that due to nonphysiological values of loading pressure and different values of axial weight the material was not preconditioned enough and some damage occurred inside the wall. A limiting fiber extensibility parameter Jm was assumed to be in relation to supposed damage. Each of overloading cycles was fitted separately with different values of Jm. Other parameters were held the same. This approach turned out to be successful. Variable value of Jm can describe changes in the axial force – axial stretch response and satisfy pressure – radius dependence simultaneously.

Keywords: Constitutive model, damage, fiber reinforcedcomposite, limiting fiber extensibility, preconditioning, vena cavainferior.

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Authors: Navdeep Goel, Salvador Gabarda

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Digital images are widely used in computer applications. To store or transmit the uncompressed images requires considerable storage capacity and transmission bandwidth. Image compression is a means to perform transmission or storage of visual data in the most economical way. This paper explains about how images can be encoded to be transmitted in a multiplexing time-frequency domain channel. Multiplexing involves packing signals together whose representations are compact in the working domain. In order to optimize transmission resources each 4 × 4 pixel block of the image is transformed by a suitable polynomial approximation, into a minimal number of coefficients. Less than 4 × 4 coefficients in one block spares a significant amount of transmitted information, but some information is lost. Different approximations for image transformation have been evaluated as polynomial representation (Vandermonde matrix), least squares + gradient descent, 1-D Chebyshev polynomials, 2-D Chebyshev polynomials or singular value decomposition (SVD). Results have been compared in terms of nominal compression rate (NCR), compression ratio (CR) and peak signal-to-noise ratio (PSNR) in order to minimize the error function defined as the difference between the original pixel gray levels and the approximated polynomial output. Polynomial coefficients have been later encoded and handled for generating chirps in a target rate of about two chirps per 4 × 4 pixel block and then submitted to a transmission multiplexing operation in the time-frequency domain.

Keywords: Chirp signals, Image multiplexing, Image transformation, Linear canonical transform, Polynomial approximation.

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Authors: Mehrnoush Shamsfard, Azadeh Nematzadeh, Sarah Motiee

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Increasing growth of information volume in the internet causes an increasing need to develop new (semi)automatic methods for retrieval of documents and ranking them according to their relevance to the user query. In this paper, after a brief review on ranking models, a new ontology based approach for ranking HTML documents is proposed and evaluated in various circumstances. Our approach is a combination of conceptual, statistical and linguistic methods. This combination reserves the precision of ranking without loosing the speed. Our approach exploits natural language processing techniques for extracting phrases and stemming words. Then an ontology based conceptual method will be used to annotate documents and expand the query. To expand a query the spread activation algorithm is improved so that the expansion can be done in various aspects. The annotated documents and the expanded query will be processed to compute the relevance degree exploiting statistical methods. The outstanding features of our approach are (1) combining conceptual, statistical and linguistic features of documents, (2) expanding the query with its related concepts before comparing to documents, (3) extracting and using both words and phrases to compute relevance degree, (4) improving the spread activation algorithm to do the expansion based on weighted combination of different conceptual relationships and (5) allowing variable document vector dimensions. A ranking system called ORank is developed to implement and test the proposed model. The test results will be included at the end of the paper.

Keywords: Document ranking, Ontology, Spread activation algorithm, Annotation.

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Authors: Farhad Asadi, S. Hossein Sadati

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This paper presents a prediction performance of feedforward Multilayer Perceptron (MLP) and Echo State Networks (ESN) trained with extended Kalman filter. Feedforward neural networks and ESN are powerful neural networks which can track and predict nonlinear signals. However, their tracking performance depends on the specific signals or data sets, having the risk of instability accompanied by large error. In this study we explore this process by applying different network size and leaking rate for prediction of nonlinear or chaotic signals in MLP neural networks. Major problems of ESN training such as the problem of initialization of the network and improvement in the prediction performance are tackled. The influence of coefficient of activation function in the hidden layer and other key parameters are investigated by simulation results. Extended Kalman filter is employed in order to improve the sequential and regulation learning rate of the feedforward neural networks. This training approach has vital features in the training of the network when signals have chaotic or non-stationary sequential pattern. Minimization of the variance in each step of the computation and hence smoothing of tracking were obtained by examining the results, indicating satisfactory tracking characteristics for certain conditions. In addition, simulation results confirmed satisfactory performance of both of the two neural networks with modified parameterization in tracking of the nonlinear signals.

Keywords: Feedforward neural networks, nonlinear signal prediction, echo state neural networks approach, leaking rates, capacity of neural networks.

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Authors: Wichai Pattanapol, Sarah J. Wakes, Michael J. Hilton, Katharine J.M. Dickinson

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Turbulence modeling of large-scale flow over a vegetated surface is complex. Such problems involve large scale computational domains, while the characteristics of flow near the surface are also involved. In modeling large scale flow, surface roughness including vegetation is generally taken into account by mean of roughness parameters in the modified law of the wall. However, the turbulence structure within the canopy region cannot be captured with this method, another method which applies source/sink terms to model plant drag can be used. These models have been developed and tested intensively but with a simple surface geometry. This paper aims to compare the use of roughness parameter, and additional source/sink terms in modeling the effect of plant drag on wind flow over a complex vegetated surface. The RNG k-ε turbulence model with the non-equilibrium wall function was tested with both cases. In addition, the k-ω turbulence model, which is claimed to be computationally stable, was also investigated with the source/sink terms. All numerical results were compared to the experimental results obtained at the study site Mason Bay, Stewart Island, New Zealand. In the near-surface region, it is found that the results obtained by using the source/sink term are more accurate than those using roughness parameters. The k-ω turbulence model with source/sink term is more appropriate as it is more accurate and more computationally stable than the RNG k-ε turbulence model. At higher region, there is no significant difference amongst the results obtained from all simulations.

Keywords: CFD, canopy flow, surface roughness, turbulence models.

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Authors: Ragi Poyyara, Vivek V., Ashish Khaparde

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The product development process (PDP) in the Technology group plays a very important role in the launch of any product. While a manufacturing process encourages the use of certain measures to reduce health, safety and environmental (HSE) risks on the shop floor, the PDP concentrates on the use of Geometric Dimensioning and Tolerancing (GD&T) to develop a flawless design. Furthermore, PDP distributes and coordinates activities between different departments such as marketing, purchasing, and manufacturing. However, it is seldom realized that PDP makes a significant contribution to developing a product that reduces HSE risks by encouraging the Technology group to use effective GD&T. The GD&T is a precise communication tool that uses a set of symbols, rules, and definitions to mathematically define parts to be manufactured. It is a quality assurance method widely used in the oil and gas sector. Traditionally it is used to ensure the interchangeability of a part without affecting its form, fit, and function. Parts that do not meet these requirements are rejected during quality audits. This paper discusses how the Technology group integrates this quality assurance tool into the PDP and how the tool plays a major role in helping the HSE department in its goal towards eliminating HSE incidents. The PDP involves a thorough risk assessment and establishes a method to address those risks during the design stage. An illustration shows how GD&T helped reduce safety risks by ergonomically improving assembling operations. A brief discussion explains how tolerances provided on a part help prevent finger injury. This tool has equipped Technology to produce fixtures, which are used daily in operations as well as manufacturing. By applying GD&T to create good fits, HSE risks are mitigated for operating personnel. Both customers and service providers benefit from reduced safety risks.

Keywords: HSE, PDP, GD&T, risks.

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Authors: Achela K. Fernando, Xiujuan Zhang, Peter F. Kinley

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A feed-forward, back-propagation Artificial Neural Network (ANN) model has been used to forecast the occurrences of wastewater overflows in a combined sewerage reticulation system. This approach was tested to evaluate its applicability as a method alternative to the common practice of developing a complete conceptual, mathematical hydrological-hydraulic model for the sewerage system to enable such forecasts. The ANN approach obviates the need for a-priori understanding and representation of the underlying hydrological hydraulic phenomena in mathematical terms but enables learning the characteristics of a sewer overflow from the historical data. The performance of the standard feed-forward, back-propagation of error algorithm was enhanced by a modified data normalizing technique that enabled the ANN model to extrapolate into the territory that was unseen by the training data. The algorithm and the data normalizing method are presented along with the ANN model output results that indicate a good accuracy in the forecasted sewer overflow rates. However, it was revealed that the accurate forecasting of the overflow rates are heavily dependent on the availability of a real-time flow monitoring at the overflow structure to provide antecedent flow rate data. The ability of the ANN to forecast the overflow rates without the antecedent flow rates (as is the case with traditional conceptual reticulation models) was found to be quite poor.

Keywords: Artificial Neural Networks, Back-propagationlearning, Combined sewer overflows, Forecasting.

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Authors: Ioannis P. Panagopoulos, Christos C. Pavlatos, George K. Papakonstantinou

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Conventional approaches in the implementation of logic programming applications on embedded systems are solely of software nature. As a consequence, a compiler is needed that transforms the initial declarative logic program to its equivalent procedural one, to be programmed to the microprocessor. This approach increases the complexity of the final implementation and reduces the overall system's performance. On the contrary, presenting hardware implementations which are only capable of supporting logic programs prevents their use in applications where logic programs need to be intertwined with traditional procedural ones, for a specific application. We exploit HW/SW codesign methods to present a microprocessor, capable of supporting hybrid applications using both programming approaches. We take advantage of the close relationship between attribute grammar (AG) evaluation and knowledge engineering methods to present a programmable hardware parser that performs logic derivations and combine it with an extension of a conventional RISC microprocessor that performs the unification process to report the success or failure of those derivations. The extended RISC microprocessor is still capable of executing conventional procedural programs, thus hybrid applications can be implemented. The presented implementation is programmable, supports the execution of hybrid applications, increases the performance of logic derivations (experimental analysis yields an approximate 1000% increase in performance) and reduces the complexity of the final implemented code. The proposed hardware design is supported by a proposed extended C-language called C-AG.

Keywords: Attribute Grammars, Logic Programming, RISC microprocessor.

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Authors: Guanglin Song

Abstract:

Situated within the context of China's tiered medical treatment system, this study aims to analyze spatial causes of urban healthcare access difficulties from the perspective of the configuration of healthcare facilities. A social network analysis approach is employed to construct a healthcare demand and supply flow network between major residential clusters and various tiers of hospitals in the city. The findings reveal that: 1) There exists overall maldistribution and over-concentration of healthcare resources in the study area, characterized by structural imbalance. 2) The low rate of primary care utilization in the study area is a key factor contributing to congestion at higher-tier hospitals, as excessive reliance on these institutions by neighboring communities exacerbates the problem. 3) Gradual optimization of the healthcare facility layout in the study area, encompassing holistic, local, and individual institutional levels, can enhance systemic efficiency and resource balance. This research proposes a method for evaluating urban healthcare resource distribution structures based on service flows within hierarchical networks. It offers spatially targeted optimization suggestions for promoting the implementation of the tiered healthcare system and alleviating challenges related to accessibility and congestion in seeking medical care. In addition, the study provides some new ideas for researchers and healthcare managers in countries, cities, and healthcare management around the world with similar challenges.

Keywords: Flow of public services, healthcare facilities, spatial planning, urban networks.

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Authors: M. Yilmaz, I. Yilmaz, M. Uysal

Abstract:

The Earth system generates different phenomena that are observable at the surface of the Earth such as mass deformations and displacements leading to plate tectonics, earthquakes, and volcanism. The dynamic processes associated with the interior, surface, and atmosphere of the Earth affect the three pillars of geodesy: shape of the Earth, its gravity field, and its rotation. Geodesy establishes a characteristic structure in order to define, monitor, and predict of the whole Earth system. The traditional and new instruments, observables, and techniques in geodesy are related to the gravity field. Therefore, the geodesy monitors the gravity field and its temporal variability in order to transform the geodetic observations made on the physical surface of the Earth into the geometrical surface in which positions are mathematically defined. In this paper, the main components of the gravity field modeling, (Free-air and Bouguer) gravity anomalies are calculated via recent global models (EGM2008, EIGEN6C4, and GECO) over a selected study area. The model-based gravity anomalies are compared with the corresponding terrestrial gravity data in terms of standard deviation (SD) and root mean square error (RMSE) for determining the best fit global model in the study area at a regional scale in Turkey. The least SD (13.63 mGal) and RMSE (15.71 mGal) were obtained by EGM2008 for the Free-air gravity anomaly residuals. For the Bouguer gravity anomaly residuals, EIGEN6C4 provides the least SD (8.05 mGal) and RMSE (8.12 mGal). The results indicated that EIGEN6C4 can be a useful tool for modeling the gravity field of the Earth over the study area.

Keywords: Free-air gravity anomaly, Bouguer gravity anomaly, global model, land gravity.

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Authors: Prasanta Kundu, N.K. Kishore, A.K. Sinha

Abstract:

Insulation used in transformer is mostly oil pressboard insulation. Insulation failure is one of the major causes of catastrophic failure of transformers. It is established that partial discharges (PD) cause insulation degradation and premature failure of insulation. Online monitoring of PDs can reduce the risk of catastrophic failure of transformers. There are different techniques of partial discharge measurement like, electrical, optical, acoustic, opto-acoustic and ultra high frequency (UHF). Being non invasive and non interference prone, acoustic emission technique is advantageous for online PD measurement. Acoustic detection of p.d. is based on the retrieval and analysis of mechanical or pressure signals produced by partial discharges. Partial discharges are classified according to the origin of discharges. Their effects on insulation deterioration are different for different types. This paper reports experimental results and analysis for classification of partial discharges using acoustic emission signal of laboratory simulated partial discharges in oil pressboard insulation system using three different electrode systems. Acoustic emission signal produced by PD are detected by sensors mounted on the experimental tank surface, stored on an oscilloscope and fed to computer for further analysis. The measured AE signals are analyzed using discrete wavelet transform analysis and wavelet packet analysis. Energy distribution in different frequency bands of discrete wavelet decomposed signal and wavelet packet decomposed signal is calculated. These analyses show a distinct feature useful for PD classification. Wavelet packet analysis can sort out any misclassification arising out of DWT in most cases.

Keywords: Acoustic emission, discrete wavelet transform, partial discharge, wavelet packet analysis.

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Authors: Sushil Kumar Swar, Sanjay Kumar Sharma, Hari Krishan Sharma, Sushil Kumar

Abstract:

The seriously damaged structures during earthquakes show the need and importance of design of reinforced concrete structures with high ductility. Reinforced concrete beam-column joints have an important function in all structures. Under seismic excitation, the beam column joint region is subjected to horizontal and vertical shear forces whose magnitude is many times higher than the adjacent beam and column. Strength and ductility of structures depends mainly on proper detailing of the reinforcement in beamcolumn joints and the old structures were found ductility deficient. DSP materials are obtained by using high quantities of super plasticizers and high volumes of micro silica. In the case of High Performance Densified Small Particle Concrete (HPDSPC), since concrete is dense even at the micro-structure level, tensile strain would be much higher than that of the conventional SFRC, SIFCON & SIMCON. This in turn will improve cracking behaviour, ductility and energy absorption capacity of composites in addition to durability. The fine fibers used in our mix are 0.3mm diameter and 10 mm which can be easily placed with high percentage. These fibers easily transfer stresses and act as a composite concrete unit to take up extremely high loads with high compressive strength. HPDSPC placed in the beam column joints helps in safety of human life due to prolonged failure.

Keywords: High Performance Densified Small Particle Concrete (HPDSPC), Steel Fıber Reinforced Concrete (SFRC), Slurry Infiltrated Concrete (SIFCON), Slurry Infiltrated Mat Concrete (SIMCON).

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Authors: S. Deswal, M. Pal

Abstract:

Presently various computational techniques are used in modeling and analyzing environmental engineering data. In the present study, an intra-comparison of polynomial and radial basis kernel functions based on Support Vector Regression and, in turn, an inter-comparison with Multi Linear Regression has been attempted in modeling mass transfer capacity of vertical (θ = 90O) and inclined (θ multiple plunging jets (varying from 1 to 16 numbers). The data set used in this study consists of four input parameters with a total of eighty eight cases, forty four each for vertical and inclined multiple plunging jets. For testing, tenfold cross validation was used. Correlation coefficient values of 0.971 and 0.981 along with corresponding root mean square error values of 0.0025 and 0.0020 were achieved by using polynomial and radial basis kernel functions based Support Vector Regression respectively. An intra-comparison suggests improved performance by radial basis function in comparison to polynomial kernel based Support Vector Regression. Further, an inter-comparison with Multi Linear Regression (correlation coefficient = 0.973 and root mean square error = 0.0024) reveals that radial basis kernel functions based Support Vector Regression performs better in modeling and estimating mass transfer by multiple plunging jets.

Keywords: Mass transfer, multiple plunging jets, polynomial and radial basis kernel functions, Support Vector Regression.

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Authors: Mohammed Sani Abdulai

Abstract:

Government budgets are the primary instruments for formulating and implementing a country’s fiscal policy objectives, development priorities, and the overall socio-economic aspirations of its people. Thus, in this paper, the author examined the Government of Ghana’s budgets with respect to their functions, coverage, classifications, and integration with the country’s chart of accounts. The author did so by amalgamating the research findings of extant literature with (a) the operational and procedural guidelines underpinning the formulation and execution of the government’s budgets; (b) the recommendations made by various development partners and thinktanks on reforming the country’s budgeting processes and procedures; and (c) the lessons Ghana could learn from the budget reform efforts of other countries. By way of research findings, the paper showed that the Government of Ghana’s budgets in terms of function are both eclectic and multidimensional. On coverage, the paper showed that the country’s budgets duly cover the revenues and expenditures of the general government (i.e., both the central and sub-national governments). Finally, on classifications, the paper noted with delight the Government of Ghana’s effort in providing classificatory codes to both its national development agenda and such international development goals as the AU’s Agenda 2063 and the UN’s Sustainable Development Goals. However, the paper found some significant lapses that require a complete overhaul and structuring on the integrations of its budget classifications with its chart of accounts. Thus, the paper concluded with a detailed examination of the challenges confronting the country’s current chart of accounts and recommendations for addressing them.

Keywords: Budget, budgetary transactions, budgetary governance, Chart of Accounts, classification, composition, coverage, Public Financial Management.

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