Search results for: estimation of inputs and outputs

Authors: Asli Tiktas, Huseyin Gunerhan, Arif Hepbasli

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Solar energy, with its abundant and clean features, is one of the prominent renewable energy sources in multigeneration energy systems where various outputs, especially power generation, are produced together. In the literature, concentrated solar energy systems, which are an expensive technology, are mostly used in solar power plants where medium-high capacity production outputs are achieved. In addition, although different methods have been developed and proposed for solar energy-supported integrated power generation systems by different investigators, absorption technology, which is one of the key points of the present study, has been used extensively in cooling systems in these studies. Unlike these common uses mentioned in the literature, this study designs a system in which a flat plate solar collector (FPSC), Rankine cycle, absorption heat transformer (AHT), and cooling systems (ACS) are integrated. The system proposed within the scope of this study aims to produce medium-high-capacity electricity, heating, and cooling outputs using a technique different from the literature, with lower production costs than existing systems. With the proposed integrated system design, the average production costs based on electricity, heating, and cooling load production for similar scale systems are 5-10% of the average production costs of 0.685 USD/kWh, 0.247 USD/kWh, and 0.342 USD/kWh. In the proposed integrated system design, this will be achieved by increasing the outlet temperature of the AHT and FPSC system first, expanding the high-temperature steam coming out of the absorber of the AHT system in the turbine up to the condenser temperature of the ACS system, and next directly integrating it into the evaporator of this system and then completing the AHT cycle. Through this proposed system, heating and cooling will be carried out by completing the AHT and ACS cycles, respectively, while power generation will be provided because of the expansion of the turbine. Using only a single generator in the production of these three outputs together, the costs of additional boilers and the need for a heat source are also saved. In order to demonstrate that the system proposed in this study offers a more optimum solution, the techno-economic parameters obtained based on energy, exergy, economic, and environmental analysis were compared with the parameters of similar scale systems in the literature. The design parameters of the proposed system were determined through a parametric optimization study to exceed the maximum efficiency and effectiveness and reduce the production cost rate values of the compared systems.

Keywords: solar energy, absorption technology, Rankine cycle, multigeneration energy system

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Authors: H. Bakhshi, E. Khayyamian

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Cognitive radios have been recognized as one of the most promising technologies dealing with the scarcity of the radio spectrum. In cognitive radio systems, secondary users are allowed to utilize the frequency bands of primary users when the bands are idle. Hence, how to accurately detect the idle frequency bands has attracted many researchers’ interest. Detection performance is sensitive toward noise power and gain fluctuation. Since signal to noise ratio (SNR) between primary user and secondary users are not the same and change over the time, SNR and noise power estimation is essential. In this paper, we present a cooperative spectrum sensing algorithm using SNR estimation to improve detection performance in the real situation.

Keywords: cognitive radio, cooperative spectrum sensing, energy detection, SNR estimation, spectrum sensing, rayleigh fading channel

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Authors: Xuemei Jiang, Kunfu Zhu, Shouyang Wang

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In this paper, we selected China as a case and employ a time-series of unique input-output tables distinguishing firm ownership and processing exports, to evaluate the role of foreign-invested enterprises (FIEs) in China’s rapid carbon dioxide emission growth. The results suggested that FIEs contributed to 11.55% of the economic outputs’ growth in China between 1992-2010, but accounted for only 9.65% of the growth of carbon dioxide emissions. In relative term, until 2010 FIEs still emitted much less than Chinese-owned enterprises (COEs) when producing the same amount of outputs, although COEs experienced much faster technology upgrades. In an ideal scenario where we assume the final demands remain unchanged and COEs completely mirror the advanced technologies of FIEs, more than 2000 Mt of carbon dioxide emissions would be reduced for China in 2010. From a policy perspective, the widespread FIEs are very effective and efficient channel to encourage technology transfer from developed to developing countries.

Keywords: carbon dioxide emissions, foreign-invested enterprises, technology transfer, input–output analysis, China

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Authors: Reem I. Al-Ya' Goub, Nasim Shatarat

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This paper presents the results of analytical and comparative study among software programs' outputs in analysis of some two way solid slabs; flat plate, flat slab with beams and flat slab with drop panels problems that already been analyzed using Classical Equivalent Frame Method (CEFM) by several reinforced concrete book authors. The primary objective of this research is to determine the moment results using various software programs. Then, a summary of the results and differences percentages were obtained to show how analysis procedure effects the outputs of calculations that vary from software program to another when comparing them with the results of CEFM. Moment values were obtained using either the Equivalent Frame Method (EFM) or Finite Element Method (FEM) that's used among many software programs. The results of the analyses demonstrate that software programs vary markedly in terms of the information they provide to the structural designer regarding values of the model insertion, stiffness, effective moment of inertia used and specially the moment values.

Keywords: two-way solid slabs, flat plate, flat slab with beams, flat slab with drop panels, analysis, modeling, EFM, CEFM, FEM

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Authors: Sabri Sulaiman, Siti Hajar Abu Bakar Ah, Haris Abd Wahab

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Street children is a global phenomenon and declared as a social problem by social researcher and scholars across the world. The insecure street environment exposes street children into various risk factors. One of them is the health and psychological problem. The objective of this study is to assess the health problem and psychological wellbeing of street children in Kuala Lumpur, Malaysia. The cross-sectional study involved 303 street children in Chow Kit, Kuala Lumpur. The study confirmed that the majority (95.7%) of street children who participated in the study have a health problem. The findings also demonstrated that the majority of them have issues related to their psychological wellbeing. The inputs from this study are instrumental for the suggestion of specific intervention to improve the health and psychology wellbeing of street children in Malaysia. Agencies which are responsible for the street children well-being can utilise the inputs to framing and improving the social care programmes for the children.

Keywords: street children, health status, psychology wellbeing, homeless

Procedia PDF Downloads 164

Authors: H. Tavakoli, S. S. Mohtasebi, R. Alimardani, R. Gebbers

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Nitrogen (N) is one of the most important agricultural inputs affecting crop growth, yield and quality in rain-fed cereal production. N demand of crops varies spatially across fields due to spatial differences in soil conditions. In addition, the response of a crop to the fertilizer applications is heavily reliant on plant available water. Matching N supply to water availability is thus essential to achieve an optimal crop response. The objective of this study was to determine effect of drought stress on estimation of nitrogen status of winter wheat by some vegetation indices. During the 2012 growing season, a field experiment was conducted at the Bundessortenamt (German Plant Variety Office) Marquardt experimental station which is located in the village of Marquardt about 5 km northwest of Potsdam, Germany (52°27' N, 12°57' E). The experiment was designed as a randomized split block design with two replications. Treatments consisted of four N fertilization rates (0, 60, 120 and 240 kg N ha-1, in total) and two water regimes (irrigated (Irr) and non-irrigated (NIrr)) in total of 16 plots with dimension of 4.5 × 9.0 m. The indices were calculated using readings of a spectroradiometer made of tec5 components. The main parts were two “Zeiss MMS1 nir enh” diode-array sensors with a nominal rage of 300 to 1150 nm with less than 10 nm resolutions and an effective range of 400 to 1000 nm. The following vegetation indices were calculated: NDVI, GNDVI, SR, MSR, NDRE, RDVI, REIP, SAVI, OSAVI, MSAVI, and PRI. All the experiments were conducted during the growing season in different plant growth stages including: stem elongation (BBCH=32-41), booting stage (BBCH=43), inflorescence emergence, heading (BBCH=56-58), flowering (BBCH=65-69), and development of fruit (BBCH=71). According to the results obtained, among the indices, NDRE and REIP were less affected by drought stress and can provide reliable wheat nitrogen status information, regardless of water status of the plant. They also showed strong relations with nitrogen status of winter wheat.

Keywords: nitrogen status, drought stress, vegetation indices, precision agriculture

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Authors: Raghunath Arnab

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The two-phase sampling which is also known as double sampling was introduced in 1938. In two-phase sampling, samples are selected in phases. In the first phase, a relatively large sample of size is selected by some suitable sampling design and only information on the auxiliary variable is collected. During the second phase, a sample of size is selected either from, the sample selected in the first phase or from the entire population by using a suitable sampling design and information regarding the study and auxiliary variable is collected. Evidently, two phase sampling is useful if the auxiliary information is relatively easy and cheaper to collect than the study variable as well as if the strength of the relationship between the variables and is high. If the sample is selected in more than two phases, the resulting sampling design is called a multi-phase sampling. In this article we will consider how one can use data collected at the first phase sampling at the stages of estimation of the parameter, stratification, selection of sample and their combinations in the second phase in a unified setup applicable to any sampling design and wider classes of estimators. The problem of the estimation of variance will also be considered. The variance of estimator is essential for estimating precision of the survey estimates, calculation of confidence intervals, determination of the optimal sample sizes and for testing of hypotheses amongst others. Although, the variance is a non-negative quantity but its estimators may not be non-negative. If the estimator of variance is negative, then it cannot be used for estimation of confidence intervals, testing of hypothesis or measure of sampling error. The non-negativity properties of the variance estimators will also be studied in details.

Keywords: auxiliary information, two-phase sampling, varying probability sampling, unbiased estimators

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Authors: J. C. M. Santos, J. C. A. Sousa, A. J. Rubio, L. S. Soletti, F. Gasparotto, N. U. Yamaguchi

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Magnetic graphene has received widespread attention for their capability of water and wastewater treatment, which has been attracted many researchers in this field. A bibliometric analysis based on the Web of Science database was employed to analyze the global scientific outputs of magnetic graphene for water treatment until the present time (2012 to 2017), to improve the understanding of the research trends. The publication year, place of publication, institutes, funding agencies, journals, most cited articles, distribution outputs in thematic categories and applications were analyzed. Three major aspects analyzed including type of pollutant, treatment process and composite composition have further contributed to revealing the research trends. The most relevant research aspects of the main technologies using magnetic graphene for water treatment were summarized in this paper. The results showed that research on magnetic graphene for water treatment goes through a period of decline that might be related to a saturated field and a lack of bibliometric studies. Thus, the result of the present work will lead researchers to establish future directions in further studies using magnetic graphene for water treatment.

Keywords: composite, graphene oxide, nanomaterials, scientometrics

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Authors: Arpita Mondal, Aurobinda Routray, Sreeraj Puravankara, Rajashree Biswas

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The most difficult part of designing a battery management system (BMS) is battery modeling. A good battery model can capture the dynamics which helps in energy management, by accurate model-based state estimation algorithms. So far the most suitable and fruitful model is the equivalent circuit model (ECM). However, in real-time applications, the model parameters are time-varying, changes with current, temperature, state of charge (SOC), and aging of the battery and this make a great impact on the performance of the model. Therefore, to increase the equivalent circuit model performance, the parameter estimation has been carried out in the frequency domain. The battery is a very complex system, which is associated with various chemical reactions and heat generation. Therefore, it’s very difficult to select the optimal model structure. As we know, if the model order is increased, the model accuracy will be improved automatically. However, the higher order model will face the tendency of over-parameterization and unfavorable prediction capability, while the model complexity will increase enormously. In the time domain, it becomes difficult to solve higher order differential equations as the model order increases. This problem can be resolved by frequency domain analysis, where the overall computational problems due to ill-conditioning reduce. In the frequency domain, several dominating frequencies can be found in the input as well as output data. The selective frequency domain estimation has been carried out, first by estimating the frequencies of the input and output by subspace decomposition, then by choosing the specific bands from the most dominating to the least, while carrying out the least-square, recursive least square and Kalman Filter based parameter estimation. In this paper, a second order battery model consisting of three resistors, two capacitors, and one SOC controlled voltage source has been chosen. For model identification and validation hybrid pulse power characterization (HPPC) tests have been carried out on a 2.6 Ah LiFePO₄ battery.

Keywords: equivalent circuit model, frequency estimation, parameter estimation, subspace decomposition

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Authors: Abdel-Aziz M. Mohamed

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This research proposes a methodology to estimate the customer order lead time in the supply chain based on the process capability index. The cases when the process output is normally distributed and when it is not are considered. The relationships between the system capability indices in both service and manufacturing applications, delivery system reliability and the percentages of orders delivered after their promised due dates are presented. The proposed method can be used to examine the current process capability to deliver the orders before the promised lead-time. If the system was found to be incapable, the method can be used to help revise the current lead-time to a proper value according to the service reliability level selected by the management. Numerical examples and a case study describing the lead time estimation methodology and testing the system capability of delivering the orders before their promised due date are illustrated.

Keywords: lead-time estimation, process capability index, delivery system reliability, statistical analysis, service achievement index, service quality

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Authors: Takashi Shimizu, Tomoaki Hashimoto

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Recently, crystal growth technologies have made progress by the requirement for the high quality of crystal materials. To control the crystal growth dynamics actively by external forces is useuful for reducing composition non-uniformity. In this study, a control method based on model predictive control using thermal inputs is proposed for crystal growth dynamics of semiconductor materials. The control system of crystal growth dynamics considered here is governed by the continuity, momentum, energy, and mass transport equations. To establish the control method for such thermal fluid systems, we adopt model predictive control known as a kind of optimal feedback control in which the control performance over a finite future is optimized with a performance index that has a moving initial time and terminal time. The objective of this study is to establish a model predictive control method for crystal growth dynamics of semiconductor materials.

Keywords: model predictive control, optimal control, process control, crystal growth

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Authors: Yemane Hailu Fissuh, Zhongzhan Zhang

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In biomedical researches and randomized clinical trials, the most commonly interested outcomes are time-to-event so-called survival data. The importance of robust models in this context is to compare the effect of randomly controlled experimental groups that have a sense of causality. Causal estimation is the scientific concept of comparing the pragmatic effect of treatments conditional to the given covariates rather than assessing the simple association of response and predictors. Hence, the causal effect based semiparametric transformation model was proposed to estimate the effect of treatment with the presence of possibly time-varying covariates. Due to its high flexibility and robustness, the semiparametric transformation model which shall be applied in this paper has been given much more attention for estimation of a causal effect in modeling left-truncated and right censored survival data. Despite its wide applications and popularity in estimating unknown parameters, the maximum likelihood estimation technique is quite complex and burdensome in estimating unknown parameters and unspecified transformation function in the presence of possibly time-varying covariates. Thus, to ease the complexity we proposed the modified estimating equations. After intuitive estimation procedures, the consistency and asymptotic properties of the estimators were derived and the characteristics of the estimators in the finite sample performance of the proposed model were illustrated via simulation studies and Stanford heart transplant real data example. To sum up the study, the bias of covariates was adjusted via estimating the density function for truncation variable which was also incorporated in the model as a covariate in order to relax the independence assumption of failure time and truncation time. Moreover, the expectation-maximization (EM) algorithm was described for the estimation of iterative unknown parameters and unspecified transformation function. In addition, the causal effect was derived by the ratio of the cumulative hazard function of active and passive experiments after adjusting for bias raised in the model due to the truncation variable.

Keywords: causal estimation, EM algorithm, semiparametric transformation models, time-to-event outcomes, time-varying covariate

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Authors: Farzaneh Khorram

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This paper endeavors to construct a block model to assess grinding energy consumption (CCE) and pinpoint blocks with the highest potential for energy usage during the grinding process within a specified region. Leveraging geostatistical techniques, particularly joint estimation, or simulation, based on geometallurgical data from various mineral processing stages, our objective is to forecast CCE across the study area. The dataset encompasses variables obtained from 2754 drill samples and a block model comprising 4680 blocks. The initial analysis encompassed exploratory data examination, variography, multivariate analysis, and the delineation of geological and structural units. Subsequent analysis involved the assessment of contacts between these units and the estimation of CCE via cokriging, considering its correlation with SPI. The selection of blocks exhibiting maximum CCE holds paramount importance for cost estimation, production planning, and risk mitigation. The study conducted exploratory data analysis on lithology, rock type, and failure variables, revealing seamless boundaries between geometallurgical units. Simulation methods, such as Plurigaussian and Turning band, demonstrated more realistic outcomes compared to cokriging, owing to the inherent characteristics of geometallurgical data and the limitations of kriging methods.

Keywords: geometallurgy, multivariate analysis, plurigaussian, turning band method, cokriging

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Authors: Shih-Pin Chen, Shih-Syuan You

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This study proposes a regression model with flexible spreads for fuzzy input-output data to cope with the situation that the existing measures cannot reflect the actual estimation error. The main idea is that a dissemblance index (DI) is carefully identified and defined for precisely measuring the actual estimation error. Moreover, the graded mean integration (GMI) representation is adopted for determining more representative numeric regression coefficients. Notably, to comprehensively compare the performance of the proposed model with other ones, three different criteria are adopted. The results from commonly used test numerical examples and an application to Taiwan's business monitoring indicator illustrate that the proposed dissemblance index method not only produces valid fuzzy regression models for fuzzy input-output data, but also has satisfactory and stable performance in terms of the total estimation error based on these three criteria.

Keywords: dissemblance index, forecasting, fuzzy sets, linear regression

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Authors: Harry Aginta

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Using Phillips curve framework, this paper seeks for new empirical evidence on the relationship between inflation and output in a major emerging economy. By exploiting sub-national data, the contribution of this paper is threefold. First, it resolves the issue of using on-target national inflation rates that potentially causes weakening inflation-output nexus. This is very relevant for Indonesia as its central bank has been adopting inflation targeting framework based on national consumer price index (CPI) inflation. Second, the study tests the relevance of mining sector in output gap estimation. The test for mining sector is important to control for the effects of mining regulation and nominal effects of coal prices on real economic activities. Third, the paper applies panel econometric method by incorporating regional variation that help to improve model estimation. The results from this paper confirm the strong presence of Phillips curve in Indonesia. Positive output gap that reflects excess demand condition gives rise to the inflation rates. In addition, the elasticity of output gap is higher if the mining sector is excluded from output gap estimation. In addition to inflation adaptation, the dynamics of exchange rate and international commodity price are also found to affect inflation significantly. The results are robust to the alternative measurement of output gap

Keywords: Phillips curve, inflation, Indonesia, panel data

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Authors: Jiri Barta

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At the present time, awareness, education, computer simulation and information systems protection are very serious and relevant topics. The article deals with perspectives and possibilities of implementation of emergence or natural hazard threats into the system which is developed for communication among members of crisis management staffs. The Czech Hydro-Meteorological Institute with its System of Integrated Warning Service resents the largest usable base of information. National information systems are connected to foreign systems, especially to flooding emergency systems of neighboring countries, systems of European Union and international organizations where the Czech Republic is a member. Use of outputs of particular information systems and computer simulations on a single communication interface of information system for communication among members of crisis management staff and setting the site interoperability in the net will lead to time savings in decision-making processes in solving extraordinary events and crisis situations. Faster managing of an extraordinary event or a crisis situation will bring positive effects and minimize the impact of negative effects on the environment.

Keywords: computer simulation, communication, continuity, critical infrastructure, information systems, safety

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Authors: Tasir Khan, Yejuan Wan, Kalim Ullah

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Hydrological frequency analysis has been conducted to estimate the minimum flow elevation of the Indus River in Pakistan to protect the ecosystem. The Maximum likelihood estimation (MLE) technique is used to estimate the best-fitted distribution for Minimum Ecological Flows at nine stations of the Indus River in Pakistan. The four selected distributions, Generalized Extreme Value (GEV) distribution, Generalized Logistics (GLO) distribution, Generalized Pareto (GPA) distribution, and Pearson type 3 (PE3) are fitted in all sites, usually used in hydro frequency analysis. Compare the performance of these distributions by using the goodness of fit tests, such as the Kolmogorov Smirnov test, Anderson darling test, and chi-square test. The study concludes that the Maximum Likelihood Estimation (MLE) method recommended that GEV and GPA are the most suitable distributions which can be effectively applied to all the proposed sites. The quantiles are estimated for the return periods from 5 to 1000 years by using MLE, estimations methods. The MLE is the robust method for larger sample sizes. The results of these analyses can be used for water resources research, including water quality management, designing irrigation systems, determining downstream flow requirements for hydropower, and the impact of long-term drought on the country's aquatic system.

Keywords: minimum ecological flow, frequency distribution, indus river, maximum likelihood estimation

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Authors: Kyugneun Lee, Ikjin Lee

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Parameter estimation with inverse problem often suffers from unfavorable conditions in the real world. Useless data and many input parameters make the problem complicated or insoluble. Data refinement and reformulation of the problem can solve that kind of difficulties. In this research, a method to solve the rank deficient inverse problem is suggested. A multi-physics system which has rank deficiency caused by response correlation is treated. Impeditive information is removed and the problem is reformulated to sequential estimations using Bayesian network (BN) and subset groups. At first, subset grouping of the responses is performed. Feature selection with singular value decomposition (SVD) is used for the grouping. Next, BN inference is used for sequential conditional estimation according to the group hierarchy. Directed acyclic graph (DAG) structure is organized to maximize the estimation ability. Variance ratio of response to noise is used to pairing the estimable parameters by each response.

Keywords: Bayesian network, feature selection, rank deficiency, statistical inverse analysis

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Authors: Muhammad Jahanzaib, Muhammad Z. A. Khan, Junaid Khayyam

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Islamabad is the capital of Pakistan with the population of 1.365 million people and with a vehicular fleet size of 0.75 million. The vehicular fleet size is growing annually by the rate of 11%. Vehicular emissions are major source of Black carbon (BC). In developing countries like Pakistan, most of the vehicles consume conventional fuels like Petrol, Diesel, and CNG. These fuels are the major emitters of pollutants like CO, CO2, NOx, CH4, VOCs, and particulate matter (PM10). Carbon dioxide and methane are the leading contributor to the global warming with a global share of 9-26% and 4-9% respectively. NOx is the precursor of nitrates which ultimately form aerosols that are noxious to human health. In this study, COPERT (Computer program to Calculate Emissions from Road Transport) was used for vehicular emission estimation in Islamabad. COPERT is a windows based program which is developed for the calculation of emissions from the road transport sector. The emissions were calculated for the year of 2016 include pollutants like CO, NOx, VOC, and PM and energy consumption. The different variable was input to the model for emission estimation including meteorological parameters, average vehicular trip length and respective time duration, fleet configuration, activity data, degradation factor, and fuel effect. The estimated emissions for CO, CH4, CO2, NOx, and PM10 were found to be 9814.2, 44.9, 279196.7, 3744.2 and 304.5 tons respectively.

Keywords: COPERT Model, emission estimation, PM10, vehicular emission

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Authors: Nhat-To Huynh, Chen-Fu Chien

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Textile batch dyeing scheduling problem is complicated which includes batch formation, batch assignment on machines, batch sequencing with sequence-dependent setup time. Most manufacturers schedule their orders manually that are time consuming and inefficient. More power methods are needed to improve the solution. Motivated by the real needs, this study aims to propose approaches in which genetic algorithm is developed with multi-subpopulation and hybridised with estimation of distribution algorithm to solve the constructed problem for minimising the makespan. A heuristic algorithm is designed and embedded into the proposed algorithms to improve the ability to get out of the local optima. In addition, an empirical study is conducted in a textile company in Taiwan to validate the proposed approaches. The results have showed that proposed approaches are more efficient than simulated annealing algorithm.

Keywords: estimation of distribution algorithm, genetic algorithm, multi-subpopulation, scheduling, textile dyeing

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Authors: Lokendra Kumar Devangan, Ajay Mishra

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This paper describes the implementation of a large-scale SAS/OR model with significant pre-processing, scenario analysis, and post-processing work done using SAS. A large cement manufacturer with ten geographically distributed manufacturing plants for two variants of cement, around 400 warehouses serving as transshipment points, and several thousand distributor locations generating demand needed to optimize this multi-echelon, multi-modal transport supply chain separately for planning and allocation purposes. For monthly planning as well as daily allocation, the demand is deterministic. Rail and road networks connect any two points in this supply chain, creating tens of thousands of such connections. Constraints include the plant’s production capacity, transportation capacity, and rail wagon batch size constraints. Each demand point has a minimum and maximum for shipments received. Price varies at demand locations due to local factors. A large mixed integer programming model built using proc OPTMODEL decides production at plants, demand fulfilled at each location, and the shipment route to demand locations to maximize the profit contribution. Using base SAS, we did significant pre-processing of data and created inputs for the optimization. Using outputs generated by OPTMODEL and other processing completed using base SAS, we generated several reports that went into their enterprise system and created tables for easy consumption of the optimization results by operations.

Keywords: production planning, mixed integer optimization, network model, network optimization

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Authors: Zhiyong Zheng, Xu Li, Liang Huang, Zhengliang Sun, Jianhua Xu

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Yaw angle plays a significant role in many vehicle safety applications, such as collision avoidance and lane-keeping system. Although the estimation of the yaw angle has been extensively studied in existing literature, it is still the main challenge to simultaneously achieve a reliable and cost-effective solution in complex urban environments. This paper proposes a multi-stage learning framework to estimate the yaw angle with a monocular camera, which can deal with the challenge in a more reliable manner. In the first stage, an efficient road detection network is designed to extract the road region, providing a highly reliable reference for the estimation. In the second stage, a variational auto-encoder (VAE) is proposed to learn the distribution patterns of road regions, which is particularly suitable for modeling the changing patterns of yaw angle under different driving maneuvers, and it can inherently enhance the generalization ability. In the last stage, a gated recurrent unit (GRU) network is used to capture the temporal correlations of the learned patterns, which is capable to further improve the estimation accuracy due to the fact that the changes of deflection angle are relatively easier to recognize among continuous frames. Afterward, the yaw angle can be obtained by combining the estimated deflection angle and the road direction stored in a roadway map. Through effective multi-stage learning, the proposed framework presents high reliability while it maintains better accuracy. Road-test experiments with different driving maneuvers were performed in complex urban environments, and the results validate the effectiveness of the proposed framework.

Keywords: gated recurrent unit, multi-stage learning, reliable estimation, variational auto-encoder, yaw angle

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Authors: Pyung Soo Kim, Eung Hyuk Lee, Mun Suck Jang

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In the current paper, an indoor positioning system is proposed with consideration of measurement delay. Firstly, an estimation filter with a measurement delay is designed for the indoor positioning mechanism under a weighted least square criterion, which utilizes only finite measurements on the most recent window. The proposed estimation filtering based scheme gives the filtered estimates for position, velocity and acceleration of moving target in real-time, while removing undesired noisy effects and preserving desired moving positions. Secondly, the proposed scheme is shown to have good inherent properties such as unbiasedness, efficiency, time-invariance, deadbeat, and robustness due to the finite memory structure. Finally, computer simulations shows that the performance of the proposed estimation filtering based scheme can outperform to the existing infinite memory filtering based mechanism.

Keywords: indoor positioning system, wireless sensor networks, measurement delay

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Authors: Abdullah Eqal Al Mazrooei

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In this paper, we introduce a mathematical algorithm which is used for estimating the states in the bilinear systems. This algorithm uses a special linearization of the second-order term by using the best available information about the state of the system. This technique makes our algorithm generalizes the well-known Kalman estimators. The system which is used here is of the bilinear class, the evolution of this model is linear-bilinear in the state of the system. Our algorithm can be used with linear and bilinear systems. We also here introduced a real application for the new algorithm to prove the feasibility and the efficiency for it.

Keywords: estimation algorithm, bilinear systems, Kakman filter, second order linearization

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Authors: Naime Boudemagh, Zoheir Hammoudi

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The parameters estimation of the K-distribution is an essential part in radar detection. In fact, presence of interfering targets in reference cells causes a decrease in detection performances. In such situation, the estimate of the shape and the scale parameters are far from the actual values. In the order to avoid interfering targets, we propose an Automatic Censoring (AC) algorithm of radar interfering targets in K-distribution. The censoring technique used in this work offers a good discrimination between homogeneous and non-homogeneous environments. The homogeneous population is then used to estimate the unknown parameters by the classical Method of Moment (MOM). The AC algorithm does not need any prior information about the clutter parameters nor does it require both the number and the position of interfering targets. The accuracy of the estimation parameters obtained by this algorithm are validated and compared to various actual values of the shape parameter, using Monte Carlo simulations, this latter show that the probability of censing in multiple target situations are in good agreement.

Keywords: parameters estimation, method of moments, automatic censoring, K distribution

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Authors: Fares Laouacheria, Said Kechida, Moncef Chabi

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The objective of the study was based on the hydrological routing modelling for the continuous monitoring of the hydrological situation in the Moudjar river catchment, especially during floods with Hydrologic Engineering Center–Hydrologic Modelling Systems (HEC-HMS). The HEC-GeoHMS was used to transform data from geographic information system (GIS) to HEC-HMS for delineating and modelling the catchment river in order to estimate the runoff volume, which is used as inputs to the hydrological routing model. Two hydrological routing models were used, namely Muskingum and Muskingum routing models, for conducting this study. In this study, a comparison between the parameters of the Muskingum and Muskingum-Cunge routing models in HEC-HMS was used for modelling flood routing in the Moudjar river catchment and determining the relationship between these parameters and the physical characteristics of the river. The results indicate that the effects of input parameters such as the weighting factor "X" and travel time "K" on the output results are more significant, where the Muskingum routing model was more sensitive to input parameters than the Muskingum-Cunge routing model. This study can contribute to understand and improve the knowledge of the mechanisms of river floods, especially in ungauged river catchments.

Keywords: HEC-HMS, hydrological modelling, Muskingum routing model, Muskingum-Cunge routing model

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Authors: Ilaria Lucrezia Amerise

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The statistical modelling of precipitation data for a given portion of territory is fundamental for the monitoring of climatic conditions and for Hydrogeological Management Plans (HMP). This modelling is rendered particularly complex by the changes taking place in the frequency and intensity of precipitation, presumably to be attributed to the global climate change. This paper applies the Wakeby distribution (with 5 parameters) as a theoretical reference model. The number and the quality of the parameters indicate that this distribution may be the appropriate choice for the interpolations of the hydrological variables and, moreover, the Wakeby is particularly suitable for describing phenomena producing heavy tails. The proposed estimation methods for determining the value of the Wakeby parameters are the same as those used for density functions with heavy tails. The commonly used procedure is the classic method of moments weighed with probabilities (probability weighted moments, PWM) although this has often shown difficulty of convergence, or rather, convergence to a configuration of inappropriate parameters. In this paper, we analyze the problem of the likelihood estimation of a random variable expressed through its quantile function. The method of maximum likelihood, in this case, is more demanding than in the situations of more usual estimation. The reasons for this lie, in the sampling and asymptotic properties of the estimators of maximum likelihood which improve the estimates obtained with indications of their variability and, therefore, their accuracy and reliability. These features are highly appreciated in contexts where poor decisions, attributable to an inefficient or incomplete information base, can cause serious damages.

Keywords: generalized extreme values, likelihood estimation, precipitation data, Wakeby distribution

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Authors: Milca F. Coelho, K. Bousson, Kawser Ahmed

Abstract:

To accurately track an aerospace vehicle in a time-critical situation and in a highly nonlinear environment, is one of the strongest interests within the aerospace community. The tracking is achieved by estimating accurately the state of a moving target, which is composed of a set of variables that can provide a complete status of the system at a given time. One of the main ingredients for a good estimation performance is the use of efficient estimation algorithms. A well-known framework is the Kalman filtering methods, designed for prediction and estimation problems. The success of the Kalman Filter (KF) in engineering applications is mostly due to the Extended Kalman Filter (EKF), which is based on local linearization. Besides its popularity, the EKF presents several limitations. To address these limitations and as a possible solution to tracking problems, this paper proposes the use of the Ensemble Kalman Filter (EnKF). Although the EnKF is being extensively used in the context of weather forecasting and it is being recognized for producing accurate and computationally effective estimation on systems with a very high dimension, it is almost unknown by the tracking community. The EnKF was initially proposed as an attempt to improve the error covariance calculation, which on the classic Kalman Filter is difficult to implement. Also, in the EnKF method the prediction and analysis error covariances have ensemble representations. These ensembles have sizes which limit the number of degrees of freedom, in a way that the filter error covariance calculations are a lot more practical for modest ensemble sizes. In this paper, a realistic simulation of a radar tracking was performed, where the EnKF was applied and compared with the Extended Kalman Filter. The results suggested that the EnKF is a promising tool for tracking applications, offering more advantages in terms of performance.

Keywords: Kalman filter, nonlinear state estimation, optimal tracking, stochastic environment

Procedia PDF Downloads 132

Authors: Mekonen G., Sileshi M., Melkamu M.

Abstract:

Recharge estimation is important for the assessment and management of groundwater resources effectively. This study applied the soil moisture balance and Baseflow separation methods to estimate groundwater recharge in the Fetam Catchment. It is one of the major catchments understudied from the different catchments in the upper Blue Nile River basin. Surface water has been subjected to high seasonal variation; due to this, groundwater is a primary option for drinking water supply to the community. This research has been conducted to estimate groundwater recharge by using fifteen years of River flow data for the Baseflow separation and ten years of daily meteorological data for the daily soil moisture balance recharge estimating method. The recharge rate by the two methods is 170.5 and 244.9mm/year daily soil moisture and baseflow separation method, respectively, and the average recharge is 207.7mm/year. The average value of annual recharge in the catchment is almost equal to the average recharge in the country, which is 200mm/year. So, each method has its own limitations, and taking the average value is preferable rather than taking a single value. Baseflow provides overestimated result compared to the average of the two, and soil moisture balance is the list estimator. The recharge estimation in the area also should be done by other recharge estimation methods.

Keywords: groundwater, recharge, baseflow separation, soil moisture balance, Fetam catchment

Procedia PDF Downloads 342

Authors: Wikanda Phaphan

Abstract:

Mixture generalized gamma distribution is a combination of two distributions: generalized gamma distribution and length biased generalized gamma distribution. These two distributions were presented by Suksaengrakcharoen and Bodhisuwan in 2014. The findings showed that probability density function (pdf) had fairly complexities, so it made problems in estimating parameters. The problem occurred in parameter estimation was that we were unable to calculate estimators in the form of critical expression. Thus, we will use numerical estimation to find the estimators. In this study, we presented a new method of the parameter estimation by using the expectation – maximization algorithm (EM), the conjugate gradient method, and the quasi-Newton method. The data was generated by acceptance-rejection method which is used for estimating α, β, λ and p. λ is the scale parameter, p is the weight parameter, α and β are the shape parameters. We will use Monte Carlo technique to find the estimator's performance. Determining the size of sample equals 10, 30, 100; the simulations were repeated 20 times in each case. We evaluated the effectiveness of the estimators which was introduced by considering values of the mean squared errors and the bias. The findings revealed that the EM-algorithm had proximity to the actual values determined. Also, the maximum likelihood estimators via the conjugate gradient and the quasi-Newton method are less precision than the maximum likelihood estimators via the EM-algorithm.

Keywords: conjugate gradient method, quasi-Newton method, EM-algorithm, generalized gamma distribution, length biased generalized gamma distribution, maximum likelihood method

Procedia PDF Downloads 210