Search results for: paramater estimation

Authors: Hesham Abdin, Mohamed Zakaria, Talaat Abd-Elmonaem, Alaa El-Din Sayed Hafez

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Kalman filter algorithm is an estimator known as the workhorse of estimation. It has an important application in missile guidance, especially in lack of accurate data of the target due to noise or uncertainty. In this paper, a Kalman filter is used as a tracking filter in a simulated target-interceptor scenario with noise. It estimates the position, velocity, and acceleration of the target in the presence of noise. These estimations are needed for both proportional navigation and differential geometry guidance laws. A Kalman filter has a good performance at low noise, but a large noise causes considerable errors leads to performance degradation. Therefore, a new technique is required to overcome this defect using tuning factors to tune a Kalman filter to adapt increasing of noise. The values of the tuning factors are between 0.8 and 1.2, they have a specific value for the first half of range and a different value for the second half. they are multiplied by the estimated values. These factors have its optimum values and are altered with the change of the target heading. A genetic algorithm updates these selections to increase the maximum effective range which was previously reduced by noise. The results show that the selected factors have other benefits such as decreasing the minimum effective range that was increased earlier due to noise. In addition to, the selected factors decrease the miss distance for all ranges of this direction of the target, and expand the effective range which leads to increase probability of kill.

Keywords: proportional navigation, differential geometry, Kalman filter, genetic algorithm

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Authors: Ananya Das, Arun Kumar, Gazala Habib, Vivekanandan Perumal

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Regulatory bodies has proposed limits on Particulate Matter (PM) concentration in air; however, it does not explicitly indicate the incorporation of effects of toxicities of constituents of PM in developing regulatory limits. This study aimed to provide a structured approach to incorporate toxic effects of components in developing regulatory limits on PM. A four-step human health risk assessment framework consists of - (1) hazard identification (parameters: PM and its constituents and their associated toxic effects on health), (2) exposure assessment (parameters: concentrations of PM and constituents, information on size and shape of PM; fate and transport of PM and constituents in respiratory system), (3) dose-response assessment (parameters: reference dose or target toxicity dose of PM and its constituents), and (4) risk estimation (metric: hazard quotient and/or lifetime incremental risk of cancer as applicable). Then parameters required at every step were obtained from literature. Using this information, an attempt has been made to determine limits on PM using component-specific information. An example calculation was conducted for exposures of PM_2.5 and its metal constituents from Indian ambient environment to determine limit on PM values. Identified data gaps were: (1) concentrations of PM and its constituents and their relationship with sampling regions, (2) relationship of toxicity of PM with its components.

Keywords: air, component-specific toxicity, human health risks, particulate matter

Procedia PDF Downloads 311

Authors: Ayat E. Ali, Christeen R. Aziz, Merna A. Helmy, Mohammed M. Malek, Sherif H. El-Gohary

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Goal: Purpose of the project was to make a plastic surgery prediction by using pre-operative images for the plastic surgeries’ patients and to show this prediction on a screen to compare between the current case and the appearance after the surgery. Methods: To this aim, we implemented a software which used data from the internet for facial skin diseases, skin burns, pre-and post-images for plastic surgeries then the post- surgical prediction is done by using K-nearest neighbor (KNN). So we designed and fabricated a smart mirror divided into two parts a screen and a reflective mirror so patient's pre- and post-appearance will be showed at the same time. Results: We worked on some skin diseases like vitiligo, skin burns and wrinkles. We classified the three degrees of burns using KNN classifier with accuracy 60%. We also succeeded in segmenting the area of vitiligo. Our future work will include working on more skin diseases, classify them and give a prediction for the look after the surgery. Also we will go deeper into facial deformities and plastic surgeries like nose reshaping and face slim down. Conclusion: Our project will give a prediction relates strongly to the real look after surgery and decrease different diagnoses among doctors. Significance: The mirror may have broad societal appeal as it will make the distance between patient's satisfaction and the medical standards smaller.

Keywords: k-nearest neighbor (knn), face detection, vitiligo, bone deformity

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Authors: A. Asgharzadeh, M. Maroufi

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5G mobile communication system has drawn more and more attention. The 5G system needs to provide three different types of services, including enhanced Mobile BroadBand (eMBB), massive machine-type communication (mMTC), and ultra-reliable and low-latency communication (URLLC). Universal Filtered Multi-Carrier (UFMC), Filter Bank Multicarrier (FBMC), and Filtered Orthogonal Frequency Division Multiplexing (f-OFDM) are suggested as a well-known candidate waveform for the coming 5G system. Themachine-to-machine (M2M) communications are one of the essential applications in 5G, and it involves exchanging of concise messages with a very short latency. However, in UFMC systems, the subcarriers are grouped into subbands but f-OFDM only one subband covers the entire band. Furthermore, in FBMC, a subband includes only one subcarrier, and the number of subbands is the same as the number of subcarriers. This paper mainly discusses the performance of UFMC with different parameters for the UFMC system. Also, paper shows that UFMC is the best choice outperforming OFDM in any case and FBMC in case of very short packets while performing similarly for long sequences with channel estimation techniques for Interleave Division Multiple Access (IDMA) systems.

Keywords: universal filtered multi-carrier technique, UFMC, interleave division multiple access, IDMA, fifth-generation, subband

Procedia PDF Downloads 134

Authors: Vladimir A. Todiras

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Spatio-temporal models provide new possibilities for real-time action in pest risk analysis. It should be noted that estimation of the possibility and probability of introduction of a pest and of its economic consequences involves many uncertainties. We present a new mapping technique that assesses pest invasion risk using online BioClass software. BioClass is a GIS tool designed to solve multiple-criteria classification and optimization problems based on fuzzy logic and level set methods. This research describes a method for predicting the potential establishment and spread of a plant pest into new areas using a case study: corn rootworm (Diabrotica spp.), tomato leaf miner (Tuta absoluta) and plum fruit moth (Grapholita funebrana). Our study demonstrated that in BioClass we can combine fuzzy logic and geographic information systems with knowledge of pest biology and environmental data to derive new information for decision making. Pests are sensitive to a warming climate, as temperature greatly affects their survival and reproductive rate and capacity. Changes have been observed in the distribution, frequency and severity of outbreaks of Helicoverpa armigera on tomato. BioClass has demonstrated to be a powerful tool for applying dynamic models and map the potential future distribution of a species, enable resource to make decisions about dangerous and invasive species management and control.

Keywords: classification, model, pest, risk

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Authors: Sanjay Kumar, Arumugam Sankaran, Arjun K., Mousumi Das

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The amount of domestic consumption and population growth is having a positive impact on economic growth and development as observed by the Harrod-Domar and endogenous growth models. The paper negates the Solow growth model which argues the population growth has a detrimental impact on per capita and steady-state growth. Unlike the Solow model, the paper observes, the per capita income growth never falls zero, and it sustains as positive. Hence, our goal here is to investigate the relationship among population, domestic consumption and economic growth of India. For this estimation, annual data from 1980-2016 has been collected from World Development Indicator and Reserve Bank of India. To know the long run as well as short-run dynamics among the variables, we have employed the ARDL bounds testing approach of cointegration followed by modified Wald causality test to know the direction of causality. The conclusion from cointegration and ARDL estimates reveal that there is a long run positive and statistically significant relationship among the variables under study. At the same time, the causality test shows that there is a causal relationship that exists among the variables. Hence, this calls for policies which have a long run perspective in strengthening the capabilities and entitlements of people and stabilizing domestic demand so as to serve long run and short run growth and stability of the economy.

Keywords: cointegration, consumption, economic development, population growth

Procedia PDF Downloads 159

Authors: Muhammad Jamil, Ning He, Wei Zhao

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It is a rough estimation that the aerospace companies received orders of 37000 new aircraft, including the air ambulances, until 2037. And titanium alloys have a 15% contribution in modern aircraft's manufacturing owing to the high strength/weight ratio. Despite their application in the aerospace and medical equipment manufacturing industry, still, their high-speed machining puts a challenge in terms of tool wear, heat generation, and poor surface quality. Among titanium alloys, Ti-6Al-4V is the major contributor to aerospace application. However, its poor thermal conductivity (6.7W/mK) accumulates shear and friction heat at the tool-chip interface zone. To dissipate the heat generation and friction effect, cryogenic cooling, Minimum quantity lubrication (MQL), nanofluids, hybrid cryogenic-MQL, solid lubricants, etc., are applied frequently to underscore their significant effect on improving the machinability of Ti-6Al-4V. Nowadays, hybrid lubri-cooling is getting attention from researchers to explore their effect regarding the hard-to-cut Ti-6Al-4V. Therefore, this study is devoted to exploring the effect of hybrid ethanol-ester oil MQL regarding the cutting temperature, surface integrity, and tool life. As the ethanol provides -OH group and ester oil of long-chain molecules provide a tribo-film on the tool-workpiece interface. This could be a green manufacturing alternative for the manufacturing industry.

Keywords: hybrid lubri-cooling, surface roughness, tool wear, MQL

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Authors: Hicham Gourgue, Ahmed Aharoune, Ahmed Ihlal

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We present in this work our model of road traffic emissions (line sources) and dispersion of these emissions, named DISPOLSPEM (Dispersion of Poly Sources and Pollutants Emission Model). In its emission part, this model was designed to keep the consistent bottom-up and top-down approaches. It also allows to generate emission inventories from reduced input parameters being adapted to existing conditions in Morocco and in the other developing countries. While several simplifications are made, all the performance of the model results are kept. A further important advantage of the model is that it allows the uncertainty calculation and emission rate uncertainty according to each of the input parameters. In the dispersion part of the model, an improved line source model has been developed, implemented and tested against a reference solution. It provides improvement in accuracy over previous formulas of line source Gaussian plume model, without being too demanding in terms of computational resources. In the case study presented here, the biggest errors were associated with the ends of line source sections; these errors will be canceled by adjacent sections of line sources during the simulation of a road network. In cases where the wind is parallel to the source line, the use of the combination discretized source and analytical line source formulas minimizes remarkably the error. Because this combination is applied only for a small number of wind directions, it should not excessively increase the calculation time.

Keywords: air pollution, dispersion, emissions, line sources, road traffic, urban transport

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Authors: José de Jesús Rivero Oliva, Jesús Salomón Llanes, Manuel Perdomo Ojeda, Antonio Torres Valle

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Combinatorial explosion is a common problem to both predominant methods for solving fault trees: Minimal Cut Set (MCS) approach and Binary Decision Diagram (BDD). High memory consumption impedes the complete solution of very complex fault trees. Only approximated non-conservative solutions are possible in these cases using truncation or other simplification techniques. The paper proposes a method (CSolv+) for solving complex fault trees, without any possibility of combinatorial explosion. Each individual MCS is immediately discarded after its contribution to the basic events importance measures and the Top gate Upper Bound Probability (TUBP) has been accounted. An estimation of the Top gate Exact Probability (TEP) is also provided. Therefore, running in a computer cluster, CSolv+ will guarantee the complete solution of complex fault trees. It was successfully applied to 40 fault trees from the Aralia fault trees database, performing the evaluation of the top gate probability, the 1000 Significant MCSs (SMCS), and the Fussell-Vesely, RRW and RAW importance measures for all basic events. The high complexity fault tree nus9601 was solved with truncation probabilities from 10-²¹ to 10-²⁷ just to limit the execution time. The solution corresponding to 10-²⁷ evaluated 3.530.592.796 MCSs in 3 hours and 15 minutes.

Keywords: system reliability analysis, probabilistic risk assessment, fault tree analysis, basic events importance measures

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Authors: Saima Iftikhar Rida Shabbir, Zeeshan Hassan

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Water shortage, energy crisis and natural misfortunes are the glitches which reduce the efficacy of agricultural ecosystems especially in Pakistan where these are more frequent besides being intense. Accordingly, the agricultural water resources, food security and country’s economy are at risk. To address this, we have used Geospatial techniques incorporating ASTER Global DEM, Geological map, rainfall data, discharge data, Landsat 5 image of Swat valley in order to assess the viability of selected sites. The sites have been studied via GIS tools, Hydrological investigation and multiparametric analysis for their potentialities of collecting and securing the rain water; regulating floods by storing the surplus water bulks by check dams and developing them for power generation. Our results showed that Siat1-1 was very useful for low-cost dam with main objective of as Debris dam; Site-2 and Site 3 were check dams sites having adequate storing reservoir so as to arrest the inconsistent flow accompanied by catering the sedimentation effects and the debris flows; Site 4 had a huge reservoir capacity but it entails enormous edifice cost over very great flood plain. Thus, there is necessity of active Hydrological developments to estimate the flooded area using advanced and multifarious GIS and remote sensing approaches so that the sites could be developed for harnessing those sites for agricultural and energy drives.

Keywords: site suitability, check dams, SHP, terrain analysis, volume estimation

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Authors: Bruno de Almeida Vilela, Heinz Ahn, Ana Lúcia Miranda Lopes, Marcelo Azevedo Costa

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Benchmarking public utilities such as transmission system operators (TSOs) is one of the main strategies employed by regulators in order to fix monopolistic companies’ revenues. Since 2007 the Brazilian regulator has been utilizing Data Envelopment Analysis (DEA) to benchmark TSOs. Despite the application of DEA to improve the transmission sector’s efficiency, some problems can be pointed out, such as the high price of electricity in Brazil; the limitation of the benchmarking only to operational expenses (OPEX); the absence of variables that represent the outcomes of the transmission service; and the presence of extremely low and high efficiencies. As an alternative to the current concept of benchmarking the Brazilian regulator uses, we propose a goal-oriented approach. Our proposal supports input/output selection by taking traditional organizational goals and measures as a basis for the selection of factors for benchmarking purposes. As the main advantage, it resolves the classical DEA problems of input/output selection, undesirable and dual-role factors. We also provide a demonstration of our goal-oriented concept regarding service quality. As a result, most TSOs’ efficiencies in Brazil might improve when considering quality as important in their efficiency estimation.

Keywords: decision making, goal-oriented benchmarking, input/output factor determination, TSO regulation

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Authors: Spandan Sahu, Amiya Kumar Pati, Kishanjit Kumar Khatua

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River is the main source of water. It is a form of natural open channel which gives rise to many complex phenomenon of sciences that needs to be tackled such as the critical flow conditions, boundary shear stress and depth averaged velocity. The development of society more or less solely depends upon the flow of rivers. The rivers are major sources of many sediments and specific ingredients which are much essential for human beings. During floods, part of a river is carried by the simple main channel and rest is carried by flood plains. For such compound asymmetric channels, the flow structure becomes complicated due to momentum exchange between main channel and adjoining flood plains. Distribution of boundary shear in subsections provides us with the concept of momentum transfer between the interface of main channel and the flood plains. Experimentally, to get better data with accurate results are very complex because of the complexity of the problem. Hence, Conveyance Estimation System (CES) software has been used to tackle the complex processes to determine the shear stresses at different sections of an open channel having asymmetric flood plains on both sides of the main channel and the results are compared with the symmetric flood plains for various geometrical shapes and flow conditions. Error analysis is also performed to know the degree of accuracy of the model implemented.

Keywords: depth average velocity, non prismatic compound channel, relative flow depth , velocity distribution

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Authors: Loubna Hamami, Bouchaib Nassereddine

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Due to the evolution of technologies, the need to observe and manage hostile environments, and reduction in size, wireless sensor networks (WSNs) are becoming essential and implicated in the most fields of life. WSNs enable us to change the style of living, working and interacting with the physical environment. The agricultural sector is one of such sectors where WSNs are successfully used to get various benefits. For successful agricultural production, the irrigation system is one of the most important factors, and it plays a tactical role in the process of agriculture domain. However, it is considered as the largest consumer of freshwater. Besides, the scarcity of water, the drought, the waste of the limited available water resources are among the critical issues that touch the almost sectors, notably agricultural services. These facts are leading all governments around the world to rethink about saving water and reducing the volume of water used; this requires the development of irrigation practices in order to have a complete and independent system that is more efficient in the management of irrigation. Consequently, the selection of WSNs in irrigation system has been a benefit for developing the agriculture sector. In this work, we propose a prototype for a complete and intelligent irrigation system based on wireless sensor networks and we present and discuss the design of this prototype. This latter aims at saving water, energy and time. The proposed prototype controls water system for irrigation by monitoring the soil temperature, soil moisture and weather conditions for estimation of water requirements of each plant.

Keywords: precision irrigation, sensor, wireless sensor networks, water resources

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Authors: Kashif Nesar Rather, Mantu Kumar Mahalik

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The discourse surrounding climate change has gained considerable traction, with a discernible emphasis on its nuanced and consequential impact on gender inequality. Concurrently, escalating global tensions are contributing to heightened uncertainty, potentially exerting influence on gender disparities. Within this framework, this study attempts to empirically investigate the implications of climate change and world uncertainty on the gender inequality for a balanced panel of 100 economies between 1995 to 2021. The estimated models also control for the effects of globalisation, economic growth, and education expenditure. The panel cointegration tests establish a significant long-run relationship between the variables of the study. Furthermore, the PMG-ARDL (Panel mean group-Autoregressive distributed lag model) estimation technique confirms that both climate change and world uncertainty perpetuate the global gender inequalities. Additionally, the results establish that globalisation, economic growth, and education expenditure exert a mitigating influence on gender inequality, signifying their role in diminishing gender disparities. These findings are further confirmed by the FGLS (Feasible Generalized Least Squares) and DKSE (Driscoll-Kraay Standard Errors) regression methods. Potential policy implications for mitigating the detrimental gender ramifications stemming from climate change and rising world uncertainties are also discussed.

Keywords: gender inequality, world uncertainty, climate change, globalisation., ecological footprint

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Authors: M. Hussaina, K. Mahboobb, S. Z. Ilyasa, S. Shaheena

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Plume dispersion modeling is a computational procedure to establish a relationship between emissions, meteorology, atmospheric concentrations, deposition and other factors. The emission characteristics (stack height, stack diameter, release velocity, heat contents, chemical and physical properties of the gases/particle released etc.), terrain (surface roughness, local topography, nearby buildings) and meteorology (wind speed, stability, mixing height, etc.) are required for the modeling of the plume dispersion and estimation of ground and air concentration. During the early phase of Fukushima accident, plume dispersion modeling and decisions were taken for the implementation of protective measures. A difference in estimated results and decisions made by different countries for taking protective actions created a concern in local and international community regarding the exact identification of the safe zone. The current study is focused to highlight the importance of accurate and exact weather data availability, scientific approach for decision making for taking urgent protective actions, compatible and harmonized approach for plume dispersion modeling during a nuclear emergency. As a case study, the influence of meteorological data on plume dispersion modeling and decision-making process has been performed.

Keywords: decision making process, radiation doses, nuclear emergency, meteorological implications

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Authors: Golayeh Yousefi, Mehdi Homaee, Ali Akbar Norouzi

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Ongoing monitoring of soil contamination by heavy metals is critical for ecosystem stability and environmental protection, and food security. The conventional methods of determining these soil contaminants are time-consuming and costly. Spectroscopy in the visible near-infrared (VNIR) - short wave infrared (SWIR) region is a rapid, non-destructive, noninvasive, and cost-effective method for assessment of soil heavy metals concentration by studying the spectral properties of soil constituents. The aim of this study is to derive spectral bands and important ranges that are sensitive to heavy metals and can be used to estimate the concentration of these soil contaminants. In other words, the change in the spectral properties of spectrally active constituents of soil can lead to the accurate identification and estimation of the concentration of these compounds in soil. For this purpose, 325 soil samples were collected, and their spectral reflectance curves were evaluated at a range of 350-2500 nm. After spectral preprocessing operations, the partial least-squares regression (PLSR) model was fitted on spectral data to predict the concentration of Cu and Ni. Based on the results, the spectral range of Cu- sensitive spectra were 480, 580-610, 1370, 1425, 1850, 1920, 2145, and 2200 nm, and Ni-sensitive ranges were 543, 655, 761, 1003, 1271, 1415, 1903, 2199 nm. Finally, the results of this study indicated that the spectral data contains a lot of information that can be applied to identify the soil properties, such as the concentration of heavy metals, with more detail.

Keywords: heavy metals, spectroscopy, spectral bands, PLS regression

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Authors: Noor Aliza Binti Ahmad, Azra Munirah Mat Daud, Sabariah Musa, Mohamad Azhar MK

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Flooding is a common natural disaster in Malaysia triggered by heavy rainfall. Urbanization that increases the construction of paved areas, subsequently raise surface runoff and reduce time of concentration. It increases flood magnitude and so that leads to greater flood problems as what has happened at Universiti Tun Hussein Onn Malaysia (UTHM) area in December 2006 and earlier 2007. Tasik Kemajuan and Tasik Inovasi were constructed as recreation ponds and have also functioned as flood ponds. Unfortunately, the flood problem still occurs persistently. Thus, the effectiveness of Tasik Kemajuan and Tasik Inovasi in reducing the flood problems need to be investigated and the causes of flood events at UTHM Campus need to be evaluated. The results from this study show that the conditions of Tasik Kemajuan and Tasik Inovasi are effective in reducing the flood water levels. It also can be concluded that increasing water level in both lakes in UTHM Campus are significantly influenced by presence of the grass and rubbish. During dry condition, the flow rates with three different days are 59.38m3/s, 60.71m3/s and 59.08m3/s and while for wet condition in two different days are 89.59 m3/s and 86.61m3/s. In conclusion, this system should be improved to prevent future flooding either widened or reduced drainage floor, and also perform maintenance on the plants that live around the lake.

Keywords: drainage system, flood detention, lakes, storm water

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Authors: Mohammad Bqoor, Mohammad Hamdan, Isam Janajreh, Sufian Abedrabbo

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This extensive theoretical study on a novel Ionized Gas Thermoelectric Generator (IG-TEG) system has shown the ability of continuous energy extracting from the thermal energy of ambient air around standard room temperature and even below. This system does not need a temperature gradient in order to work, unlike the other TEGs that use the Seebeck effect, and therefore this new system can be utilized in sustainable energy systems, as well as in green cooling solutions, by extracting energy instead of wasting energy in compressing the gas for cooling. This novel system was designed based on Static Ratchet Potential (SRP), which is known as a spatially asymmetric electric potential produced by an array of positive and negative electrodes. The ratchet potential produces an electrical current from the random Brownian Motion of charged particles that are driven by thermal energy. The key parameter of the system is particle transportation, and it was studied under the condition of flashing ratchet potentials utilizing several methods and examined experimentally, ensuring its functionality. In this study, a different approach is pursued to estimate particle transportation by evaluating the charged particle distribution and applying the other conditions of the SRP, and showing continued energy harvesting potency from the particles’ transportation. Ultimately, power levels of 10 Watt proved to be achievable from a 1 m long system tube of 10 cm radius.

Keywords: thermoelectric generator, ratchet potential, Brownian ratchet, energy harvesting, sustainable energy, green technology

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Authors: Ajai Singh

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Simulation of hydrological processes at the watershed outlet through modelling approach is essential for proper planning and implementation of appropriate soil conservation measures in Damodar Barakar catchment, Hazaribagh, India where soil erosion is a dominant problem. This study quantifies the parametric uncertainty involved in simulation of stream flow using Soil and Water Assessment Tool (SWAT), a watershed scale model and Radial Basis Neural Network (RBNN), an artificial neural network model. Both the models were calibrated and validated based on measured stream flow and quantification of the uncertainty in SWAT model output was assessed using ‘‘Sequential Uncertainty Fitting Algorithm’’ (SUFI-2). Though both the model predicted satisfactorily, but RBNN model performed better than SWAT with R2 and NSE values of 0.92 and 0.92 during training, and 0.71 and 0.70 during validation period, respectively. Comparison of the results of the two models also indicates a wider prediction interval for the results of the SWAT model. The values of P-factor related to each model shows that the percentage of observed stream flow values bracketed by the 95PPU in the RBNN model as 91% is higher than the P-factor in SWAT as 87%. In other words the RBNN model estimates the stream flow values more accurately and with less uncertainty. It could be stated that RBNN model based on simple input could be used for estimation of monthly stream flow, missing data, and testing the accuracy and performance of other models.

Keywords: SWAT, RBNN, SUFI 2, bootstrap technique, stream flow, simulation

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Authors: Haggay Etkes

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This study provides evidence for the impact of monthly interviews conducted for the Israeli Labor Force Surveys (LFSs) on estimated flows between labor force (LF) statuses and on coefficients in fixed-effects estimations. The study uses the natural experiment of parallel interviews for the quarterly and the monthly LFSs in Israel in 2011 for demonstrating that the Labor Force Participation (LFP) rate of Jewish persons who participated in the monthly LFS increased between interviews, while in the quarterly LFS it decreased. Interestingly, the estimated impact on the LFP rate of self-reporting individuals is 2.6–3.5 percentage points while the impact on the LFP rate of individuals whose data was reported by another member of their household (a proxy), is lower and statistically insignificant. The relative increase of the LFP rate in the monthly survey is a result of a lower rate of exit from the LF and a somewhat higher rate of entry into the LF relative to these flows in the quarterly survey. These differing flows have a bearing on labor search models as the monthly survey portrays a labor market with less friction and a “steady state” LFP rate that is 5.9 percentage points higher than the quarterly survey. The study also demonstrates that monthly interviews affect a specific group (45–64 year-olds); thus the sign of coefficient of age as an explanatory variable in fixed-effects regressions on LFP is negative in the monthly survey and positive in the quarterly survey.

Keywords: measurement error, surveys, search, LFSs

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Authors: Celso L. Fernando, Toshio Yoshii, Takahiro Tsubota

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Understanding the causes of a road accident and predicting their occurrence is key to preventing deaths and serious injuries from road accident events. Traditional statistical methods such as the Poisson and the Logistics regressions have been used to find the association of the traffic environmental factors with the accident occurred; recently, an artificial neural network, ANN, a computational technique that learns from historical data to make a more accurate prediction, has emerged. Although the ability to make accurate predictions, the ANN has difficulty dealing with highly unbalanced attribute patterns distribution in the training dataset; in such circumstances, the ANN treats the minority group as noise. However, in the real world data, the minority group is often the group of interest; e.g., in the road traffic accident data, the events of the accident are the group of interest. This study proposes a combination of the k-means with the ANN to improve the predictive ability of the neural network model by alleviating the effect of the unbalanced distribution of the attribute patterns in the training dataset. The results show that the proposed method improves the ability of the neural network to make a prediction on a highly unbalanced distributed attribute patterns dataset; however, on an even distributed attribute patterns dataset, the proposed method performs almost like a standard neural network.

Keywords: accident risks estimation, artificial neural network, deep learning, k-mean, road safety

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Authors: Feng Yang

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Machine learning (ML), especially deep learning (DL), has been extensively applied to many applications in recently years and gained great success in solving different problems, including scientific problems. However, conventional ML/DL methodologies are purely data-driven which have the limitations, such as need of ample amount of labelled training data, lack of consistency to physical principles, and lack of generalizability to new problems/domains. Recently, there is a growing consensus that ML models need to further take advantage of prior knowledge to deal with these limitations. Physics-informed machine learning, aiming at integration of physics/domain knowledge into ML, has been recognized as an emerging area of research, especially in the recent 2 to 3 years. In this work, physics-informed ML, specifically physics-informed neural network (NN), is employed and implemented to estimate the displacements at x, y, z directions in a solid mechanics problem that is controlled by equilibrium equations with boundary conditions. By incorporating the physics (i.e. the equilibrium equations) into the learning process of NN, it is showed that the NN can be trained very efficiently with a small set of labelled training data. Experiments with different settings of the NN model and the amount of labelled training data were conducted, and the results show that very high accuracy can be achieved in fulfilling the equilibrium equations as well as in predicting the displacements, e.g. in setting the overall displacement of 0.1, a root mean square error (RMSE) of 2.09 × 10−4 was achieved.

Keywords: deep learning, neural network, physics-informed machine learning, solid mechanics

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Authors: Dinebari Akpee, Friday Aabe Gaage, Florence Fred Nwaigwu

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Natural disasters like flood affect many parts of the world including developing countries like Nigeria. As a result, many human lives are lost, properties damaged and so much money is lost in infrastructure damages. These hazards and losses can be mitigated and reduced by providing reliable spatial information to the generality of the people through about flood risks through flood inundation maps. Flood inundation maps are very crucial for emergency action plans, urban planning, ecological studies and insurance rates. Nigeria experience her worst flood in her entire history this year. Many cities were submerged and completely under water due to torrential rainfall. Poor city planning, lack of effective development control among others contributes to the problem too. Geographic information system (GIS) can be used to visualize the extent of flooding, analyze flood maps to produce flood damaged estimation maps and flood risk maps. In this research, the under listed steps were taken in preparation of flood risk maps for the study area: (1) Digitization of topographic data and preparation of digital elevation model using ArcGIS (2) Flood simulation using hydraulic model and integration and (3) Integration of the first two steps to produce flood risk maps. The results shows that GIS can play crucial role in Flood disaster control and mitigation.

Keywords: flood disaster, risk maps, geographic information system, hazards

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Authors: Meziane Rachid, Boufala Seddik, Hamzi Amar, Amara Mohamed

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Today’s energy industry seeks maximum benefit with maximum reliability. In order to achieve this goal, design engineers depend on reliability optimization techniques. This work uses a harmony search algorithm (HS) meta-heuristic optimization method to solve the problem of wind-Solar-Gas power systems design optimization. We consider the case where redundant electrical components are chosen to achieve a desirable level of reliability. The electrical power components of the system are characterized by their cost, capacity and reliability. The reliability is considered in this work as the ability to satisfy the consumer demand which is represented as a piecewise cumulative load curve. This definition of the reliability index is widely used for power systems. The proposed meta-heuristic seeks for the optimal design of series-parallel power systems in which a multiple choice of wind generators, transformers and lines are allowed from a list of product available in the market. Our approach has the advantage to allow electrical power components with different parameters to be allocated in electrical power systems. To allow fast reliability estimation, a universal moment generating function (UMGF) method is applied. A computer program has been developed to implement the UMGF and the HS algorithm. An illustrative example is presented.

Keywords: reliability optimization, harmony search optimization (HSA), universal generating function (UMGF)

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Authors: Constantin Ott, Dietmar Drummer

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The direct encapsulation of electronic components is an effective way of protecting components against external influences. In addition to achieving a sufficient protective effect, there are two other big challenges for satisfying the increasing demand for encapsulated circuit boards. The encapsulation process should be both suitable for mass production and offer a low component load. Injection molding is a method with good suitability for large series production but also with typically high component stress. In this article, two aims were pursued: first, the development of a calculation model that allows an estimation of the occurring forces based on process variables and material parameters. Second, the evaluation of a new approach for stress reduction by means of thermoplastic foam injection molding. For this purpose, simulation-based process data was generated with the Moldflow simulation tool. Based on this, component stresses were calculated with the calculation model. At the same time, this paper provided a model for estimating the forces occurring during overmolding and derived a solution method for reducing these forces. The suitability of this approach was clearly demonstrated and a significant reduction in shear forces during overmolding was achieved. It was possible to demonstrate a process development that makes it possible to meet the two main requirements of direct encapsulation in addition to a high protective effect.

Keywords: encapsulation, stress reduction, foam-injection-molding, simulation

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Authors: Vani Chintapally

Abstract:

The expansion of modest and minimized Global Positioning System (GPS) beneficiaries has prompted most Automatic Vehicle Location (AVL) frameworks today depending solely on satellite-based finding frameworks, as GPS is the most stable usage of these. This paper shows the attributes of a proposed framework for following and dissecting open transport in a run of the mill medium-sized city and complexities the qualities of such a framework to those of broadly useful AVL frameworks. Particular properties of the courses broke down by the AVL framework utilized for the examination of open transport in our study incorporate cyclic vehicle courses, the requirement for particular execution reports, and so forth. This paper particularly manages vehicle movement forecasts and the estimation of station landing time, combined with consequently produced reports on timetable conformance and other execution measures. Another side of the watched issue is proficient exchange of information from the vehicles to the control focus. The pervasiveness of GSM bundle information exchange advancements combined with decreased information exchange expenses have brought on today's AVL frameworks to depend predominantly on parcel information exchange administrations from portable administrators as the correspondences channel in the middle of vehicles and the control focus. This methodology brings numerous security issues up in this conceivably touchy application field.

Keywords: automatic vehicle location (AVL), expectation of landing times, AVL security, data administrations, wise transport frameworks (ITS), guide coordinating

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Authors: Andreas Gohs, Reinhold Kosfeld

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This study should be of interest for practitioners who want to predict precisely the sales numbers of vehicles equipped with an innovative propulsion technology as well as for researchers interested in applied (regional) time series analysis. The study is based on the numbers of new registrations of pure electric and hybrid cars. Methods of time series analysis like ARIMA are compared with the Bass Diffusion-model concerning their forecasting performances for new registrations in Germany at the national and federal state levels. Especially it is investigated if the additional information content from regional data increases the forecasting accuracy for the national level by adding predictions for the federal states. Results of parameters of the Bass Diffusion Model estimated for Germany and its sixteen federal states are reported. While the focus of this research is on the German market, estimation results are also provided for selected European and other countries. Concerning Bass-parameters and forecasting performances, we get very different results for Germany's federal states and the member states of the European Union. This corresponds to differences across the EU-member states in the adoption process of this innovative technology. Concerning the German market, the adoption is rather proceeded in southern Germany and stays behind in Eastern Germany except for Berlin.

Keywords: bass diffusion model, electric vehicles, forecasting performance, market diffusion

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Authors: Abdullahi Jibrin, Aishetu Abdulkadir

Abstract:

The development of allometric models is crucial to accurate forest biomass/carbon stock assessment. The aim of this study was to develop a set of biomass prediction models that will enable the determination of total tree aboveground biomass for savannah woodland area in Niger State, Nigeria. Based on the data collected through biometric measurements of 1816 trees and destructive sampling of 36 trees, five species specific and one site specific models were developed. The sample size was distributed equally between the five most dominant species in the study site (Vitellaria paradoxa, Irvingia gabonensis, Parkia biglobosa, Anogeissus leiocarpus, Pterocarpus erinaceous). Firstly, the equations were developed for five individual species. Secondly these five species were mixed and were used to develop an allometric equation of mixed species. Overall, there was a strong positive relationship between total tree biomass and the stem diameter. The coefficient of determination (R2 values) ranging from 0.93 to 0.99 P < 0.001 were realised for the models; with considerable low standard error of the estimates (SEE) which confirms that the total tree above ground biomass has a significant relationship with the dbh. The F-test value for the biomass prediction models were also significant at p < 0.001 which indicates that the biomass prediction models are valid. This study recommends that for improved biomass estimates in the study site, the site specific biomass models should preferably be used instead of using generic models.

Keywords: allometriy, biomass, carbon stock , model, regression equation, woodland, inventory

Procedia PDF Downloads 448

Authors: Xiuguo Zhao, He Li, Alireza Yazdani, Xiaoning Zheng, Xinxi Xu

Abstract:

Wound infected poses a serious threat to the surgery on the patient during the process of surgery. Understanding the bacteria-carrying particles (BCPs) transportation and deposition in the open surgical wound model play essential role in protecting wound against being infected. Therefore BCPs transportation and deposition in the surgical wound model were investigated using force-coupling method (FCM) based computational fluid dynamics. The BCPs deposition in the wound was strongly associated with BCPs diameter and concentration. The results showed that the rise on the BCPs deposition was increasing not only with the increase of BCPs diameters but also with the increase of the BCPs concentration. BCPs deposition morphology was impacted by the combination of size distribution, airflow patterns and model geometry. The deposition morphology exhibited the characteristic with BCPs deposition on the sidewall in wound model and no BCPs deposition on the bottom of the wound model mainly because the airflow movement in one direction from up to down and then side created by laminar system constructing airflow patterns and then made BCPs hard deposit in the bottom of the wound model due to wound geometry limit. It was also observed that inertial impact becomes a main mechanism of the BCPs deposition. This work may contribute to next study in BCPs deposition limit, as well as wound infected estimation in surgical-site infections.

Keywords: BCPs deposition, computational fluid dynamics, force-coupling method (FCM), numerical simulation, open surgical wound model

Procedia PDF Downloads 289

Authors: Yosra Ben Mhamed

Abstract:

Most previous research that investigates the importance of earnings volatility for a firm’s value has focused on the effects of earnings volatility on the cost of capital. Many study illustrate that earnings volatility can reduce the firm’s value by enhancing the cost of capital. However, a few recent studies directly examine the relation between earnings volatility and subsequent earnings levels. In our study, we further explore the role of volatility in forecasting. Our study makes two primary contributions to the literature. First, taking into account the level of current firm’s performance, we provide causal theory to the link between volatility and earnings predictability. Nevertheless, previous studies testing the linearity of this relationship have not mentioned any underlying theory. Secondly, our study contributes to the vast body of fundamental analysis research that identifies a set of variables that improve valuation, by showing that earnings volatility affects the estimation of future earnings. Projections of earnings are used by valuation research and practice to derive estimates of firm value. Since we want to examine the impact of volatility on earnings predictability, we sort the sample into three portfolios according to the level of their earnings volatility in ascending order. For each quintile, we present the predictability coefficient. In a second test, each of these portfolios is, then, sorted into three further quintiles based on their level of current earnings. These yield nine quintiles. So we can observe whether volatility strongly predicts decreases on earnings predictability only for highest quintile of earnings. In general, we find that earnings volatility has an inverse relationship with earnings predictability. Our results also show that the sensibility of earnings predictability to ex-ante volatility is more pronounced among profitability firms. The findings are most consistent with overinvestment and persistence explanations.

Keywords: earnings volatility, earnings predictability, earnings persistence, current profitability

Procedia PDF Downloads 433