Search results for: fault estimation

Authors: Ali Hojati Najafabadi, Mohamad Hosein Rahmati, Seyed Ali Madanizadeh

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Governments execute energy subsidy reforms by either increasing energy prices or reducing energy price dispersion. These policies make less use of energy per plant (intensive margin), vary the total number of firms (extensive margin), promote technological progress (technology channel), and make additional resources to redistribute (resource channel). We estimate a structural dynamic firm model with endogenous technology adaptation using data from the manufacturing firms in Iran and a country ranked the second-largest energy subsidy plan by the IMF. The findings show significant dynamics and distributional effects due to an energy reform plan. The price elasticity of energy consumption in the industrial sector is about -2.34, while it is -3.98 for large firms. The dispersion elasticity, defined as the amounts of changes in energy consumption by a one-percent reduction in the standard error of energy price distribution, is about 1.43, suggesting significant room for a distributional policy. We show that the intensive margin is the main driver of energy price elasticity, whereas the other channels mostly offset it. In contrast, the labor response is mainly through the extensive margin. Total factor productivity slightly improves in light of the reduction in energy consumption if, at the same time, the redistribution policy boosts the aggregate demands.

Keywords: energy reform, firm dynamics, structural estimation, subsidy policy

Procedia PDF Downloads 78

Authors: Kanwal Javid, Shazia Pervaiz, Maria Mumtaz, Muhammad Ameer Nawaz Akram

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Forests and grasslands ecosystems play an important role in the global carbon cycle. Land management activities influence both ecosystems and enable them to absorb and sequester carbon dioxide (CO2). Similarly, in Pakistan, these terrestrial ecosystems are well known to mitigate carbon emissions and have a great source to supply a variety of services such as clean air and water, biodiversity, wood products, wildlife habitat, food, recreation and carbon sequestration. Carbon sequestration is the main agenda of developed and developing nations to reduce the impacts of global warming. But the amount of carbon storage within these ecosystems can be affected by many factors related to air quality such as land management, land-use change, deforestation, over grazing and natural calamities. Moreover, the long-term capacity of forests and grasslands to absorb and sequester CO2 depends on their health, productivity, resilience and ability to adapt to changing conditions. Thus, the main rationale of this study is to monitor the difference in carbon amount of forests and grasslands of Northern Pakistan using MODIS data sets and map results using Geographic Information System. Results of the study conclude that forests ecosystems are more effective in reducing the CO2 level and play a key role in improving the quality of air.

Keywords: carbon sequestration, grasslands, global warming, climate change.

Procedia PDF Downloads 170

Authors: Pankaj Kumar Jha, Mahendra Singh Hada, Brijendra Singh, Sandeep Yadav

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Protective relays are commonly connected to the secondary windings of instrument transformers, i.e., current transformers (CTs) and/or capacitive voltage transformers (CVTs). The purpose of CT and CVT is to provide galvanic isolation from high voltages and reduce primary currents and voltages to a nominal quantity recognized by the protective relays. Selecting the correct instrument transformers for an application is imperative: failing to do so may compromise the relay’s performance, as the output of the instrument transformer may no longer be an accurately scaled representation of the primary quantity. Having an accurately rated instrument transformer is of no use if these devices are not properly connected. The performance of the protective relay is reliant on its programmed settings and on the current and voltage inputs from the instrument transformers secondary. This paper will help in understanding the fundamental concepts of the connections of Instrument Transformers to the protection relays and the effect of incorrect connection on the performance of protective relays. Multiple case studies of protection system mal-operations due to incorrect connections of instrument transformers will be discussed in detail in this paper. Apart from the connection issue of instrument transformers to protective relays, this paper will also discuss the effect of multiple earthing of CTs and CVTs secondary on the performance of the protection system. Case studies presented in this paper will help the readers to analyse the problem through real-world challenges in complex power system networks. This paper will also help the protection engineer in better analysis of disturbance records. CT and CVT connection errors can lead to undesired operations of protection systems. However, many of these operations can be avoided by adhering to industry standards and implementing tried-and-true field testing and commissioning practices. Understanding the effect of missing neutral of CVT, multiple earthing of CVT secondary, and multiple grounding of CT star points on the performance of the protection system through real-world case studies will help the protection engineer in better commissioning the protection system and maintenance of the protection system.

Keywords: bus reactor, current transformer, capacitive voltage transformer, distance protection, differential protection, directional earth fault, disturbance report, instrument transformer, ICT, REF protection, shunt reactor, voltage selection relay, VT fuse failure

Procedia PDF Downloads 63

Authors: Bai-Chen Xie, Biao Li

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With the deepening of international exchanges and investment, the international comparison of power grid firms has become the focus of regulatory authorities. Ignoring the differences in the economic environment, resource endowment, technology, and other aspects of different countries or regions may lead to efficiency bias. Based on the Meta-frontier model, this paper divides China and Japan into two groups by using the data of China and Japan from 2006 to 2020. While preserving the differences between the two countries, it analyzes and compares the efficiency of the transmission and distribution industries of the two countries. Combined with the four-component stochastic frontier model, the efficiency is divided into transient and persistent efficiency. We found that there are obvious differences between the transmission and distribution sectors in China and Japan. On the one hand, the inefficiency of the two countries is mostly caused by long-term and structural problems. The key to improve the efficiency of the two countries is to focus more on solving long-term and structural problems. On the other hand, the long-term and structural problems that cause the inefficiency of the two countries are not the same. Quality factors have different effects on the efficiency of the two countries, and this different effect is captured by the common frontier model but is offset in the overall model. Based on these findings, this paper proposes some targeted policy recommendations.

Keywords: transmission and distribution industries, transient efficiency, persistent efficiency, meta-frontier, international comparison

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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: Adel A. Ghobbar, Varun Raman

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The Corrective maintenance of components and systems is a problem plaguing almost every industry in the world today. Train operators’ and the maintenance repair and overhaul subsidiary of the Dutch railway company is also facing this problem. A considerable portion of the maintenance activities carried out by the company are unscheduled. This, in turn, severely stresses and stretches the workforce and resources available. One possible solution is to have a robust preventive maintenance plan. The other possible solution is to plan maintenance based on real-time data obtained from sensor-based ‘Health and Usage Monitoring Systems.’ The former has been investigated in this paper. The preventive maintenance model developed for train operator will subsequently be extended, to tackle the unscheduled maintenance problem also affecting the aerospace industry. The extension of the model to the aerospace sector will be dealt with in the second part of the research, and it would, in turn, validate the soundness of the model developed. Thus, there are distinct areas that will be addressed in this paper, including the mathematical modelling of preventive maintenance and optimization based on cost and system availability. The results of this research will help an organization to choose the right maintenance strategy, allowing it to save considerable sums of money as opposed to overspending under the guise of maintaining high asset availability. The concept of delay time modelling was used to address the practical problem of unscheduled maintenance in this paper. The delay time modelling can be used to help with support planning for a given asset. The model was run using MATLAB, and the results are shown that the ideal inspection intervals computed using the extended from a minimal cost perspective were 29 days, and from a minimum downtime, perspective was 14 days. Risk matrix integration was constructed to represent the risk in terms of the probability of a fault leading to breakdown maintenance and its consequences in terms of maintenance cost. Thus, the choice of an optimal inspection interval of 29 days, resulted in a cost of approximately 50 Euros and the corresponding value of b(T) was 0.011. These values ensure that the risk associated with component X being maintained at an inspection interval of 29 days is more than acceptable. Thus, a switch in maintenance frequency from 90 days to 29 days would be optimal from the point of view of cost, downtime and risk.

Keywords: delay time modelling, unscheduled maintenance, reliability, maintainability, availability

Procedia PDF Downloads 116

Authors: Vinda B. Manurung, Laila Warkhaida, David Hutabarat, Sentanu Wisnuwardhana, Christovik Simatupang, Dhani Sanjaya, Ashadi, Redha B. Putra, Kiki Yustendi

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The geothermal drilling environment presents many obstacles that have limited the use of directional drilling and logging-while-drilling (LWD) technologies, such as borehole washout, mud losses, severe vibration, and high temperature. The case study presented in this paper demonstrates a practice to enhance data logging in geothermal drilling by deploying advanced telemetry and LWD technologies. This operation is aiming continuous improvement in geothermal drilling operations. The case study covers a 12.25-in. hole section of well XX-05 in Pad XX of the Sorik Marapi Geothermal Field. LWD string consists of electromagnetic (EM) telemetry, pressure while drilling (PWD), vibration (DDSr), and acoustic calliper (ACAL). Through this tool configuration, the operator acquired drilling mechanics and caliper logs in real-time and recorded mode, enabling effective monitoring of wellbore stability. Throughout the real-time acquisition, EM-PPM telemetry had provided a three times faster data rate to the surface unit. With the integration of Caliper data and Drilling mechanics data (vibration and ECD -equivalent circulating density), the borehole conditions were more visible to the directional driller, allowing for better control of drilling parameters to minimize vibration and achieve optimum hole cleaning in washed-out or tight formation sequences. After reaching well TD, the recorded data from the caliper sensor indicated an average of 8.6% washout for the entire 12.25-in. interval. Washout intervals were compared with loss occurrence, showing potential for the caliper to be used as an indirect indicator of fractured intervals and validating fault trend prognosis. This LWD case study has given added value in geothermal borehole characterization for both drilling operation and subsurface. Identified challenges while running LWD in this geothermal environment need to be addressed for future improvements, such as the effect of tool eccentricity and the impact of vibration. A perusal of both real-time and recorded drilling mechanics and caliper data has opened various possibilities for maximizing sensor usage in future wells.

Keywords: geothermal drilling, geothermal formation, geothermal technologies, logging-while-drilling, vibration, caliper, case study

Procedia PDF Downloads 107

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

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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

Procedia PDF Downloads 144

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 120

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

Procedia PDF Downloads 268

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 134

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: 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

Procedia PDF Downloads 299

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

Procedia PDF Downloads 180

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: Sanjay Rathee, Arti Kashyap

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Extraction of useful information from large datasets is one of the most important research problems. Association rule mining is one of the best methods for this purpose. Finding possible associations between items in large transaction based datasets (finding frequent patterns) is most important part of the association rule mining. There exist many algorithms to find frequent patterns but Apriori algorithm always remains a preferred choice due to its ease of implementation and natural tendency to be parallelized. Many single-machine based Apriori variants exist but massive amount of data available these days is above capacity of a single machine. Therefore, to meet the demands of this ever-growing huge data, there is a need of multiple machines based Apriori algorithm. For these types of distributed applications, MapReduce is a popular fault-tolerant framework. Hadoop is one of the best open-source software frameworks with MapReduce approach for distributed storage and distributed processing of huge datasets using clusters built from commodity hardware. However, heavy disk I/O operation at each iteration of a highly iterative algorithm like Apriori makes Hadoop inefficient. A number of MapReduce-based platforms are being developed for parallel computing in recent years. Among them, two platforms, namely, Spark and Flink have attracted a lot of attention because of their inbuilt support to distributed computations. Earlier we proposed a reduced- Apriori algorithm on Spark platform which outperforms parallel Apriori, one because of use of Spark and secondly because of the improvement we proposed in standard Apriori. Therefore, this work is a natural sequel of our work and targets on implementing, testing and benchmarking Apriori and Reduced-Apriori and our new algorithm ReducedAll-Apriori on Apache Flink and compares it with Spark implementation. Flink, a streaming dataflow engine, overcomes disk I/O bottlenecks in MapReduce, providing an ideal platform for distributed Apriori. Flink's pipelining based structure allows starting a next iteration as soon as partial results of earlier iteration are available. Therefore, there is no need to wait for all reducers result to start a next iteration. We conduct in-depth experiments to gain insight into the effectiveness, efficiency and scalability of the Apriori and RA-Apriori algorithm on Flink.

Keywords: apriori, apache flink, Mapreduce, spark, Hadoop, R-Apriori, frequent itemset mining

Procedia PDF Downloads 270

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

Procedia PDF Downloads 136

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

Abstract:

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

Abstract:

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

Abstract:

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

Abstract:

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

Abstract:

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

Abstract:

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: Felipe Riveros, Ricardo Alvarez, Claudia Rahmann, Rodrigo Moreno

Abstract:

Around the world, many countries have committed to a decarbonization of their electricity system. Under this global drive, converter-interfaced generators (CIG) such as wind and photovoltaic generation appear as cornerstones to achieve these energy targets. Despite its benefits, an increasing use of CIG brings several technical challenges in power systems, especially from a stability viewpoint. Among the key differences are limited short circuit current capacity, inertia-less characteristic of CIG, and response times within the electromagnetic timescale. Along with the integration of CIG into the power system, one enabling technology for the energy transition towards low-carbon power systems is battery energy storage systems (BESS). Because of the flexibility that BESS provides in power system operation, its integration allows for mitigating the variability and uncertainty of renewable energies, thus optimizing the use of existing assets and reducing operational costs. Another characteristic of BESS is that they can also support power system stability by injecting reactive power during the fault, providing short circuit currents, and delivering fast frequency response. However, most methodologies for sizing and allocating BESS in power systems are based on economic aspects and do not exploit the benefits that BESSs can offer to system stability. In this context, this paper presents a methodology for determining the optimal allocation of battery energy storage systems (BESS) in weak power systems with high levels of CIG. Unlike traditional economic approaches, this methodology incorporates stability constraints to allocate BESS, aiming to mitigate instability issues arising from weak grid conditions with low short-circuit levels. The proposed methodology offers valuable insights for power system engineers and planners seeking to maintain grid stability while harnessing the benefits of renewable energy integration. The methodology is validated in the reduced Chilean electrical system. The results show that integrating BESS into a power system with high levels of CIG with stability criteria contributes to decarbonizing and strengthening the network in a cost-effective way while sustaining system stability. This paper potentially lays the foundation for understanding the benefits of integrating BESS in electrical power systems and coordinating their placements in future converter-dominated power systems.

Keywords: battery energy storage, power system stability, system strength, weak power system

Procedia PDF Downloads 43