Search results for: supply and demand prediction

Authors: Yujiang Wu

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As electrical machine (e-machine) power density re-querulents become more stringent in vehicle electrification, mounting a temperature sensor for e-machine stator windings becomes increasingly difficult. This can lead to higher manufacturing costs, complicated harnesses, and reduced reliability. In this paper, we propose a deep-learning method for predicting electric machine winding temperature, which can either replace the sensor entirely or serve as a backup to the existing sensor. We compare the performance of our method, the stateful long short-term memory networks (LSTM) with truncated backpropagation through time (TBTT), with that of linear regression, as well as stateless LSTM with/without residual connection. Our results demonstrate the strength of combining stateful LSTM and TBTT in tackling nonlinear time series prediction problems with long sequence lengths. Additionally, in industrial applications, high-temperature region prediction accuracy is more important because winding temperature sensing is typically used for derating machine power when the temperature is high. To evaluate the performance of our algorithm, we developed a temperature-stratified MSE. We propose a simple but effective data preprocessing trick to improve the high-temperature region prediction accuracy. Our experimental results demonstrate the effectiveness of our proposed method in accurately predicting winding temperature, particularly in high-temperature regions, while also reducing manufacturing costs and improving reliability.

Keywords: deep learning, electrical machine, functional safety, long short-term memory networks (LSTM), thermal management, time series prediction

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Authors: A. J. Regan, F. J. Lidgey, M. Betteridge, P. Georgiou, C. Toumazou, K. Hayatleh, J. R. Dibble

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Our goal is development of an algorithm capable of predicting the directional trend of the Standard and Poor’s 500 index (S&P 500). Extensive research has been published attempting to predict different financial markets using historical data testing on an in-sample and trend basis, with many authors employing excessively complex mathematical techniques. In reviewing and evaluating these in-sample methodologies, it became evident that this approach was unable to achieve sufficiently reliable prediction performance for commercial exploitation. For these reasons, we moved to an out-of-sample strategy based on linear regression analysis of an extensive set of financial data correlated with historical closing prices of the S&P 500. We are pleased to report a directional trend accuracy of greater than 55% for tomorrow (t+1) in predicting the S&P 500.

Keywords: financial algorithm, GPU, S&P 500, stock market prediction

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Authors: Bita Mashayekhi, Azadeh Maddahi, Arash Tahriri

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The purpose of this paper is developing a model for audit quality, with regard to the contextual and environmental attributes of the audit profession in Iran. For this purpose, using an exploratory approach, and because of the special attributes of the auditing profession in Iran in terms of the legal environment, regulatory and supervisory mechanisms, audit firms size, and etc., we used grounded theory approach as a qualitative research method. Therefore, we got the opinions of the experts in the auditing and capital market areas through unstructured interviews. As a result, the authors revealed the determinants of audit quality, and by using these determinants, developed an Integrated Audit Quality Model, including causal conditions, intervening conditions, context, as well as action strategies related to AQ and their consequences. In this research, audit quality is studied using a systemic approach. According to this approach, the quality of inputs, processes, and outputs of auditing determines the quality of auditing, therefore, the quality of all different parts of this system is considered.

Keywords: audit quality, integrated audit quality model, demand for audit service, supply of audit, grounded theory

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Authors: Ntandoyenkosi Moyo

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Majority of rural Eastern Cape villages of South Africa households do not have access to safe water supply. Due to changes in climatic conditions for example higher temperatures, these sources become not reliable in supplying adequate and safe water to the population. These rural populations due to the drying up of water resources have to find other alternative ways to get water. Climate change has an impact on the reliability of water resources and this has an impact on rural communities. This study seeks to establish what alternative ways do people use when affected by unfavorable conditions like less rainfall and increased temperatures. The study also seeks to investigate any local and provincial intervention in the provision of water to the village. Interventions can be in the form of programmes or initiatives that involve water supply strategies. The community should participate fully in making sure that their place is serviced. The study will identify households with improved sources (JOJO tanks) and those with unimproved sources (rivers) and investigate what alternatives they resort to when their sources dry up. The study also investigates community views on whether they have any challenges of water supply (reliability and adequacy) as required by section 27(1) (b) of the constitution which states that everyone should have access to safe and clean water.

Keywords: rural water resources, temperature, improved sources, unimproved sources

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Authors: Lei Zhao, Zhe Yuan, Wenyue Kuang

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This paper studies two-warehouse inventory models for a deteriorating item considering that the demand is influenced by inventory levels. The problem mainly focuses on the optimal order policy and the optimal order cycle with inventory-level-dependent demand in two-warehouse system for retailers. It considers the different deterioration rates and the inventory holding costs in owned warehouse (OW) and rented warehouse (RW), and the conditions of transportation cost, allowed shortage and partial backlogging. Two inventory models are formulated: last-in first-out (LIFO) model and first-in-first-out (FIFO) model based on the policy choices of LIFO and FIFO, and a comparative analysis of LIFO model and FIFO model is made. The study finds that the FIFO policy is more in line with realistic operating conditions. Especially when the inventory holding cost of OW is high, and there is no difference or big difference between deterioration rates of OW and RW, the FIFO policy has better applicability. Meanwhile, this paper considers the differences between the effects of warehouse and shelf inventory levels on demand, and then builds retailers’ inventory decision model and studies the factors of the optimal order quantity, the optimal order cycle and the average inventory cost per unit time. To minimize the average total cost, the optimal dispatching policies are provided for retailers’ decisions.

Keywords: FIFO model, inventory-level-dependent, LIFO model, two-warehouse inventory

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Authors: Amar Omeiri

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This paper presents a three-phase shunt active power filter for current harmonics suppression and reactive power compensation using the supply current as reference. The proposed APF has a simple control circuit; it consists of detecting the supply current instead of the load current. The advantages of this APF are simplicity of control circuits and low implementation cost. The simulation results show that the proposed APF can compensate the reactive power and suppress current harmonics with two types of non-linear loads.

Keywords: active power filter, current harmonics and reactive power compensation, PWM inverter, Total Harmonic Distortion, power quality

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Authors: Dominique Arrouays, Songchao Chen, Anne C. Richer-De-Forges

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Soils are at the crossing of many issues such as food and water security, sustainable energy, climate change mitigation and adaptation, biodiversity protection, human health and well-being. They deliver many ecosystem services that are essential to life on Earth. Therefore, there is a growing demand for soil information on a national and global scale. Unfortunately, many countries do not have detailed soil maps, and, when existing, these maps are generally based on more or less complex and often non-harmonized soil classifications. An estimate of their uncertainty is also often missing. Thus, there are not easy to understand and often not properly used by end-users. Therefore, there is an urgent need to provide end-users with spatially exhaustive grids of essential soil properties, together with an estimate of their uncertainty. One way to achieve this is digital soil mapping (DSM). The concept of DSM relies on the hypothesis that soils and their properties are not randomly distributed, but that they depend on the main soil-forming factors that are climate, organisms, relief, parent material, time (age), and position in space. All these forming factors can be approximated using several exhaustive spatial products such as climatic grids, remote sensing products or vegetation maps, digital elevation models, geological or lithological maps, spatial coordinates of soil information, etc. Thus, DSM generally relies on models calibrated with existing observed soil data (point observations or maps) and so-called “ancillary co-variates” that come from other available spatial products. Then the model is generalized on grids where soil parameters are unknown in order to predict them, and the prediction performances are validated using various methods. With the growing demand for soil information at a national and global scale and the increase of available spatial co-variates national and continental DSM initiatives are continuously increasing. This short review illustrates the main national and continental advances in Europe, the diversity of the approaches and the databases that are used, the validation techniques and the main scientific and other issues. Examples from several countries illustrate the variety of products that were delivered during the last ten years. The scientific production on this topic is continuously increasing and new models and approaches are developed at an incredible speed. Most of the digital soil mapping (DSM) products rely mainly on machine learning (ML) prediction models and/or the use or pedotransfer functions (PTF) in which calibration data come from soil analyses performed in labs or for existing conventional maps. However, some scientific issues remain to be solved and also political and legal ones related, for instance, to data sharing and to different laws in different countries. Other issues related to communication to end-users and education, especially on the use of uncertainty. Overall, the progress is very important and the willingness of institutes and countries to join their efforts is increasing. Harmonization issues are still remaining, mainly due to differences in classifications or in laboratory standards between countries. However numerous initiatives are ongoing at the EU level and also at the global level. All these progress are scientifically stimulating and also promissing to provide tools to improve and monitor soil quality in countries, EU and at the global level.

Keywords: digital soil mapping, global soil mapping, national and European initiatives, global soil mapping products, mini-review

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Authors: S. Jayasinghe, R. B. N. Dissanayake

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Optimization modelling together with the Network models and Linear Programming techniques is a powerful tool in problem solving and decision making in real world applications. This study developed a mathematical model to optimize the net profit by minimizing the transportation cost. This model focuses the transportation among decentralized production plants to a centralized distribution centre and then the distribution among island wide agencies considering the customer satisfaction as a requirement. This company produces basically 9 types of food items with 82 different varieties and 4 types of non-food items with 34 different varieties. Among 6 production plants, 4 were located near the city of Mawanella and the other 2 were located in Galewala and Anuradhapura cities which are 80 km and 150 km away from Mawanella respectively. The warehouse located in the Mawanella was the main production plant and also the only distribution plant. This plant distributes manufactured products to 39 agencies island-wide. The average values and average amount of the goods for 6 consecutive months from May 2013 to October 2013 were collected and then average demand values were calculated. The following constraints are used as the necessary requirement to satisfy the optimum condition of the model; there was one source, 39 destinations and supply and demand for all the agencies are equal. Using transport cost for a kilometer, total transport cost was calculated. Then the model was formulated using distance and flow of the distribution. Network optimization and linear programming techniques were used to originate the model while excel solver is used in solving. Results showed that company requires total transport cost of Rs. 146, 943, 034.50 to fulfil the customers’ requirement for a month. This is very much less when compared with data without using the model. Model also proved that company can reduce their transportation cost by 6% when distributing to island-wide customers. Company generally satisfies their customers’ requirements by 85%. This satisfaction can be increased up to 97% by using this model. Therefore this model can be used by other similar companies in order to reduce the transportation cost.

Keywords: mathematical model, network optimization, linear programming

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Authors: Safa’ Abdelkarim Hammad

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The electrical power system is one of the main parts of the nation's infrastructure, and the availability and cost of electricity are critical factors in industrial competitiveness and strategy. Restructuring of the electricity supply industries is a very complex exercise based on national energy strategies and policies, macroeconomic developments, and national conditions, and its application varies from country to country. Electricity regulation of natural monopolies is a challenging task. Regulators face the problem of providing appropriate incentives for improvement of efficiency. Incentive regulation is often considered as an efficient regulatory tool to handle the problem, and it is widely applied in several countries. However, the exact regulation methodologies differ from one country to another. Network quantitative reliability evaluation is an essential factor with regard to the quality of supply. The main factors used to judge the reliability of supply is measured by the number and duration of interruptions experienced by customers. Several indicators are used to evaluate reliability in distribution networks. This paper addresses the impact of incentive regulation and performance benchmarking in the field of electricity distribution in Jordan. The theory of efficiency measurement and the most common models; NCSQS and DEA models are presented.

Keywords: incentive regulations, reliability, restructuring, Tarrif

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Authors: Rahul Ravindranathan, R. P. Gokul

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India, in its efforts towards economic and social development, is currently experiencing a heavy demand for energy. Due to the lack of sufficient domestic energy reserves, the country is highly dependent on energy imports which has increased rapidly at a rate of about 12 % per annum since 2005. Hence, India is currently focusing its efforts to manage this energy supply and demand gap and eventually achieve energy security. One of the most cost effective means to reduce this gap is by adopting Energy efficiency measures in the country. Initial assessments have shown that Energy efficiency measures have an energy conservation potential of about 23%. For an estimated investment potential of USD 8 Billion, the annual energy savings was estimated to be about 180 Billion Units per annum. In order to explore this huge energy conservation potential, many critical factors need to be considered to achieve practical energy savings. Financing options for these investments is one such major factor. Not only has India come out with various policy level as well as technology level drives to promote Energy efficiency but it has also developed various financing schemes to promote investment in Energy Efficiency projects. The Public sector has already come out with certain financing schemes such as the Partial Risk Guarantee Fund (PRGF), Venture Capital Fund (VCF), Partial Risk Sharing Fund (PRSF) etc., and various sectors are gradually utilizing these schemes to implement energy saving measures. However, additional financing options are required in order to explore the untouched energy conservation potential in the country. Hence, there is a need to develop some innovative financing options for India which would motivate the private sectors as well as financing institutions to invest in these energy saving measures. This paper shall review the existing financing schemes launched by the Government of India and highlight the key benefits as well as challenges with respect to these schemes. In addition to this, the paper would also review new and innovative financing schemes for India and how the same could be adopted in other parts of the globe especially in South and South East Asia. This review would provide an insight to the various Governments as well as Financial Institutions in coming out with new financing schemes for their country.

Keywords: energy, efficiency, financing, India

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Authors: Jong Won, Park, Sung Hyun, Kim

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The kinetic energy to pass subjected to shock and chisel reciprocating piston hydraulic power supplied by the excavator using for the purpose of crushing the rock, and roads, buildings, etc., hydraulic breakers blow. Impact frequency, efficiency measurement of the impact energy, hydraulic breakers, to demonstrate the ability of hydraulic breaker manufacturers and users to a very important item. And difficult in order to confirm the initial performance degradation in the life of the hydraulic breaker has been thought to be a problem.In this study, we measure the efficiency of hydraulic breaker, Impact energy and Impact frequency, the degradation analysis of research to predict the life.

Keywords: impact energy, impact frequency, hydraulic breaker, life prediction

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Authors: Deepak Raj Bhat, Ryuichi Yatabe

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In this study, a residual-state creep failure model was developed based on the residual-state creep test results of clayey soils. To develop the proposed model, the regression analyses were done by using the R. The model results of the failure time (tf) and critical displacement (δc) were compared with experimental results and found in close agreements to each others. It is expected that the proposed regression model for residual-state creep failure will be more useful for the prediction of displacement of different clayey soils in the future.

Keywords: regression model, residual-state creep failure, displacement prediction, clayey soils

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Authors: Sharvari Sane, Swati Sharma, Sanjay K. Prasad

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Local trains are the lifelines of Mumbai city. Earlier 1500 Volt D.C. supply, is now completely and successfully converted into 25 KV A.C. in central, western and harbour routes. This task is the outcome of the advancement in the area of power electronics. Author has already done the comparative study between D.C. and A.C. supply of traction and predicted the serious problem regarding the harmonics. In this paper, the simulation for 5 level as well as 7 level multilevel inverter has been done which is the substitute for the present cascade type inverter. This paper also showed the reduced level of Total Harmonic Distortion (THD) in the traction system.

Keywords: total harmonic distortion (THD), traction sub station (TSS), harmonics, multilevel inverter

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Authors: Nuwan Kuruwitaarachchi, M. K. M. Peiris, C. N. Madawala, K. M. A. R. Perera, V. U. N Perera

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Dealing with businesses with the foreign market always took a special place in a country’s economy. Political and social factors came into play making currency rate changes fluctuate rapidly. Currency rate prediction has become an important factor for larger international businesses since large amounts of money exchanged between countries. This research focuses on comparing the accuracy of mainly three models; Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Networks(ANN) and Support Vector Machines(SVM). series of data import, export, USD currency exchange rate respect to LKR has been selected for training using above mentioned algorithms. After training the data set and comparing each algorithm, it was able to see that prediction in SVM performed better than other models. It was improved more by combining SVM and SVR models together.

Keywords: ARIMA, ANN, FFNN, RMSE, SVM, SVR

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Authors: Hassan Baji, Chun-Qing Li, Wei Yang

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Water seepage is one of the most common causes of damage in tunnel structures, which can cause direct and indirect e.g. reinforcement corrosion and calcium leaching damages. Estimation of water seepage or inflow is one of the main challenges in probabilistic assessment of tunnels. The methodology proposed in this study is an attempt for mathematically modeling the water seepage in tunnel structures and further predicting its service life. Using the time-dependent reliability, water seepage is formulated as a failure mode, which can be used for prediction of service life. Application of the formulated seepage failure mode to a case study tunnel is presented.

Keywords: water seepage, tunnels, time-dependent reliability, service life

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Authors: Chuan-Ming Tung, Tzu-Chiao Yuan

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This research aims to construct the supply indicators and weights of shelter space from a perspective of the needs of the elderly by virtue of literature review, a systematical compilation of related regulations, and the use of the Analytical Hierarchy Process method, the questionnaires regarding the indicators filled out by 16 experts and scholars. The researcher then used 3 schools and 2 activity centers in Banqiao District, New Taipei City, as study cases to evaluate the ‘friendliness’ degree/level for the supply of shelters meeting the needs of elderly people. The supply evaluation indicators of friendly shelters meeting the needs of the elderly include "Administrative Operations and Service Needs" and "Residence-related and Living Needs"; under the "Administrative Operations and Service Needs" are "Management Operations and Information Provision", "Shelter Space Preparedness and Logistics Support", "Medical Care and Social Support", and "Shelters and Medical Environment", a total of 17 assessment items in four indicators, while under the "Residence-related and Living Needs" are "Dietary Needs", "Sleep Needs", "Hygiene and Sanitation Needs", "Accessibility and Convenience Needs ", etc., a total of 18 assessment items in four indicators. The results show that "Residence-related and Living Needs" is the most important item in the main levels of the supply indicators of the needs for friendly shelters to elderly people (weigh value 0.5504), followed by "Administrative Operations and Service Needs" (0.4496). The order of importance of the supply indicators of friendly shelters for the needs of elderly people is as follows: "Hygiene and Sanitation Needs" (0.1721), "Dietary Needs" (0.1340), "Medical Care and Social Support" (0.1300), "Sleep Needs" (0.1277), "Accessibility and Convenience Needs" (0.1166), "Basic Environment of Shelters" (0.1145), "Shelter Space Preparedness and Logistics Support" (0.1115) and "Management Operations and Information Provision" (0.0936). In addition, it can be noticed from the results of the case evaluation that the provision of refuges and shelters, mainly from schools and activity centers, is extremely inadequate for the needs of the elderly. In a set of comprehensive comparisons and contrasts, the evaluation indicators of refuges and shelters that need to be improved are "Medical Care and Social Support", "Hygiene and Sanitation Needs", "Sleep Needs", "Dietary Needs", and "Shelter Space Preparedness and Logistics Support".

Keywords: needs of the elderly people, urban shelters, evaluation indicators/indices., taiwan

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Authors: Fitri CaturLestari

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According to National Population and Family Planning Board (BKKBN), demographic bonus will occur in 2025-2035, when the number of people within the productive age bracket is higher than the number of elderly people and children. This time will be a gold moment for Indonesia to achieve maximum productivity and prosperity. But it will be a demographic bomb if it isn’t balanced by economic and social aspect considerations. Therefore it is important to make a prediction mapping of all provinces in Indonesia whether in demographic bomb or bonus condition after 100 years Indonesian independence. The purpose of this research were to make the demographic mapping based on the economic and social aspects of the provinces in Indonesia and categorizing them into demographic bomb and bonus condition. The research data are gained from Statistics Indonesia (BPS) as the secondary data. The multiregional component method, regression and quadrant analysis were used to predict the number of people, economic growth, Human Development Index (HDI), and gender equality in education and employment. There were different characteristic of provinces in Indonesia from economic aspect and social aspect. The west Indonesia was already better developed than the east one. The prediction result, many provinces in Indonesia will get demographic bonus but the others will get demographic bomb. It is important to prepare particular strategy to particular provinces with all of their characteristic based on the prediction result so the demographic bomb can be minimalized.

Keywords: demography, economic growth, gender, HDI

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Authors: Senol Dogan, Gunay Karli

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Identification of relevant publications based on a Medline query is time-consuming and error-prone. An all based process has the potential to solve this problem without any manual work. To the best of our knowledge, our study is the first to investigate the ability of machine learning to identify relevant articles accurately. 5 different machine learning algorithms were tested using 23 predictors based on several metadata fields attached to publications. We find that the Boosted model is the best-performing algorithm and its overall accuracy is 96%. In addition, specificity and sensitivity of the algorithm is 97 and 93%, respectively. As a result of the work, we understood that we can apply the same procedure to understand cancer gene expression big data.

Keywords: prediction of publications, machine learning, algorithms, bariatric surgery, comparison of algorithms, boosted, tree, logistic regression, ANN model

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Authors: Yasin Tadayonrad

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Most of the supply chain networks have many nodes starting from the suppliers’ side up to the customers’ side that each node sends/receives the raw materials/products from/to the other nodes. One of the major concerns in this kind of supply chain network is finding the best schedule for loading /unloading the shipments through the whole network by which all the constraints in the source and destination nodes are met and all the shipments are delivered on time. One of the main constraints in this problem is loading/unloading capacity in each source/ destination node at each time slot (e.g., per week/day/hour). Because of the different characteristics of different products/groups of products, the capacity of each node might differ based on each group of products. In most supply chain networks (especially in the Fast-moving consumer goods industry), there are different planners/planning teams working separately in different nodes to determine the loading/unloading timeslots in source/destination nodes to send/receive the shipments. In this paper, a mathematical problem has been proposed to find the best timeslots for loading/unloading the shipments minimizing the overall delays subject to respecting the capacity of loading/unloading of each node, the required delivery date of each shipment (considering the lead-times), and working-days of each node. This model was implemented on python and solved using Python-MIP on a sample data set. Finally, the idea of a heuristic algorithm has been proposed as a way of improving the solution method that helps to implement the model on larger data sets in real business cases, including more nodes and shipments.

Keywords: supply chain management, transportation, multiple-multiple network, timeslots management, mathematical modeling, mixed integer programming

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Authors: Ana Julia Acevedo Urquiaga, Jose A. Acevedo Suarez, Ana Julia Urquiaga Rodriguez, Neyfe Sablon Cossio

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Despite the progress made in logistics and supply chains fields, it is unavoidable the development of business models that use efficiently information to facilitate the integrated logistics flows management between partners. Collaborative management is an important tool for materializing the cooperation between companies, as a way to achieve the supply chain efficiency and effectiveness. The first face of this research was a comprehensive analysis of the collaborative planning on the Cuban companies. It is evident that they have difficulties in supply chains planning where production, supplies and replenishment planning are independent tasks, as well as logistics and distribution operations. Large inventories generate serious financial and organizational problems for entities, demanding increasing levels of working capital that cannot be financed. Problems were found in the efficient application of Information and Communication Technology on business management. The general objective of this work is to develop a methodology that allows the deployment of a planning and control system in a coordinated way on the medicine’s logistics system in Cuba. To achieve these objectives, several mechanisms of supply chain coordination, mathematical programming models, and other management techniques were analyzed to meet the requirements of collaborative logistics management in Cuba. One of the findings is the practical and theoretical inadequacies of the studied models to solve the current situation of the Cuban logistics systems management. To contribute to the tactical-operative management of logistics, the Collaborative Logistics Flow Management Model (CLFMM) is proposed as a tool for the balance of cycles, capacities, and inventories, always to meet the final customers’ demands in correspondence with the service level expected by these. The CLFMM has as center the supply chain planning and control system as a unique information system, which acts on the processes network. The development of the model is based on the empirical methods of analysis-synthesis and the study cases. Other finding is the demonstration of the use of a single information system to support the supply chain logistics management, allows determining the deadlines and quantities required in each process. This ensures that medications are always available to patients and there are no faults that put the population's health at risk. The simulation of planning and control with the CLFMM in medicines such as dipyrone and chlordiazepoxide, during 5 months of 2017, permitted to take measures to adjust the logistic flow, eliminate delayed processes and avoid shortages of the medicines studied. As a result, the logistics cycle efficiency can be increased to 91%, the inventory rotation would increase, and this results in a release of financial resources.

Keywords: collaborative management, medicine logistic system, supply chain planning, tactical-operative planning

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Authors: Shauma L. Tamba

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This study examined whether morality is the most important aspect in actual behavior. The prediction was that people tend to behave in line with moral (as compared to competence) norms, especially when such norms are presented by their ingroup. The actual behavior that was tested was support for a military intervention without a mandate from the UN. In addition, this study also examined whether identification with the ingroup and religion moderated the effect of group and norm on support for the norm that was prescribed by their ingroup. The prediction was that those who identified themselves higher with the ingroup moral would show a higher support for the norm. Furthermore, the prediction was also that those who have religion would show a higher support for the norm in the ingroup moral rather than competence. In an online survey, participants were asked to read a scenario in which a military intervention without a mandate was framed as either the moral (but stupid) or smart (but immoral) thing to do by members of their own (ingroup) or another (outgroup) society. This study found that when people identified themselves with the smart (but immoral) norm, they showed a higher support for the norm. However, when people identified themselves with the moral (but stupid) norm, they tend to show a lesser support towards the norm. Most of the results in the study did not support the predictions. Possible explanations and implications are discussed.

Keywords: morality, competence, ingroup identification, religion, group norm

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Authors: Nima Dastanboo, Xiao-Qing Li, Hamed Gharibdoost

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The purpose of this study is to investigate about the geological properties ahead of a tunnel face with using Electrical Resistivity Tomography ERT and Tunnel Seismic Prediction TSP303 methods. In deep tunnels with hydro-geological conditions, it is important to study the geological structures of the region before excavating tunnels. Otherwise, it would lead to unexpected accidents that impose serious damage to the project. For constructing Nosoud tunnel in west of Iran, the ERT and TSP303 methods are employed to predict the geological conditions dynamically during the excavation. In this paper, based on the engineering background of Nosoud tunnel, the important results of applying these methods are discussed. This work demonstrates seismic method and electrical tomography as two geophysical techniques that are able to detect a tunnel. The results of these two methods were being in agreement with each other but the results of TSP303 are more accurate and quality. In this case, the TSP 303 method was a useful tool for predicting unstable geological structures ahead of the tunnel face during excavation. Thus, using another geophysical method together with TSP303 could be helpful as a decision support in excavating, especially in complicated geological conditions.

Keywords: tunnel seismic prediction (TSP303), electrical resistivity tomography (ERT), seismic wave, velocity analysis, low-velocity zones

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Authors: Behzad Behnia, Noah LaRussa-Trott

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In recent years, fibers have been successfully used as an additive to reinforce asphalt concrete materials and to enhance the sustainability and resiliency of transportation infrastructure. Roads covered with fiber-reinforced asphalt concrete (FRAC) require less frequent maintenance and tend to have a longer lifespan. The present work investigates the application of sasobit-coated aramid fibers in asphalt pavements and employs machine learning to develop prediction models to evaluate the cracking performance of FRAC materials. For the experimental part of the study, the effects of several important parameters such as fiber content, fiber length, and testing temperature on fracture characteristics of FRAC mixtures were thoroughly investigated. Two mechanical performance tests, i.e., the disk-shaped compact tension [DC(T)] and indirect tensile [ID(T)] strength tests, as well as the non-destructive acoustic emission test, were utilized to experimentally measure the cracking behavior of the FRAC material in both macro and micro level, respectively. The experimental results were used to train the supervised machine learning approach in order to establish prediction models for fracture performance of the FRAC mixtures in the field. Experimental results demonstrated that adding fibers improved the overall fracture performance of asphalt concrete materials by increasing their fracture energy, tensile strength and lowering their 'embrittlement temperature'. FRAC mixtures containing long-size fibers exhibited better cracking performance than regular-size fiber mixtures. The developed prediction models of this study could be easily employed by pavement engineers in the assessment of the FRAC pavements.

Keywords: fiber reinforced asphalt concrete, machine learning, cracking performance tests, prediction model

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Authors: Y. Ghiassi-Farrokhfal

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The number of electric vehicles (EVs) is increasing worldwide. Replacing gas fueled cars with EVs reduces carbon emission. However, the extensive energy consumption of EVs stresses the energy systems, requiring non-green sources of energy (such as gas turbines) to compensate for the new energy demand caused by EVs in the energy systems. To make EVs even a greener solution for the future energy systems, new EV charging stations are equipped with solar PV panels and batteries. This will help serve the energy demand of EVs through the green energy of solar panels. To ensure energy availability, solar panels are combined with batteries. The energy surplus at any point is stored in batteries and is used when there is not enough solar energy to serve the demand. While EV charging stations equipped with solar panels and batteries are green and ecologically optimal, they might not be financially viable solutions, due to battery prices. To make the system viable, we should size the battery economically and operate the system optimally. This is, in general, a challenging problem because of the stochastic nature of the EV arrivals at the charging station, the available solar energy, and the battery operating system. In this work, we provide a mathematical model for this problem and we compute the return on investment (ROI) of such a system, which is designed to be ecologically and financially optimal. We also quantify the minimum required investment in terms of battery and solar panels along with the operating strategy to ensure that a charging station has enough energy to serve its EV demand at any time.

Keywords: solar energy, battery storage, electric vehicle, charging stations

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Authors: Yusuf S. Dambatta, Ahmed A. D. Sarhan

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Fused deposition modelling (FDM) is one of the most prominent rapid prototyping (RP) technologies which is being used to efficiently fabricate CAD 3D geometric models. However, the process is coupled with many drawbacks, of which the surface quality of the manufactured RP parts is among. Hence, studies relating to improving the surface roughness have been a key issue in the field of RP research. In this work, a technique of modelling the surface roughness in FDM is presented. Using experimentally measured surface roughness response of the FDM parts, an ANFIS prediction model was developed to obtain the surface roughness in the FDM parts using the main critical process parameters that affects the surface quality. The ANFIS model was validated and compared with experimental test results.

Keywords: surface roughness, fused deposition modelling (FDM), adaptive neuro fuzzy inference system (ANFIS), orientation

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Authors: Aneta Oblouková, Eva Vítková

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The article deals with the issue of water and sewerage charge rate prices in the Czech Republic. The research is specifically focused on the analysis of the development of the average prices of water and sewerage charge rate in the Czech Republic in the years 1994-2021 and on the validation of the chosen methodology relevant for the prediction of the development of the average prices of water and sewerage charge rate in the Czech Republic. The research is based on data collection. The data for this research was obtained from the Czech Statistical Office. The aim of the paper is to validate the relevance of the mathematical linear trend estimate technique for the calculation of the predicted average prices of water and sewerage charge rates. The real values of the average prices of water and sewerage charge rates in the Czech Republic in the years 1994-2018 were obtained from the Czech Statistical Office and were converted into a mathematical equation. The same type of real data was obtained from the Czech Statistical Office for the years 2019-2021. Prediction of the average prices of water and sewerage charge rates in the Czech Republic in the years 2019-2021 were also calculated using a chosen method -a linear trend estimation technique. The values obtained from the Czech Statistical Office and the values calculated using the chosen methodology were subsequently compared. The research result is a validation of the chosen mathematical technique to be a suitable technique for this research.

Keywords: Czech Republic, linear trend estimation, price prediction, water and sewerage charge rate

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Authors: Malo Pocheau-Lesteven, Olivier Le Maître

Abstract:

Engineering and optimisation of complex systems is often achieved through multi-disciplinary analysis of the system, where each subsystem is modeled and interacts with other subsystems to model the complete system. The coherence of the output of the different sub-systems is achieved through the use of compatibility constraints, which enforce the coupling between the different subsystems. Due to the complexity of some sub-systems and the computational cost of evaluating their respective models, it is often necessary to build surrogate models of these subsystems to allow repeated evaluation these subsystems at a relatively low computational cost. In this paper, gaussian processes are used, as their probabilistic nature is leveraged to evaluate the likelihood of satisfying the compatibility constraints. This paper presents infilling strategies to build accurate surrogate models of the subsystems in areas where they are likely to meet the compatibility constraint. It is shown that these infilling strategies can reduce the computational cost of building surrogate models for a given level of accuracy. An application of these methods to velocity prediction programs used in offshore racing naval architecture further demonstrates these method's applicability in a real engineering context. Also, some examples of the application of uncertainty quantification to field of naval architecture are presented.

Keywords: infilling strategy, gaussian process, multi disciplinary analysis, velocity prediction program

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Authors: Florian Kleber, Martin Kampel

Abstract:

The increasing demand of gallium, indium and rare-earth elements for the production of electronics, e.g. solid state-lighting, photovoltaics, integrated circuits, and liquid crystal displays, will exceed the world-wide supply according to current forecasts. Recycling systems to reclaim these materials are not yet in place, which challenges the sustainability of these technologies. This paper proposes a multispectral imaging system as a basis for a vision based recognition system for valuable components of electronics waste. Multispectral images intend to enhance the contrast of images of printed circuit boards (single components, as well as labels) for further analysis, such as optical character recognition and entire printed circuit board recognition. The results show that a higher contrast is achieved in the near infrared compared to ultraviolet and visible light.

Keywords: electronics waste, multispectral imaging, printed circuit boards, rare-earth elements

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Authors: Aragorn Joaquin Pineda Dela Cruz

Abstract:

Road traffic congestion is continually deteriorating in Hong Kong. The largest contributing factor is the increase in vehicle fleet size, resulting in higher competition over the utilisation of road space. This study proposes a project that can process closed-circuit television images and videos to provide real-time traffic detection and prediction capabilities. Specifically, a deep-learning model involving computer vision techniques for video and image-based vehicle counting, then a separate model to detect and predict traffic congestion levels based on said data. State-of-the-art object detection models such as You Only Look Once and Faster Region-based Convolutional Neural Networks are tested and compared on closed-circuit television data from various major roads in Hong Kong. It is then used for training in long short-term memory networks to be able to predict traffic conditions in the near future, in an effort to provide more precise and quicker overviews of current and future traffic conditions relative to current solutions such as navigation apps.

Keywords: intelligent transportation system, vehicle detection, traffic analysis, deep learning, machine learning, computer vision, traffic prediction

Procedia PDF Downloads 92

Authors: Raid Hommady, Waseem Al-Talalwah, Shorok Al Dorazi, Roger Soames

Abstract:

The iliolumbar artery is a regular branch of posterior division of the internal iliac artery. The present study investigate 82 specimens to identify the origin of iliolumbar artery. The present study targets the sciatic nerve root supply from iliolumbar artery based on its origin and course. In present study, the ililumbar artery arose from the posterior division of internal iliac artery in 52.2%. In few cases, it arose from dorsomedial aspect of the internal iliac artery in 28.8%. In few cases, the iliolumbar artery arose from the dorsal aspects of the internal iliac artery as well as from the common and external iliac artery 1.7%. Also, the iliolumbar artery arose from the sciatic artery as well as from superior and inferior gluteal arteries in 5.1%. Conversely, it found to be congenital absent in 8.5%. Therefore, the posterior trunk of the internal iliac artery is the most common origin of the iliolumbar artery. With the origin variability of the iliolumbar artery, there is a vascular supply variability of the lumbosacral trunk and sacral root of sciatic nerve. The iliolumbar artery provides vascular supply for lumbosacral trunk 57.3% in whereas the sacral root in 5.1%. As a result, surgeons should pay attention to these variations to decrease iatrogenic fault.

Keywords: iliolumbar, sciatic artery, internal iliac, external iliac, posterior division

Procedia PDF Downloads 300