Search results for: MSW quantity prediction

Authors: Veronica Kah Jo Wong, Yoke Mui Lim, Nurul Sakina Mokhtar Azizi

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The impact on the construction industry in Malaysia is unprecedented, as the government implemented a lockdown to restrict human movement in an effort to stop COVID-19 from spreading. Quantity surveyor (QS), as one of the key construction professionals, found that the working practices and environments for quantity surveyors today have changed due to the current pandemic. The QS profession must deal not only with changes in project issues but also with a different working environment in which most people are required to work from home and follow the standard operating procedures. Therefore, QS should be flexible, agile, and have the capability to adapt to the current working practices by strengthening their competencies. Adapting to the current and recovering environment of COVID-19 may result in the emergence of a new competence such as skill and knowledge for QS in order to maintain the quality of performance in the delivery of their professional services. Thus, this paper's objective is to investigate the changes in knowledge and skills in quantity surveyors. The data will be collected through interviews with registered professional QS to gain better insights that are specific in this industry, and the findings will be verified using the Delphi method. It is hoped that new knowledge and skill will be found from the study and will not only contribute to the betterment of the professional QS but also in guiding higher learning institutions to incorporate the new competencies into their curriculum.

Keywords: competency, COVID-19 pandemic, Malaysia, quantity surveying

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Authors: Olena Grygorieva

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Nowadays mechanisms of thymocytes emigration from the thymus to the periphery are investigated actively. We have proposed a hypothesis of thymocytes’ migration from the thymus through lymphatic vessels during periodical short-term local edema. By morphological, hystochemical methods we have examined quantity of lymphocytes, epitelioreticulocytes, mast cells, blood and lymphatic vessels in morpho-functional areas of rats’ thymuses during the first week after birth in 4 hours interval. In newborn and beginning from 8 hour after birth every 12 hours specific density of the thymus, absolute quantity of microcirculatory vessels, especially of lymphatic ones, lymphcyte-epithelial index, quantity of mast cells and their degranulative forms increase. Structure of extracellular matrix, intrathymical microenvironment and lymphocytes’ adhesive properties change. Absolute quantity of small lymphocytes in thymic cortex changes wavy. All these changes are straightly expressed from 0 till 2, from 12 till 16, from 108 till 120 hours of postnatal life. During this periods paravasal lymphatic vessels are stuffed with lymphocytes, i.e. discrete migration of lymphocytes from the thymus occurs. After rapid edema reduction, quantity of lymphatic vessels decrease, they become empty. Therefore, in the thymus of newborn periodical short-term local edema is observed, on its top discrete migration of lymphocytes from the thymus occurs.

Keywords: lymphocytes, lymphatic vessels, mast cells, thymus

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Authors: Muhammad Awais Shafique, Eiji Hato, Hideki Yaginuma

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Recently GPS data is used in a lot of studies to automatically reconstruct travel patterns for trip survey. The aim is to minimize the use of questionnaire surveys and travel diaries so as to reduce their negative effects. In this paper data acquired from GPS and accelerometer embedded in smart phones is utilized to predict the mode of transportation used by the phone carrier. For prediction, Support Vector Machine (SVM) and Adaptive boosting (AdaBoost) are employed. Moreover a unique method to improve the prediction results from these algorithms is also proposed. Results suggest that the prediction accuracy of AdaBoost after improvement is relatively better than the rest.

Keywords: accelerometer, AdaBoost, GPS, mode prediction, support vector machine

Procedia PDF Downloads 359

Authors: H. B. Torane, M. C. Kasture, S. S. Prabhudesai, P. B. Sanap, V. N. Palsande, J. J. Palkar

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The present investigation entitled “Response of briquettes application with different coating materials on yield and quality of Cucumber [Cucumis sativus (L.)]” was conducted at Central Experiment Center, Wakawali during kharif season 2013. The field experiment was laid out in Factorial Randomized Block Design with three replicate. The four coating materials viz., Co – Non coating, C1 – Wax coating, C2 – Jaggary coating, and C3 – Tar coating was applied to Konkan Annapurna Briquette along with three sub treatments of application time i.e B1 – ½ at sowing, B2 - ½ at sowing and ½ at 30 days after sowing and B3 - 1/3 at sowing, 1/3 at 30 days after sowing and 1/3 at 60 days after sowing. It was observed that the application of tar coated Konkan Annapurna Briquettes (KAB) in three times @1/3 quantity of briquettes at sowing time, 1/3 quantity of briquettes at 30 days after sowing and 1/3 quantity of briquettes at 60 days after sowing was found promising to enhancing the cucumber fruit yield, higher vine length, number of fruits vine-1, girth of fruit, length of fruit. It was also observed that the quality of the cucumber fruit increased in terms of ascorbic acid. UB-Godavari forms of briquettes .i.e. are promising source of N, P2O5 and K2O fertilizers as compared to straight fertilizers for enhancing green cucumber fruit yield of Sheetal variety of cucumber in lateritic soil. Amongst the three types of coated briquettes, the tar coated briquettes application was found to be superior for increasing cucumber fruit yield applied in three times @1/3 quantity of briquettes at sowing time, 1/3 quantity of briquettes at 30 days after sowing and 1/3 quantity of briquettes at 60 days after sowing @ 5 briquettes per plant at an interval of 30 days after sowing.

Keywords: briquettes, coating, yield, tar, wax and quality

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Authors: Carl van Walraven, Meltem Tuna

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Background: Network meta-analysis (NMA) quantifies the relative efficacy of 3 or more interventions from studies containing a subgroup of interventions. This study applied the analytical approach of NMA to quantify the relative accuracy of prediction models with distinct inclusion criteria that are evaluated on a common population (‘concurrent external validation’). Methods: We simulated binary events in 5000 patients using a known risk function. We biased the risk function and modified its precision by pre-specified amounts to create 15 prediction models with varying accuracy and distinct patient applicability. Prediction model accuracy was measured using the Scaled Brier Score (SBS). Overall prediction model accuracy was measured using fixed-effects methods that accounted for model applicability patterns. Prediction model accuracy was summarized as the Network Relative Model Accuracy (NeRMA) Score which ranges from -∞ through 0 (accuracy of random guessing) to 1 (accuracy of most accurate model in concurrent external validation). Results: The unbiased prediction model had the highest SBS. The NeRMA score correctly ranked all simulated prediction models by the extent of bias from the known risk function. A SAS macro and R-function was created to implement the NeRMA Score. Conclusions: The NeRMA Score makes it possible to quantify the accuracy of binomial prediction models having distinct inclusion criteria in a concurrent external validation.

Keywords: prediction model accuracy, scaled brier score, fixed effects methods, concurrent external validation

Procedia PDF Downloads 235

Authors: Arvinder Kaur, Deepti Chopra

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Software Entropy Metrics for bug prediction have been validated on various software systems by different researchers. In our previous research, we have validated that Software Entropy Metrics calculated for Mozilla subsystem’s predict the future bugs reasonably well. In this study, the Software Entropy metrics are calculated for a subsystem of Android and it is noticed that these metrics are not suitable for bug prediction. The results are compared with a subsystem of Mozilla and a comparison is made between the two software systems to determine the reasons why Software Entropy metrics are not applicable for Android.

Keywords: android, bug prediction, mining software repositories, software entropy

Procedia PDF Downloads 578

Authors: Keshab Shrestha, Hung-Suck Park

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Emergy accounting of the systems networks is guided by a definite rule called ‘emergy algebra’. The systems networks consist of two types of branching. These are the co-product branching and split branching. The emergy accounting procedure for both the branching types is different. According to the emergy algebra, each branch in the co-product branching has different transformity values whereas the split branching has the same transformity value. After the transformity value of each branch is determined, the emergy is calculated by multiplying this with the energy. The aim of this research is to solve the problems in determining the transformity values in the co-product branching through the introduction of a new methodology, the modified physical quantity method. Initially, the existing methodologies for emergy accounting in the co-product branching is discussed and later, the modified physical quantity method is introduced with a case study of the Eucalyptus pulp production. The existing emergy accounting methodologies in the co-product branching has wrong interpretations with incorrect emergy calculations. The modified physical quantity method solves those problems of emergy accounting in the co-product branching systems. The transformity value calculated for each branch is different and also applicable in the emergy calculations. The methodology also strictly follows the emergy algebra rules. This new modified physical quantity methodology is a valid approach in emergy accounting particularly in the multi-production systems networks.

Keywords: co-product branching, emergy accounting, emergy algebra, modified physical quantity method, transformity value

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Authors: Chang Su Woo, Hyun Sung Park

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Useful lifetime evaluation of chevron rubber spring was very important in design procedure to assure the safety and reliability. It is, therefore, necessary to establish a suitable criterion for the replacement period of chevron rubber spring. In this study, we performed characteristic analysis and useful lifetime prediction of chevron rubber spring. Rubber material coefficient was obtained by curve fittings of uni-axial tension, equi bi-axial tension and pure shear test. Computer simulation was executed to predict and evaluate the load capacity and stiffness for chevron rubber spring. In order to useful lifetime prediction of rubber material, we carried out the compression set with heat aging test in an oven at the temperature ranging from 50°C to 100°C during a period 180 days. By using the Arrhenius plot, several useful lifetime prediction equations for rubber material was proposed.

Keywords: chevron rubber spring, material coefficient, finite element analysis, useful lifetime prediction

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Authors: Amit R. Bhende, G. K. Awari

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Remaining useful life (RUL) prediction is one of key technologies to realize prognostics and health management that is being widely applied in many industrial systems to ensure high system availability over their life cycles. The present work proposes a data-driven method of RUL prediction based on multiple health state assessment for rolling element bearings. Bearing degradation data at three different conditions from run to failure is used. A RUL prediction model is separately built in each condition. Feed forward back propagation neural network models are developed for prediction modeling.

Keywords: bearing degradation data, remaining useful life (RUL), back propagation, prognosis

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Authors: Qiang Zhang, Chun Yuan

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Since the PU (Prediction Unit) decision process is the most time consuming part of the emerging HEVC (High Efficient Video Coding) standardin intra and inter frame coding, this paper proposes the fast PU decision algorithm and speed up the algorithm using CUDA (Compute Unified Device Architecture). In intra frame coding, the fast PU decision algorithm uses the texture features to skip intra-frame prediction or terminal the intra-frame prediction for smaller PU size. In inter frame coding of HEVC, the fast PU decision algorithm takes use of the similarity of its own two Nx2N size PU's motion vectors and the hierarchical structure of CU (Coding Unit) partition to skip some modes of PU partition, so as to reduce the motion estimation times. The accelerate algorithm using CUDA is based on the fast PU decision algorithm which uses the GPU to make the motion search and the gradient computation could be parallel computed. The proposed algorithm achieves up to 57% time saving compared to the HM 10.0 with little rate-distortion losses (0.043dB drop and 1.82% bitrate increase on average).

Keywords: HEVC, PU decision, inter prediction, intra prediction, CUDA, parallel

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Authors: J. K. Alhassan, C. S. Actsu

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This study is on the prediction of feature performance of undergraduate students with Artificial Neural Networks (ANN). With the growing decline in the quality academic performance of undergraduate students, it has become essential to predict the students’ feature academic performance early in their courses of first and second years and to take the necessary precautions using such prediction-based information. The feed forward multilayer neural network model was used to train and develop a network and the test carried out with some of the input variables. A result of 80% accuracy was obtained from the test which was carried out, with an average error of 0.009781.

Keywords: academic performance, artificial neural network, prediction, students

Procedia PDF Downloads 467

Authors: Ali Akber Jaffri, Asad Naseem, Javeria Khan, Zubair Hamza, Ishtiaq

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The purpose of this study is to determine safety stock, maximum inventory level, reordering point, and reordering quantity by rearranging lot sizes for supplier and customer in MRO (maintenance repair operations) warehouse of Engro Polymers & Chemicals. To achieve the aim, physical analysis method and excel commands were carried out to elicit the customer and supplier data provided by the company. Initially, we rearranged the current lot sizes and MOUs (measure of units) in SAP software. Due to change in lot sizes, we have to determine the new quantities for safety stock, maximum inventory, reordering point and reordering quantity as per company's demand. By proposed system, we saved extra cost in terms of reducing time of receiving from vendor and in issuance to customer, ease of material handling in MRO warehouse and also reduce human efforts.

Keywords: maintenance repair operation, maximum inventory, reorder quantity, safety stock

Procedia PDF Downloads 275

Authors: Mohammad Ghavami, Reza S. Dilmaghani

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This paper presents an adaptive framework for modelling financial markets using equity risk premiums, risk free rates and volatilities. The recorded economic factors are initially used to train four adaptive filters for a certain limited period of time in the past. Once the systems are trained, the adjusted coefficients are used for modelling and prediction of an important financial market index. Two different approaches based on least mean squares (LMS) and recursive least squares (RLS) algorithms are investigated. Performance analysis of each method in terms of the mean squared error (MSE) is presented and the results are discussed. Computer simulations carried out using recorded data show MSEs of 4% and 3.4% for the next month prediction using LMS and RLS adaptive algorithms, respectively. In terms of twelve months prediction, RLS method shows a better tendency estimation compared to the LMS algorithm.

Keywords: adaptive methods, LSE, MSE, prediction of financial Markets

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Authors: Emel Kızılkaya Aydogan, Mihrimah Ozmen, Yılmaz Delice

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In recent days, there is a change and the ongoing development of the telecommunications sector in the global market. In this sector, churn analysis techniques are commonly used for analysing why some customers terminate their service subscriptions prematurely. In addition, customer churn is utmost significant in this sector since it causes to important business loss. Many companies make various researches in order to prevent losses while increasing customer loyalty. Although a large quantity of accumulated data is available in this sector, their usefulness is limited by data quality and relevance. In this paper, a cost-sensitive feature selection framework is developed aiming to obtain the feature reducts to predict customer churn. The framework is a cost based optional pre-processing stage to remove redundant features for churn management. In addition, this cost-based feature selection algorithm is applied in a telecommunication company in Turkey and the results obtained with this algorithm.

Keywords: churn prediction, data mining, decision-theoretic rough set, feature selection

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Authors: Seyoung Kim, Jeongmin Kim, Kwang Ryel Ryu

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A database that records average traffic speeds measured at five-minute intervals for all the links in the traffic network of a metropolitan city. While learning from this data the models that can predict future traffic speed would be beneficial for the applications such as the car navigation system, building predictive models for every link becomes a nontrivial job if the number of links in a given network is huge. An advantage of adopting k-nearest neighbor (k-NN) as predictive models is that it does not require any explicit model building. Instead, k-NN takes a long time to make a prediction because it needs to search for the k-nearest neighbors in the database at prediction time. In this paper, we investigate how much we can speed up k-NN in making traffic speed predictions by reducing the amount of data to be searched for without a significant sacrifice of prediction accuracy. The rationale behind this is that we had a better look at only the recent data because the traffic patterns not only repeat daily or weekly but also change over time. In our experiments, we build several different k-NN models employing different sets of features which are the current and past traffic speeds of the target link and the neighbor links in its up/down-stream. The performances of these models are compared by measuring the average prediction accuracy and the average time taken to make a prediction using various amounts of data.

Keywords: big data, k-NN, machine learning, traffic speed prediction

Procedia PDF Downloads 363

Authors: Zelda Jansen Van Rensburg

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Purpose: This study investigates the perceived disparities between Quantity Surveying and Commercial Management in the construction industry, questioning if these differences are substantive or merely semantic. It aims to challenge the conventional notion of Commercial Managers’ superiority by critically evaluating QS and CM roles, exploring CM integration possibilities, examining qualifications for aspiring Commercial Managers, assessing regulatory frameworks, and considering terminology redefinition for global QS professional enhancement. Design: Utilizing mixed methods like literature reviews, surveys, interviews, and document analyses, this research examines the QS-CM relationship. Insights from industry professionals, academics, and regulatory bodies inform the investigation into changing QS roles. Findings: Empirical data highlight evolving roles, showcasing areas of convergence and divergence between QSs and CM. Potential CM integration into QS practice and qualifications for aspiring Commercial Managers are identified. Limitations/Implications: Limitations include potential bias in self-reported data and findings. Nevertheless, the research informs future practices and educational approaches in QS and CM, reflecting the changing roles and responsibilities of Quantity Surveyors. Practical Implications: Findings inform industry practitioners, educators, and regulators, stressing the need to adapt to changing QS roles and integrate CM principles where applicable. Value to the Conference Theme: Aligned with ‘Evolving roles and responsibilities of Quantity Surveyors,’ this research offers insights crucial for understanding the changing dynamics within the QS profession and informs strategies to navigate these shifts effectively.

Keywords: quantity surveying, commercial management, cost engineering, quantity survey

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Authors: Dong-Ho Kim, Zheng-Xun Jin, Yong-Woon Cha, Su-Sang Lim, Sang-Won Han, Chang-Taek Hyun

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As construction is a labor-intensive industry, it is important to identify and manage labor quantities for accurate progress management of the construction project. However, the progress management that focuses on construction cost calculated based on materials rather than labor quantities has led to a difference in the implementation of cost and progress of the actual construction. In addition, since it is not easy to predict accurate labor quantities in the estimation of labor quantity-based progress rate, there have been limited researches into the progress rate estimation based on labor quantity. Accordingly, this study proposed a process for labor quantity-based progress rate estimation using a standard of estimate to predict accurate progress rate of the construction project in Republic Korea. It is expected that the utilization of the proposed process will help to identify the progress rate closer to that of the actual site management and adjust the workforce in each construction type, thereby contributing to improving construction efficiency.

Keywords: labor based, labor cost, progress management, progress rate, progress payment

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Authors: Jiun Jeon, Byung-Ju Yi

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This paper investigates modeling and shape prediction of elastic kinematic chains such as colonoscopy. 2D and 3D models of elastic kinematic chains are suggested and their behaviors are demonstrated through simulation. To corroborate the effectiveness of those models, experimental work is performed using a magnetic sensor system.

Keywords: elastic kinematic chain, shape prediction, colonoscopy, modeling

Procedia PDF Downloads 605

Authors: D. Alromi, A. Alansari, S. Alghamdi, E. Jambi

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As the need for water is increasing globally, and the available water resources are barely meeting the current quality of life and economy. Air conditioning (AC) condensate water could be explored as an alternative water source, which could be considered within the global calculations of the water supply. The objective of this study is to better understand the potential for recovery of condensate water from air conditioning systems. The results generated so far showed that the AC produces a high quantity of water, and data analysis revealed that the amount of water is positively and significantly correlated with the humidity (P <= 0.05). In the meantime, the amount of heavy metals has been measuring using ICP-OES. The results, in terms of quantity, clearly show that the AC can be used as an alternative source of water, especially in the regions characterized by high humidity. The results also showed that the amount of produced water depends on the type of AC.

Keywords: air conditioning systems, water quantity, water resources, wastewater

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Authors: Zhanbo Cheng, Xueyu Geng

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Vegetation slope protection technique is economic, aesthetic and practical. Herbs are widely used in practice because of rapid growth, strong erosion resistance, obvious slope protection and simple method, in which the root system of grass plays a very important role. In this paper, through changing the variables value of grassroots quantity, grassroots diameter, grassroots length and grassroots reinforce layers, the direct shear tests were carried out to discuss the change of shear strength indexes of grassroots reinforced soil under different reinforce situations, and analyse the effects of grassroots characteristics on reinforced soil performance. The laboratory test results show that: (1) in the certain number of grassroots diameter, grassroots length and grassroots reinforce layers, the value of shear strength, and cohesion first increase and then reduce with the increasing of grassroots quantity; (2) in the certain number of grassroots quantity, grassroots length and grassroots reinforce layers, the value of shear strength and cohesion rise with the increasing of grassroots diameter; (3) in the certain number of grassroots diameter, and grassroots reinforce layers, the value of shear strength and cohesion raise with the increasing of grassroots length in a certain range of grassroots quantity, while the value of shear strength and cohesion first rise and then decline with the increasing of grassroots length when the grassroots quantity reaches a certain value; (4) in the certain number of grassroots quantity, grassroots diameter, and grassroots length, the value of shear strength and cohesion first climb and then decline with the increasing of grassroots reinforced layers; (5) the change of internal friction angle is small in different parameters of grassroots. The research results are of importance for understanding the mechanism of vegetation protection for slopes and determining the parameters of grass planting.

Keywords: direct shear test, reinforced soil, grassroots characteristics, shear strength indexes

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Authors: Li Yu, Chenlu Jiao, Hongrun Xin, Yan Wang, Kaiyang Wang

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In order to study the impact of various factors on the housing price, we propose to build different prediction models based on deep learning to determine the existing data of the real estate in order to more accurately predict the housing price or its changing trend in the future. Considering that the factors which affect the housing price vary widely, the proposed prediction models include two categories. The first one is based on multiple characteristic factors of the real estate. We built Convolution Neural Network (CNN) prediction model and Long Short-Term Memory (LSTM) neural network prediction model based on deep learning, and logical regression model was implemented to make a comparison between these three models. Another prediction model is time series model. Based on deep learning, we proposed an LSTM-1 model purely regard to time series, then implementing and comparing the LSTM model and the Auto-Regressive and Moving Average (ARMA) model. In this paper, comprehensive study of the second-hand housing price in Beijing has been conducted from three aspects: crawling and analyzing, housing price predicting, and the result comparing. Ultimately the best model program was produced, which is of great significance to evaluation and prediction of the housing price in the real estate industry.

Keywords: deep learning, convolutional neural network, LSTM, housing prediction

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Authors: Dimitrios Triantakonstantis, Demetris Stathakis

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Urban areas have been expanded throughout the globe. Monitoring and modeling urban growth have become a necessity for a sustainable urban planning and decision making. Urban prediction models are important tools for analyzing the causes and consequences of urban land use dynamics. The objective of this research paper is to analyze and model the urban change, which has been occurred from 1990 to 2000 using CORINE land cover maps. The model was developed using drivers of urban changes (such as road distance, slope, etc.) under an Artificial Neural Network modeling approach. Validation was achieved using a prediction map for 2006 which was compared with a real map of Urban Atlas of 2006. The accuracy produced a Kappa index of agreement of 0,639 and a value of Cramer's V of 0,648. These encouraging results indicate the importance of the developed urban growth prediction model which using a set of available common biophysical drivers could serve as a management tool for the assessment of urban change.

Keywords: artificial neural networks, CORINE, urban atlas, urban growth prediction

Procedia PDF Downloads 528

Authors: P. S. Jagadeesh Kumar, S. Meenakshi Sundaram, Wenli Hu, Yang Yung

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In 3D video games, the dominance of production is unceasingly growing with a protruding level of affordability in terms of budget. Afterward, the automation of speech-lip synchronization technique is customarily onerous and has advanced a critical research subject in virtual reality based 3D video games. This paper presents one of these automatic tools, precisely riveted on the synchronization of the speech and the lip movement of the game characters. A robust and precise speech recognition segment that systematized with Algebraic Code Excited Linear Prediction method is developed which unconventionally delivers lip sync results. The Algebraic Code Excited Linear Prediction algorithm is constructed on that used in code-excited linear prediction, but Algebraic Code Excited Linear Prediction codebooks have an explicit algebraic structure levied upon them. This affords a quicker substitute to the software enactments of lip sync algorithms and thus advances the superiority of service factors abridged production cost.

Keywords: algebraic code excited linear prediction, speech-lip synchronization, video games, virtual reality

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Authors: Pradeep Singh, Shrish Verma

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Software fault prediction models are created by using the source code, processed metrics from the same or previous version of code and related fault data. Some company do not store and keep track of all artifacts which are required for software fault prediction. To construct fault prediction model for such company, the training data from the other projects can be one potential solution. The earlier we predict the fault the less cost it requires to correct. The training data consists of metrics data and related fault data at function/module level. This paper investigates fault predictions at early stage using the cross-project data focusing on the design metrics. In this study, empirical analysis is carried out to validate design metrics for cross project fault prediction. The machine learning techniques used for evaluation is Naïve Bayes. The design phase metrics of other projects can be used as initial guideline for the projects where no previous fault data is available. We analyze seven data sets from NASA Metrics Data Program which offer design as well as code metrics. Overall, the results of cross project is comparable to the within company data learning.

Keywords: software metrics, fault prediction, cross project, within project.

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Authors: Mojahid F. Saeed Osman

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Repairable items inventory system is a management tool used to incorporate all information concerning inventory levels and movements for repaired and new items. This paper presents development of an effective simulation model for managing the inventory of repairable items for a production system where production lines send their faulty items to a repair shop considering the stochastic failure behavior and repair times. The developed model imitates the process of handling the on-hand inventory of repaired items and the replenishment of the inventory of new items using Economic Order Quantity and Reorder Point ordering policy in a flexible and risk-free environment. We demonstrate the appropriateness and effectiveness of the proposed simulation model using an illustrative case problem. The developed simulation model can be used as a reliable tool for estimating a healthy on-hand inventory of new and repaired items, backordered items, and downtime due to unavailability of repaired items, and validating and examining Economic Order Quantity and Reorder Point ordering policy, which would further be compared with other ordering strategies as future work.

Keywords: inventory system, repairable items, simulation, maintenance, economic order quantity, reorder point

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Authors: Haozhe Xiang

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With the rise of the Internet of Things era, intelligent products are gradually integrating into people's lives. Pedestrian trajectory prediction has become a key issue, which is crucial for the motion path planning of intelligent agents such as autonomous vehicles, robots, and drones. In the current technological context, deep learning technology is becoming increasingly sophisticated and gradually replacing traditional models. The pedestrian trajectory prediction algorithm combining neural networks and attention mechanisms has significantly improved prediction accuracy. Based on in-depth research on deep learning and pedestrian trajectory prediction algorithms, this article focuses on physical environment modeling and learning of historical trajectory time dependence. At the same time, social interaction between pedestrians and scene interaction between pedestrians and the environment were handled. An improved pedestrian trajectory prediction algorithm is proposed by analyzing the existing model architecture. With the help of these improvements, acceptable predicted trajectories were successfully obtained. Experiments on public datasets have demonstrated the algorithm's effectiveness and achieved acceptable results.

Keywords: deep learning, graph convolutional network, attention mechanism, LSTM

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Authors: Tugce Arslan

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For a communicative act to be carried out successfully, the speaker and the listener must consider certain principles in line with the intention–centered “Cooperative Principle” expressed by H. P. Grice. Violation of a communication principle causes the listener to make new inferences called “implicatures”. In this study, focusing on the linguistic use of H. P. Grice’s principles, we aim to find out which principles of conversation are generally followed in case files from different fields and which principles are frequently violated. Three case files were examined, and the violating and the abiding cases of the maxims were classified in terms of four categories (Quality, Quantity, Relevance and Manner). The results of this investigation is reported below (V: Violating, A: Abiding): Quality Quantity Relevance Manner V A V A V A V A Case 1 10 8 5 9 3 15 16 6 Case 2 4 5 11 6 2 11 7 14 Case 3 21 13 7 12 9 14 15 9 Total 35 26 23 27 14 40 38 29 The excerpts were selected from files covering three different areas: the Assize Court, the Family Court and the Commercial Court of First Instance. In this way, the relations between the types of violations and the types of courts are examined. Our main finding is that in the 1st and the 3rd file, as the cases of violation in “Quality” and “Manner” increase, the cases of violation in “Quantity” and “Relevance” decrease. In the second file, on the other hand, as the cases of violation in “Quantity” increase, the cases of violation in “Quality”, “Relevance” and “Manner” decrease. In the talk, we shall compare these results with the results obtained in the study of Tajabadi, Dowlatabadi, and Mehric (2014), which examined various case files in Iran. Our main finding is that in the study conducted in Iran, violations were found only on the principles of “Quantity” and “Relevance”, while violations were found on the principles of “Quality”, “Quantity” and “Manner” in this study. In this case, it shows us that there is a connection between at least two maxims. In both cases, it has been noticed that the “Quantity” maxim is a common denominator. Studies in this field can be enlightening for many areas such as discourse analysis, legal studies, etc. Accordingly, comments will be made about the nature of the violations mentioned in H. P. Grice’s “Cooperation Principle”. We shall also discuss various conversational practices that cannot be analysed with these maxims.

Keywords: comparative analysis, cooperation principle, forensic linguistics, pragmatic.

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Authors: Dalin Si, Azizan Aziz, Bertrand Lasternas

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To encourage building owners to purchase electricity at the wholesale market and reduce building peak demand, this study aims to develop models that predict day-ahead hourly electricity consumption and demand using artificial neural network (ANN) and support vector machine (SVM). All prediction models are built in Python, with tool Scikit-learn and Pybrain. The input data for both consumption and demand prediction are time stamp, outdoor dry bulb temperature, relative humidity, air handling unit (AHU), supply air temperature and solar radiation. Solar radiation, which is unavailable a day-ahead, is predicted at first, and then this estimation is used as an input to predict consumption and demand. Models to predict consumption and demand are trained in both SVM and ANN, and depend on cooling or heating, weekdays or weekends. The results show that ANN is the better option for both consumption and demand prediction. It can achieve 15.50% to 20.03% coefficient of variance of root mean square error (CVRMSE) for consumption prediction and 22.89% to 32.42% CVRMSE for demand prediction, respectively. To conclude, the presented models have potential to help building owners to purchase electricity at the wholesale market, but they are not robust when used in demand response control.

Keywords: building energy prediction, data mining, demand response, electricity market

Procedia PDF Downloads 316

Authors: Yong-Il Lee, Do-Yeon Hwang, Won-Seog Jeong, Duckshin Park

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Recently, because of the high-speed trains forced ventilation, it is important to control the ventilation. The ventilation is for controlling various contaminants, temperature, and humidity. The high-speed train route is straight to a destination having a high speed. And there are many mountainous areas in Korea. So, tunnel rate is higher then other country. KTX HVAC block off the outdoor air, when entering tunnel. So the high tunnel rate is an effect of ventilation in the KTX cabin. It is important to reduction rate in CO2 concentration prediction. To meet the air quality of the public transport vehicles recommend standards, the KTX cabin of CO2 concentration should be managed. In this study, the concentration change was predicted by CO2 prediction simulation in route to be opened.

Keywords: CO2 prediction, KTX, ventilation, infrastructure and transportation engineering

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Authors: Katawut Kaewbanjong

Abstract:

We analyzed a volume of data and found significant user satisfaction in software project factors. A statistical significance analysis (logistic regression) and collinearity analysis determined the significance factors from a group of 71 pre-defined factors from 191 software projects in ISBSG Release 12. The eight prediction models used for testing the prediction potential of these factors were Neural network, k-NN, Naïve Bayes, Random forest, Decision tree, Gradient boosted tree, linear regression and logistic regression prediction model. Fifteen pre-defined factors were truly significant in predicting user satisfaction, and they provided 82.71% prediction accuracy when used with a neural network prediction model. These factors were client-server, personnel changes, total defects delivered, project inactive time, industry sector, application type, development type, how methodology was acquired, development techniques, decision making process, intended market, size estimate approach, size estimate method, cost recording method, and effort estimate method. These findings may benefit software development managers considerably.

Keywords: prediction model, statistical analysis, software project, user satisfaction factor

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