Search results for: expert assessments are used. In the proposed methodology

Authors: Zhong Ma, Zhuping Wang, Congxin Liu, Xiangzeng Liu

Abstract:

Satellite imagery classification is a challenging problem with many practical applications. In this paper, we designed a deep convolution neural network (DCNN) to classify the satellite imagery. The contributions of this paper are twofold — First, to cope with the large-scale variance in the satellite image, we introduced the inception module, which has multiple filters with different size at the same level, as the building block to build our DCNN model. Second, we proposed a genetic algorithm based method to efficiently search the best hyper-parameters of the DCNN in a large search space. The proposed method is evaluated on the benchmark database. The results of the proposed hyper-parameters search method show it will guide the search towards better regions of the parameter space. Based on the found hyper-parameters, we built our DCNN models, and evaluated its performance on satellite imagery classification, the results show the classification accuracy of proposed models outperform the state of the art method.

Keywords: satellite imagery classification, deep convolution network, genetic algorithm, hyper-parameter optimization

Procedia PDF Downloads 302

Authors: Aishat Abdul-Qadir Zubair

Abstract:

Background: The absence of Shari’ah screening methodology for companies in Nigeria has further engineered the uncertainty surrounding the acceptability of investing in certain companies by people professing the religion of Islam due to the nature of the activities carried out by these companies. There are some existing shariah screening indices in other jurisdictions whose criteria can be used to check if a company or business is shariah-compliant or not. Examples such as FTSE, DJIM, Standard and Poor to mention just a few. What these indices have tried to do is to ensure that there are benchmarks to check with before investing in companies that carry out mixed activities in their business, wherein some are halal and others may be haram. Purpose: There have been numerous studies on the need to adopt certain screening methodologies as well as call for new methods in screening companies for shariah compliance in order to suit the investments needs of Muslims in other jurisdictions. It is, however, unclear how suitable these methodologies will be to Nigeria. This paper, therefore, seeks to address this gap to consider an appropriate screening methodology to be employed in Nigeria, drawing from the experience of other jurisdictions. Methods: This study employs a triangulation of both quantitative and qualitative methods to analyze the need for Shari’ah screening of companies in Nigeria. The qualitative method is used by way of ijtihad, and this study tries to apply some Islamic Principles of Maqasid al-shari’ah as well as Qawaid al-Fiqiyyah to analyze activities of companies in order to ensure that they are indeed Shari’ah compliant. In addition, using the quantitative data gathered from the interview survey, the perspective of the investors with regards to the need for Shari’ah screening of companies in Nigeria is further analyzed. Results: The result of the study shows that there is a lack of awareness from the teeming Muslim population in Nigeria on the need for Shari’ah screening of companies in Nigeria. The result further shows that there is the need to take into cognizance the peculiar nature of company activities in Nigeria before any particular Shari’ah screening methodology is adopted and setting the necessary benchmarks. Conclusion and Implications: The study concludes that there is the need to ensure that the conscious Muslims in Nigeria screen companies for Shari’ah compliance so that they can easily identify the companies to invest in. The paper, therefore, recommends that the Nigerian government need to come up with a screening methodology that will suit the peculiar nature of companies in Nigeria. The study thus has a direct implication on the Investment regulatory bodies in Nigeria such as the Securities and Exchange Commission (SEC), Central Bank of Nigeria (CBN) as well as the investor Muslims.

Keywords: Shari'ah screening, Muslims, investors, companies

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Authors: Keunhong Chae, Seokho Yoon

Abstract:

In this paper, frequency offset (FO) estimation schemes robust to the non-Gaussian noise environments are proposed for orthogonal frequency division multiplexing (OFDM) systems. First, a maximum-likelihood (ML) estimation scheme in non-Gaussian noise environments is proposed, and then, the complexity of the ML estimation scheme is reduced by employing a reduced set of candidate values. In numerical results, it is demonstrated that the proposed schemes provide a significant performance improvement over the conventional estimation scheme in non-Gaussian noise environments while maintaining the performance similar to the estimation performance in Gaussian noise environments.

Keywords: frequency offset estimation, maximum-likelihood, non-Gaussian noise environment, OFDM, training symbol

Procedia PDF Downloads 355

Authors: S. J. Yune, S. Y. Lee, S. J. Im, B. S. Kam, S. Y. Baek

Abstract:

This study aimed to determine the reliability of clinical performance assessments, having been emphasized by ability-based education, and professors overall assessment methods. We addressed the following problems: First, we try to find out whether there is a difference in what we consider to be the main variables affecting the clinical performance test according to the evaluator’s working period and the number of evaluation experience. Second, we examined the relationship among the global rating score (G), analytic global rating score (Gc), and the sum of the analytical checklists (C). What are the main factors affecting clinical performance assessments in relation to the numbers of times the evaluator had administered evaluations and the length of their working period service? What is the relationship between overall assessment score and analytic checklist score? How does analytic global rating with 6 components in OSCE and 4 components in sub-domains (Gc) CPX: aseptic practice, precision, systemic approach, proficiency, successfulness, and attitude overall assessment score and task-specific analytic checklist score sum (C) affect the professor’s overall global rating assessment score (G)? We studied 75 professors who attended a 2016 Bugyeoung Consortium clinical skills performances test evaluating third and fourth year medical students at the Pusan National University Medical school in South Korea (39 prof. in OSCE, 36 prof. in CPX; all consented to participate in our study). Each evaluator used 3 forms; a task-specific analytic checklist, subsequent analytic global rating scale with sub-6 domains, and overall global scale. After the evaluation, the professors responded to the questionnaire on the important factors of clinical performance assessment. The data were analyzed by frequency analysis, correlation analysis, and hierarchical regression analysis using SPSS 21.0. Their understanding of overall assessment was analyzed by dividing the subjects into groups based on experiences. As a result, they considered ‘precision’ most important in overall OSCE assessment, and ‘precise accuracy physical examination’, ‘systemic approaches to taking patient history’, and ‘diagnostic skill capability’ in overall CPX assessment. For OSCE, there was no clear difference of opinion about the main factors, but there was for CPX. Analytic global rating scale score, overall rating scale score, and analytic checklist score had meaningful mutual correlations. According to the regression analysis results, task-specific checklist score sum had the greatest effect on overall global rating. professors regarded task-specific analytic checklist total score sum as best reflecting overall OSCE test score, followed by aseptic practice, precision, systemic approach, proficiency, successfulness, and attitude on a subsequent analytic global rating scale. For CPX, subsequent analytic global rating scale score, overall global rating scale score, and task-specific checklist score had meaningful mutual correlations. These findings support explanations for validity of professors’ global rating in clinical performance assessment.

Keywords: global rating, clinical performance assessment, medical education, analytic checklist

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Authors: Jihoon Lim, Patrick Kirchen, Ryozo Nagamune

Abstract:

This paper proposes a linear parameter-varying (LPV) controller capable of reducing nitrogen oxide (NOx) emissions with low ammonia (NH3) slip downstream of selective catalytic reduction (SCR) systems. SCR systems are widely adopted in diesel engines due to high NOx conversion efficiency. However, the nonlinearity of the SCR system and sensor uncertainty result in a challenging control problem. In order to overcome the control challenges, an LPV controller is proposed based on gain-scheduling parameters, that is, exhaust gas temperature and exhaust gas flow rate. Based on experimentally obtained data under the non-road transient driving cycle (NRTC), the simulations firstly show that the proposed controller yields high NOx conversion efficiency with a desired low NH3 slip. The performance of the proposed LPV controller is then compared with other controllers, including a gain-scheduling PID controller and a sliding mode controller. Additionally, the robustness is also demonstrated using the uncertainties ranging from 10 to 30%. The results show that the proposed controller is robustly stable under uncertainties.

Keywords: diesel engine, gain-scheduling control, linear parameter-varying, selective catalytic reduction

Procedia PDF Downloads 147

Authors: Aicha Zohbie

Abstract:

The purpose of this paper is to critically compare two teaching methods: the communicative method and the grammar-translation method. The paper presents the importance of language awareness as an approach to teaching and learning language and some challenges that language teachers face. In addition, the paper strives to determine whether the adoption of communicative teaching methods or the grammar teaching method would be more effective to teach a language. A variety of features are considered for comparing the two methods: the purpose of each method, techniques used, teachers’ and students’ roles, the use of L1, the skills that are emphasized, the correction of students’ errors, and the students’ assessments. Finally, the paper includes suggestions and recommendations for implementing an approach that best meets the students’ needs in a classroom.

Keywords: language teaching methods, language awareness, communicative method grammar translation method, advantages and disadvantages

Procedia PDF Downloads 154

Authors: Sarah Al-Lumai, Fatima Al-Attar, Nour Jamal, Badria Al-Dabbous, Manal Abdulla

Abstract:

Today, many commonly used Industrial Engineering tools and techniques are being used in hospitals around the world for the goal of producing a more efficient and effective healthcare system. A common quality improvement methodology known as Lean Six-Sigma has been successful in manufacturing industries and recently in healthcare. The objective of our project is to use the Lean Six-Sigma methodology to reduce waiting time in the Emergency Department (ED), in a local private hospital. Furthermore, a comprehensive literature review was conducted to evaluate the success of Lean Six-Sigma in the ED. According to the study conducted by Ibn Sina Hospital, in Morocco, the most common problem that patients complain about is waiting time. To ensure patient satisfaction many hospitals such as North Shore University Hospital were able to reduce waiting time up to 37% by using Lean Six-Sigma. Other hospitals, such as John Hopkins’s medical center used Lean Six-Sigma successfully to enhance the overall patient flow that ultimately decreased waiting time. Furthermore, it was found that capacity constraints, such as staff shortages and lack of beds were one of the main reasons behind long waiting time. With the use of Lean Six-Sigma and bed management, hospitals like Memorial Hermann Southwest Hospital were able to reduce patient delays. Moreover, in order to successfully implement Lean Six-Sigma in our project, two common methodologies were considered, DMAIC and DMADV. After the assessment of both methodologies, it was found that DMAIC was a more suitable approach to our project because it is more concerned with improving an already existing process. With many of its successes, Lean Six-Sigma has its limitation especially in healthcare; but limitations can be minimized if properly approached.

Keywords: lean six sigma, DMAIC, hospital, methodology

Procedia PDF Downloads 497

Authors: Soheila Sadeghi

Abstract:

Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: cost prediction, machine learning, project management, random forest, neural networks

Procedia PDF Downloads 61

Authors: Emmanuelle Merat, Arnaud Aubert, Sophie Cambos, Francis Vial, Patrick Beau

Abstract:

Basically, consumers are sensitive to many stimuli when applying a cream: brand, packaging and indeed formulation compositions. Many studies demonstrated the influence of some stimuli such as brand, packaging, formula color and odor (e.g. in make-up applications). Those parameters influence perceived quality of the product. The objective of this work is to further investigate the relationship between nude skincare basic compositions with different textures and consumer experience. A tentative final step will be to connect the consumer feelings with key ingredients in the compositions. A new approach was developed to better understand touch-related subjective experience in consumers based on a combination of methods: sensory analysis with ten experts, preference mapping on one hundred female consumers and emotional assessments on thirty consumers (verbal and non-verbal through prosody and gesture monitoring). Finally, a methodology based on ‘sensorial trip’ (after olfactory, haptic and musical stimuli) has been experimented on the most interesting textures with 10 consumers. The results showed more or less impact depending on compositions and also on key ingredients. Three types of formulation particularly attracted the consumer: an aqueous gel, an oil-in-water emulsion, and a patented gel-in-oil formulation type. Regarding these three formulas, the preferences were both revealed through sensory and emotion tests. One was recognized as the most innovative in consumer sensory test whereas the two other formulas were discriminated in emotions evaluation. The positive emotions were highlighted especially in prosody criteria. The non-verbal analysis, which corresponds to the physical parameters of the voice, showed high pitch and amplitude values; linked to positive emotions. Verbatim, verbal content of responses (i.e., ideas, concepts, mental images), confirmed the first conclusion. On the formulas selected for their positive emotions generation, the ‘sensorial trip’ provided complementary information to characterize each emotional profile. In the second step, dedicated to better understand ingredients power, two types of ingredients demonstrated an obvious input on consumer preference: rheology modifiers and emollients. As a conclusion, nude cosmetic compositions with well-chosen textures and ingredients can positively stimulate consumer emotions contributing to capture their preference. For a complete achievement of the study, a global approach (Asia, America territories...) should be developed.

Keywords: sensory, emotion, cosmetic formulations, ingredients' influence

Procedia PDF Downloads 181

Authors: Jaehyung An, Sungjoo Lee

Abstract:

Patent documents constitute an up-to-date and reliable source of knowledge for reflecting technological advance, so patent analysis has been widely used for identification of technological trends and formulation of technology strategies. But, identifying technological information from patent data entails some limitations such as, high cost, complexity, and inconsistency because it rely on the expert’ knowledge. To overcome these limitations, researchers have applied to a quantitative analysis based on the keyword technique. By using this method, you can include a technological implication, particularly patent documents, or extract a keyword that indicates the important contents. However, it only uses the simple-counting method by keyword frequency, so it cannot take into account the sematic relationship with the keywords and sematic information such as, how the technologies are used in their technology area and how the technologies affect the other technologies. To automatically analyze unstructured technological information in patents to extract the semantic information, it should be transformed into an abstracted form that includes the technological key concepts. Specific sentence structure ‘SAO’ (subject, action, object) is newly emerged by representing ‘key concepts’ and can be extracted by NLP (Natural language processor). An SAO structure can be organized in a problem-solution format if the action-object (AO) states that the problem and subject (S) form the solution. In this paper, we propose the new methodology that can extract the SAO structure through technical elements extracting rules. Although sentence structures in the patents text have a unique format, prior studies have depended on general NLP (Natural language processor) applied to the common documents such as newspaper, research paper, and twitter mentions, so it cannot take into account the specific sentence structure types of the patent documents. To overcome this limitation, we identified a unique form of the patent sentences and defined the SAO structures in the patents text data. There are four types of technical elements that consist of technology adoption purpose, application area, tool for technology, and technical components. These four types of sentence structures from patents have their own specific word structure by location or sequence of the part of speech at each sentence. Finally, we developed algorithms for extracting SAOs and this result offer insight for the technology innovation process by providing different perspectives of technology.

Keywords: NLP, patent analysis, SAO, semantic-analysis

Procedia PDF Downloads 262

Authors: Koirala Abarodh, Maghaiya Ujjwal, Guragain Phani Raj

Abstract:

This analysis focuses on defining the spectrum evaluation model for telecom operators in terms of total cost of ownership (TCO). A quantitative approach for specific case analysis research methodology was used for identifying the results. Specific input parameters like target User experience, year on year traffic growth, capacity site limit per year, target additional spectrum type, bandwidth, spectrum efficiency, UE penetration have been used for the spectrum evaluation process and desired outputs in terms of the number of sites, capex in USD and required spectrum bandwidth have been calculated. Furthermore, this study gives a comparison of capex investment for target growth with and without addition spectrum. As a result, the combination of additional spectrum bands of 700 and 2600 MHz has a better evaluation in terms of TCO and performance. It is our recommendation to use these bands for expansion rather than expansion in the current 1800 and 2100 bands.

Keywords: spectrum, capex planning, case study methodology, TCO

Procedia PDF Downloads 66

Authors: Lucie Jirotková, Daniel Macek, Andrea Palazzo, Veronika Malinová

Abstract:

Currently, the implementation of 3D models in the construction industry is gaining popularity. Countries around the world are developing their own modelling standards and implement the use of 3D models into their individual permitting processes. Another theme that needs to be addressed are public building spaces and their subsequent maintenance, where the usage of BIM methodology is directly offered. The significant benefit of the implementation of Building Information Management is the information transfer. The 3D model contains not only the spatial representation of the item shapes but also various parameters that are assigned to the individual elements, which are easily traceable, mainly because they are all stored in one place in the BIM model. However, it is important to keep the data in the models up to date to achieve useability of the model throughout the life cycle of the building. It is now becoming standard practice to use BIM models in the construction of buildings, however, the building environment is very often neglected. Especially in large-scale development projects, the public space of buildings is often forwarded to municipalities, which obtains the ownership and are in charge of its maintenance. A 3D model of the building surroundings would include both the above-ground visible elements of the development as well as the underground parts, such as the technological facilities of water features, electricity lines for public lighting, etc. The paper shows the possibilities of a model in the field of information for the handover of premises, the following maintenance and decision making. The attributes and spatial representation of the individual elements make the model a reliable foundation for the creation of "Smart Cities". The paper analyses the current use of the BIM methodology and presents the state-of-the-art possibilities of development.

Keywords: BIM model, urban space, BIM methodology, facility management

Procedia PDF Downloads 125

Authors: Ilige S. Hage, Charbel Y. Seif

Abstract:

Forces measured during cutting operations are generated by the cutting process and include parasitic forces, known as edge forces. A fraction of these measured forces arises from the tertiary cutting zone, such as flank or edge forces. Most machining models are designed for sharp tools; where edge forces represent the portion of the measured forces associated with deviations of the tool from an ideal sharp geometry. Flank forces are challenging to isolate. The most common method involves plotting the force at a constant cutting speed against uncut chip thickness and then extrapolating to zero feed. The resulting positive intercept on the vertical axis is identified as the edge or plowing force. The aim of this research is to identify the effect of tool rake angle and cutting speeds on flank forces and to develop a force model as a function of tool rake angle and cutting speed for predicting plowing forces. Edge forces were identified based on a limited number of drilling experiments using a 10 mm twist drill, where lip edge cutting forces were collected from 2.5 mm pre-cored samples. Cutting lip forces were measured with feed rates varying from 0.04 to 0.64 mm/rev and spindle speeds ranging from 796 to 9868 rpm, at a sampling rate of 200 Hz. By using real-time force measurements as the drill enters the workpiece, this study provides an economical method for analyzing the effect of tool geometry and cutting conditions on generated cutting forces, reducing the number of required experimental setups. As a result, an empirical model predicting parasitic edge forces was developed function of the cutting velocity, tool rake angle, and clearance angle along the lip of the tool, demonstrating strong agreement with edge forces reported in the literature for Aluminum 6061-T6. The model achieved an R2 value of 0.92 and a mean square error of 4%, validating the accuracy of the proposed methodology. The presented methodology leverages variations in machining parameters. This approach contrasts with traditional machining experiments, where the turning process typically serves as the basis for force measurements and each experimental setup is characterized by a single cutting velocity, tool rake angle, and clearance angle.

Keywords: drilling, plowing, edge forces, cutting force, torque

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Authors: Aigin Bashti

Abstract:

Preparation of multicomponent reactions (MCRs) via a simple one-pot strategy is considered a novel procedure which has attracted a lot of interest from organic and medicinal chemists. Due to the great importance of phthalazide triones, it was decided to introduce a novel and cost-effective green procedure for the preparation of these derivatives. In this methodology, an efficient 4,4ʹ-Bipyridinium Dichloride Ordered Mesoporous Silica functionalized catalyst (BP-SBA-15) was utilized. The catalyst was characterized by X-ray diffraction analysis (XRD), field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), thermo-gravimetric analysis (TGA), and Fourier-transform infrared spectroscopy (FT-IR) analysis. In conclusion, it should be mentioned that this methodology has some advantages, including short reaction time, high yield of the products, recyclable catalyst, green procedure, and facile work-up procedure. The catalyst was successfully utilized for the one-pot preparation of various phthalazinetrione derivatives.

Keywords: dimedone, green procedure, multicomponent reactions, phthalhydrazide

Procedia PDF Downloads 100

Authors: Sepali Guruge

Abstract:

Background: There is an increase in interest among health care professionals and social workers in understanding how best to identify, mitigate, and prevent elder abuse. Purpose & method: Based on a recently completed scoping review of related literature, this paper will focus on the current state of knowledge on elder abuse interventions. Results: The results will be presented in light of the fact that limited literature exists on primary prevention of elder abuse. The existing literature on interventions to reduce or stop abuse will be critically examined in terms of their effectiveness. Particular attention will be paid to interventions such as relocation of older adults experiencing abuse, in-home assessments, empowerment and psycho-educational support for older adults. Conclusions: Overall, multi-stakeholder collaborative, community-based interventions should be designed to identify, mitigate, and prevent elder abuse.

Keywords: elder abuse, interventions, scoping review, prevention

Procedia PDF Downloads 355

Authors: Mohammad Arif Sobhan Bhuiyan, Zainal Abidin Nordin, Mamun Bin Ibne Reaz

Abstract:

A compact and power efficient high performance Voltage Controlled Oscillator (VCO) is a must in analog and digital circuits especially in the communication system, but the best trade-off among the performance parameters is a challenge for researchers. In this paper, a design of a compact 3-stage differential voltage controlled ring oscillator (VCRO) with low phase noise, low power and higher tuning bandwidth is proposed in 0.18 µm CMOS technology. The VCRO is designed with symmetric load and positive feedback techniques to achieve higher gain and minimum delay. The proposed VCRO can operate at tuning range of 3.9-5.0 GHz at 1.6 V supply voltage. The circuit consumes only 1.0757 mW of power and produces -129 dbc/Hz. The total active area of the proposed VCRO is only 11.74 x 37.73 µm2. Such a VCO can be the best choice for compact and low-power RF applications.

Keywords: CMOS, VCO, VCRO, oscillator

Procedia PDF Downloads 480

Authors: Asawari Ajay Avhad

Abstract:

The Soil and Water Assessment Tool (SWAT) is a hydrological model designed to predict the complex interactions within natural and human-altered watersheds. This research applies the SWAT model to the Ulhas River basin, a small watershed undergoing urbanization and characterized by bowl-like topography. Three simulation scenarios (LC17, LC22, and LC27) are investigated, each representing different land use and land cover (LULC) configurations, to assess the impact of urbanization on runoff. The LULC for the year 2027 is generated using the MOLUSCE Plugin of QGIS, incorporating various spatial factors such as DEM, Distance from Road, Distance from River, Slope, and distance from settlements. Future climate data is simulated within the SWAT model using historical data spanning 30 years. A susceptibility map for runoff across the basin is created, classifying runoff into five susceptibility levels ranging from very low to very high. Sub-basins corresponding to major urban settlements are identified as highly susceptible to runoff. With consideration of future climate projections, a slight increase in runoff is forecasted. The reliability of the methodology was validated through the identification of sub-basins known for experiencing severe flood events, which were determined to be highly susceptible to runoff. The susceptibility map successfully pinpointed these sub-basins with a track record of extreme flood occurrences, thus reinforcing the credibility of the assessment methodology. This study suggests that the methodology employed could serve as a valuable tool in flood management planning.

Keywords: future land use impact, flood management, run off prediction, ArcSWAT

Procedia PDF Downloads 49

Authors: Soheila Sadeghi

Abstract:

Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: resource allocation, machine learning, optimization, data-driven decision-making, project management

Procedia PDF Downloads 41

Authors: Jonas Pedro Fabris, Alberth Almeida Amorim Souza, Maria Emilia Camargo, Suzana Leitão Russo

Abstract:

With the law Nº. 9279, of May 14, 1996, the Brazilian government regulates rights and obligations relating to industrial property considering the economic development of the country as granting patents, trademark registration, registration of industrial designs and other forms of protection copyright. In this study, we show the application of the methodology of Box and Jenkins in the series of deposits of industrial design at the National Institute of Industrial Property for the period from May 1996 to April 2013. First, a graphical analysis of the data was done by observing the behavior of the data and the autocorrelation function. The best model found, based on the analysis of charts and statistical tests suggested by Box and Jenkins methodology, it was possible to determine the model number for the deposit of industrial design, SARIMA (2,1,0)(2,0,0), with an equal to 9.88% MAPE.

Keywords: ARIMA models, autocorrelation, Box and Jenkins Models, industrial design, MAPE, time series

Procedia PDF Downloads 546

Authors: Shih-Chieh Liao, Cheng-Yan Shuai

Abstract:

The feature of a side-scrolling shooting game is facing the surrounding enemy and barraging in entire screen. The player will be in trouble when they are trying to do complicated operations because of the physical and system limitations of the joystick in the games. This study designed the prototype of a new type of arcade stick by focus group and assessed by the expert. By filtering the most representative, and build up the control system for the arcade stick, and testing time and bullets consumed in two experiments, try to prove it works in the game. Finally, the prototype of L-1 solves the dilemma of scroll shooting games when the player uses the arcade stick and improves the function of the arcade stick.

Keywords: arcade stick, joystick, user interface, 2D STG

Procedia PDF Downloads 80

Authors: Richard Mbatu

Abstract:

Implementation of the Reducing Emissions from Deforestation and forest Degradation (REDD+) program will not only lead to significant net gains in greenhouse gas reduction but also gains in biodiversity conservation. However, the looming paradigm shift in the program in the form of the proposed landscape governance approach could change this inclination. The concern lies with the fact that pursue of carbon credits by governments and private entities under the proposed landscape approach could encourage obstinate land use behaviors that are detrimental to the cause of biodiversity conservation and ecosystem services. Yet, the landscape approach could also stimulate governments to develop and implement land use management policies for climate change adaptation and mitigation. Using two potential areas of land use under the proposed landscape approach – carbon farming in grasslands and carbon farming in plantations – this paper provides a balanced analytical review of conservation challenges and opportunities for forest governance and beyond under the proposed landscape approach to REDD+. The paper argues that such a balanced view will enable policymakers and other stakeholders to better present their arguments in their efforts to shape the course of the REDD+ program in the post-Paris Agreement era.

Keywords: biodiversity conservation, REDD+, forest governance, grasslands, landscape approach, plantations

Procedia PDF Downloads 368

Authors: H. K. Esfahani, B. Datta

Abstract:

Groundwater is one of the most important natural resources in many parts of the world; however it is widely polluted due to human activities. Currently, effective and reliable groundwater management and remediation strategies are obtained using characterization of groundwater pollution sources, where the measured data in monitoring locations are utilized to estimate the unknown pollutant source location and magnitude. However, accurately identifying characteristics of contaminant sources is a challenging task due to uncertainties in terms of predicting source flux injection, hydro-geological and geo-chemical parameters, and the concentration field measurement. Reactive transport of chemical species in contaminated groundwater systems, especially with multiple species, is a complex and highly non-linear geochemical process. Although sufficient concentration measurement data is essential to accurately identify sources characteristics, available data are often sparse and limited in quantity. Therefore, this inverse problem-solving method for characterizing unknown groundwater pollution sources is often considered ill-posed, complex and non- unique. Different methods have been utilized to identify pollution sources; however, the linked simulation-optimization approach is one effective method to obtain acceptable results under uncertainties in complex real life scenarios. With this approach, the numerical flow and contaminant transport simulation models are externally linked to an optimization algorithm, with the objective of minimizing the difference between measured concentration and estimated pollutant concentration at observation locations. Concentration measurement data are very important to accurately estimate pollution source properties; therefore, optimal design of the monitoring network is essential to gather adequate measured data at desired times and locations. Due to budget and physical restrictions, an efficient and effective approach for groundwater pollutant source characterization is to design an optimal monitoring network, especially when only inadequate and arbitrary concentration measurement data are initially available. In this approach, preliminary concentration observation data are utilized for preliminary source location, magnitude and duration of source activity identification, and these results are utilized for monitoring network design. Further, feedback information from the monitoring network is used as inputs for sequential monitoring network design, to improve the identification of unknown source characteristics. To design an effective monitoring network of observation wells, optimization and interpolation techniques are used. A simulation model should be utilized to accurately describe the aquifer properties in terms of hydro-geochemical parameters and boundary conditions. However, the simulation of the transport processes becomes complex when the pollutants are chemically reactive. Three dimensional transient flow and reactive contaminant transport process is considered. The proposed methodology uses HYDROGEOCHEM 5.0 (HGCH) as the simulation model for flow and transport processes with chemically multiple reactive species. Adaptive Simulated Annealing (ASA) is used as optimization algorithm in linked simulation-optimization methodology to identify the unknown source characteristics. Therefore, the aim of the present study is to develop a methodology to optimally design an effective monitoring network for pollution source characterization with reactive species in polluted aquifers. The performance of the developed methodology will be evaluated for an illustrative polluted aquifer sites, for example an abandoned mine site in Queensland, Australia.

Keywords: monitoring network design, source characterization, chemical reactive transport process, contaminated mine site

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Authors: Kevin Zhao, Norman J. Horing

Abstract:

A methodology for cells sorting and circulating tumor cell detection and discrimination is presented in this paper. The technique is based on Dielectrophoresis and microfluidic device theory. Specifically, the sorting of the cells is realized by adjusting the relation among the sedimentation forces, the drag force provided by the fluid, and the Dielectrophortic force that is relevant to the bias voltage applied on the device. The relation leads to manipulation of the elevation of the cells of the same kind to a height by controlling the bias voltage. Once the cells have been lifted to a position next to the bottom of the cell collection channel, the buffer fluid flashes them into the cell collection channel. Repeated elevation of the cells leads to a complete sorting of the cells in the sample chamber. A proof-of-principle example is presented which verifies the feasibility of the methodology.

Keywords: cell sorter, CTC cell, detection and discrimination, dielectrophoresisords, simulation

Procedia PDF Downloads 433

Authors: Aron Witkowski, Andrzej Wodecki

Abstract:

Purpose: This study aims to explore the feasibility and effectiveness of utilizing Generative Artificial Intelligence (GenAI) agents as product managers within the manufacturing sector. It seeks to evaluate whether current GenAI capabilities can fulfill the complex requirements of product management and deliver comparable outcomes to human counterparts. Study Design/Methodology/Approach: This research involved the creation of a support application for product managers, utilizing high-quality sources on product management and generative AI technologies. The application was designed to assist in various aspects of product management tasks. To evaluate its effectiveness, a study was conducted involving 10 experienced product managers from the manufacturing sector. These professionals were tasked with using the application and providing feedback on the tool's responses to common questions and challenges they encounter in their daily work. The study employed a mixed-methods approach, combining quantitative assessments of the tool's performance with qualitative interviews to gather detailed insights into the user experience and perceived value of the application. Findings: The findings reveal that GenAI-based product management agents exhibit significant potential in handling routine tasks, data analysis, and predictive modeling. However, there are notable limitations in areas requiring nuanced decision-making, creativity, and complex stakeholder interactions. The case study demonstrates that while GenAI can augment human capabilities, it is not yet fully equipped to independently manage the holistic responsibilities of a product manager in the manufacturing sector. Originality/Value: This research provides an analysis of GenAI's role in product management within the manufacturing industry, contributing to the limited body of literature on the application of GenAI agents in this domain. It offers practical insights into the current capabilities and limitations of GenAI, helping organizations make informed decisions about integrating AI into their product management strategies. Implications for Academic and Practical Fields: For academia, the study suggests new avenues for research in AI-human collaboration and the development of advanced AI systems capable of higher-level managerial functions. Practically, it provides industry professionals with a nuanced understanding of how GenAI can be leveraged to enhance product management, guiding investments in AI technologies and training programs to bridge identified gaps.

Keywords: generative artificial intelligence, GenAI, NPD, new product development, product management, manufacturing

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Authors: Veniamin Boiarkin, Bruno Bogaz Zarpelão, Muttukrishnan Rajarajan

Abstract:

The manufacturing sector is a vital component of most economies, which leads to a large number of cyberattacks on organisations, whereas disruption in operation may lead to significant economic consequences. Adversaries aim to disrupt the production processes of manufacturing companies, gain financial advantages, and steal intellectual property by getting unauthorised access to sensitive data. Access to sensitive data helps organisations to enhance the production and management processes. However, the majority of the existing data-sharing mechanisms are either susceptible to different cyber attacks or heavy in terms of computation overhead. In this paper, a privacy-preserving data-sharing scheme for smart utilities is proposed. First, a customer’s privacy adjustment mechanism is proposed to make sure that end-users have control over their privacy, which is required by the latest government regulations, such as the General Data Protection Regulation. Secondly, a local differential privacy-based mechanism is proposed to ensure the privacy of the end-users by hiding real data based on the end-user preferences. The proposed scheme may be applied to different industrial control systems, whereas in this study, it is validated for energy utility use cases consisting of smart, intelligent devices. The results show that the proposed scheme may guarantee the required level of privacy with an expected relative error in utility.

Keywords: data-sharing, local differential privacy, manufacturing, privacy-preserving mechanism, smart utility

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Authors: Dimitrios J. Dimitriou, Maria F. Sartzetaki

Abstract:

Transport infrastructures are high-cost, long-term investments that serve as vital foundations for the operation of a region or nation and are essential to a country’s or business’s economic development and prosperity, by improving well-being and generating jobs and income. The development of appropriate financing options is of key importance in the decision making process in order develop viable transport infrastructures. The development of transport infrastructure has increasingly been shifting toward alternative methods of project financing such as Public Private Partnership (PPPs) and hybrid forms. In this paper, a methodological decision-making framework based on the evaluation of the financial viability of transportation infrastructure for different financial schemes is presented. The framework leads to an assessment of the financial viability which can be achieved by performing various financing scenarios analyses. To illustrate the application of the proposed methodology, a case study of rail transport infrastructure financing scenario analysis in Greece is developed.

Keywords: rail transport infrastructure, financial viability, scenario analysis, rail project feasibility

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Authors: T. Damak, O. Kriaa, A. Baccar, M. A. Ben Ayed, N. Masmoudi

Abstract:

In the last few years, Automatic Number Plate Recognition (ANPR) systems have become widely used in the safety, the security, and the commercial aspects. Forethought, several methods and techniques are computing to achieve the better levels in terms of accuracy and real time execution. This paper proposed a computer vision algorithm of Number Plate Localization (NPL) and Characters Segmentation (CS). In addition, it proposed an improved method in Optical Character Recognition (OCR) based on Deep Learning (DL) techniques. In order to identify the number of detected plate after NPL and CS steps, the Convolutional Neural Network (CNN) algorithm is proposed. A DL model is developed using four convolution layers, two layers of Maxpooling, and six layers of fully connected. The model was trained by number image database on the Jetson TX2 NVIDIA target. The accuracy result has achieved 95.84%.

Keywords: ANPR, CS, CNN, deep learning, NPL

Procedia PDF Downloads 307

Authors: Michael Anton Kraus, Miriam Schuster, Geralt Siebert, Jens Schneider

Abstract:

Polymers increasingly gained interest in construction materials over the last years in civil engineering applications. As polymeric materials typically show time- and temperature dependent material behavior, which is accounted for in the context of the theory of linear viscoelasticity. Within the context of this paper, the authors show, that some polymeric interlayers for laminated glass can not be considered as thermorheologically simple as they do not follow a simple TTSP, thus a methodology of identifying the thermorheologically complex constitutive bahavioir is needed. ‘Dynamical-Mechanical-Thermal-Analysis’ (DMTA) in tensile and shear mode as well as ‘Differential Scanning Caliometry’ (DSC) tests are carried out on the interlayer material ‘Ethylene-vinyl acetate’ (EVA). A navoel Bayesian framework for the Master Curving Process as well as the detection and parameter identification of the TTSPs along with their associated Prony-series is derived and applied to the EVA material data. To our best knowledge, this is the first time, an uncertainty quantification of the Prony-series in a Bayesian context is shown. Within this paper, we could successfully apply the derived Bayesian methodology to the EVA material data to gather meaningful Master Curves and TTSPs. Uncertainties occurring in this process can be well quantified. We found, that EVA needs two TTSPs with two associated Generalized Maxwell Models. As the methodology is kept general, the derived framework could be also applied to other thermorheologically complex polymers for parameter identification purposes.

Keywords: bayesian parameter identification, generalized Maxwell model, linear viscoelasticity, thermorheological complex

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Authors: Ana Paula Camelo, Keila Sanches

Abstract:

The Geographically Weighted Regression (GWR) was proposed in geography literature to allow relationship in a regression model to vary over space. In Brazil, the agricultural exploitation of the Cerrado Biome is the main cause of deforestation. In this study, we propose a methodology using geostatistical methods to characterize the spatial dependence of deforestation in the Cerrado based on agricultural production indicators. Therefore, it was used the set of exploratory spatial data analysis tools (ESDA) and confirmatory analysis using GWR. It was made the calibration a non-spatial model, evaluation the nature of the regression curve, election of the variables by stepwise process and multicollinearity analysis. After the evaluation of the non-spatial model was processed the spatial-regression model, statistic evaluation of the intercept and verification of its effect on calibration. In an analysis of Spearman’s correlation the results between deforestation and livestock was +0.783 and with soybeans +0.405. The model presented R²=0.936 and showed a strong spatial dependence of agricultural activity of soybeans associated to maize and cotton crops. The GWR is a very effective tool presenting results closer to the reality of deforestation in the Cerrado when compared with other analysis.

Keywords: deforestation, geographically weighted regression, land use, spatial analysis

Procedia PDF Downloads 365

Authors: N. N. Mosola, S. J. Molete, L. S. Masoebe, M. Letsae

Abstract:

Hand gesture recognition is a technique used to locate, detect, and recognize a hand gesture. Detection and recognition are concepts of Artificial Intelligence (AI). AI concepts are applicable in Human Computer Interaction (HCI), Expert systems (ES), etc. Hand gesture recognition can be used in sign language interpretation. Sign language is a visual communication tool. This tool is used mostly by deaf societies and those with speech disorder. Communication barriers exist when societies with speech disorder interact with others. This research aims to build a hand recognition system for Lesotho’s Sesotho and English language interpretation. The system will help to bridge the communication problems encountered by the mentioned societies. The system has various processing modules. The modules consist of a hand detection engine, image processing engine, feature extraction, and sign recognition. Detection is a process of identifying an object. The proposed system uses Canny pruning Haar and Haarcascade detection algorithms. Canny pruning implements the Canny edge detection. This is an optimal image processing algorithm. It is used to detect edges of an object. The system employs a skin detection algorithm. The skin detection performs background subtraction, computes the convex hull, and the centroid to assist in the detection process. Recognition is a process of gesture classification. Template matching classifies each hand gesture in real-time. The system was tested using various experiments. The results obtained show that time, distance, and light are factors that affect the rate of detection and ultimately recognition. Detection rate is directly proportional to the distance of the hand from the camera. Different lighting conditions were considered. The more the light intensity, the faster the detection rate. Based on the results obtained from this research, the applied methodologies are efficient and provide a plausible solution towards a light-weight, inexpensive system which can be used for sign language interpretation.

Keywords: canny pruning, hand recognition, machine learning, skin tracking

Procedia PDF Downloads 186