Search results for: strategy optimization

Authors: Yogish Huchaiah, Chandrashekara Krishnappa

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This study considers the optimization and validation of experimental results using Multi-Functional Criteria Technique (MFCT). MFCT is concerned with structuring and solving decision and planning problems involving multiple variables. Production of biodiesel from Composite Oil Methyl Esters (COME) of Jatropha and Pongamia oils, mixed in various proportions and Biodiesel thus obtained from two step transesterification process were tested for various Physico-Chemical properties and it has been ascertained that they were within limits proposed by ASTME. They were blended with Petrodiesel in various proportions. These Methyl Esters were blended with Petrodiesel in various proportions and coded. These blends were used as fuels in a computerized CI DI engine to investigate Performance and Emission characteristics. From the analysis of results, it was found that 180MEM4B20 blend had the maximum Performance and minimum Emissions. To validate the experimental results, MFCT was used. Characteristics such as Fuel Consumption (FC), Brake Power (BP), Brake Specific Fuel Consumption (BSFC), Brake Thermal Efficiency (BTE), Carbon dioxide (CO2), Carbon Monoxide (CO), Hydro Carbon (HC) and Nitrogen oxide (NOx) were considered as dependent variables. It was found from the application of this method that the optimized combination of Injection Pressure (IP), Mix and Blend is 178MEM4.2B24. Overall corresponding variation between optimization and experimental results was found to be 7.45%.

Keywords: COME, IP, MFCT, optimization, PI, PN, PV

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Authors: Abdolsalam Ghaderi

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In this paper, the single-depot capacitated location-routing problem under uncertainty is presented. The problem aims to find the optimal location of a single depot and the routing of vehicles to serve the customers when the parameters may change under different circumstances. This problem has many applications, especially in the area of supply chain management and distribution systems. To get closer to real-world situations, travel time of vehicles, the fixed cost of vehicles usage and customers’ demand are considered as a source of uncertainty. A combined approach including robust optimization and stochastic programming was presented to deal with the uncertainty in the problem at hand. For this purpose, a mixed integer programming model is developed and a heuristic algorithm based on Variable Neighborhood Search(VNS) is presented to solve the model. Finally, the computational results are presented and future research directions are discussed.

Keywords: location-routing problem, robust optimization, stochastic programming, variable neighborhood search

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Authors: Alper Akin, Ibrahim Aydogdu

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This study presents a hybrid metaheuristic algorithm to obtain optimum designs for steel space buildings. The optimum design problem of three-dimensional steel frames is mathematically formulated according to provisions of LRFD-AISC (Load and Resistance factor design of American Institute of Steel Construction). Design constraints such as the strength requirements of structural members, the displacement limitations, the inter-story drift and the other structural constraints are derived from LRFD-AISC specification. In this study, a hybrid algorithm by using teaching-learning based optimization (TLBO) and harmony search (HS) algorithms is employed to solve the stated optimum design problem. These algorithms are two of the recent additions to metaheuristic techniques of numerical optimization and have been an efficient tool for solving discrete programming problems. Using these two algorithms in collaboration creates a more powerful tool and mitigates each other’s weaknesses. To demonstrate the powerful performance of presented hybrid algorithm, the optimum design of a large scale steel building is presented and the results are compared to the previously obtained results available in the literature.

Keywords: optimum structural design, hybrid techniques, teaching-learning based optimization, harmony search algorithm, minimum weight, steel space frame

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Authors: Ibrahim Isa Ali (Pantami), Abdu Jaafaru Bambale, Abimbola Alale, Danjuma Ibrahim Ndihgihdah, Muhammad Alkali, Adamu Idris Umar, Moshood Kareem, Samson Olufunmilayo Abodunrin, Muhammad Dokko Zubairu

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The National Digital Economy Policy and Strategy, NDEPS document developed by Nigeria’s Federal Ministry of Communications & Digital Economy (FMoCDE) is anchored on 8 pillars for the acceleration of the National Digital Economy for a Digital Nigeria. NIGCOMSAT-1R and the planned HTS communication Satellite are critical assets for supporting the pillars in the drive for sustainable growth and development. This paper discusses on the gains and contribution of the strategy as a solid infrastructure. The paper also highlights these assets’ contribution as platform for Indigenous Content Development & Adoption, Digital Literacy & Skills, and Digital Services Development & Promotion.

Keywords: FMoCDE, HTS, NDEPS, nigcomsat!R, pillars

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Authors: P. Makendran, M. Pragadeesh, N. Narash, N. Manikandan, A. Rajasri, V. Sanal Kumar

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The growing severity of government-obligatory emissions legislation has required continuous improvement in catalysts performance and the associated reactor systems. IC engines emit a lot of harmful gases into the atmosphere. These gases are toxic in nature and a catalytic converter is used to convert these toxic gases into less harmful gases. The catalytic converter converts these gases by Oxidation and reduction reaction. Stoichiometric engines usually use the three-way catalyst (TWC) for simultaneously destroying all of the emissions. CO and NO react to form CO2 and N2 over one catalyst, and the remaining CO and HC are oxidized in a subsequent one. Literature review reveals that typically precious metals are used as a catalyst. The actual reactor is composed of a washcoated honeycomb-style substrate, with the catalyst being contained in the washcoat. The main disadvantage of a catalytic converter is that it exerts a back pressure to the exhaust gases while entering into them. The objective of this paper is to optimize the back pressure developed by the catalytic converter through geometric optimization of catalystic converter. This can be achieved by designing a catalyst with a optimum cone angle and a more surface area of the catalyst substrate. Additionally, the arrangement of the pores in the catalyst substrate can be changed. The numerical studies have been carried out using k-omega turbulence model with varying inlet angle of the catalytic converter and the length of the catalyst substrate. We observed that the geometry optimization is a meaningful objective for the lucrative design optimization of a catalytic converter for industrial applications.

Keywords: catalytic converter, emission control, reactor systems, substrate for emission control

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Authors: Abdelali Laid, Abid Hamza, Zeroukhi Houari, Sayah Naimi

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This work relates to the study of the energy levels and the optimization of the Parameter intrinsic (a number of wells and their widths, width of barrier of potential, index of refraction etc.) and extrinsic (temperature, pressure) in the Structure laser diode containing the structure GaInP. The methods of calculation used; - method of the empirical pseudo potential to determine the electronic structures of bands, - graphic method for optimization. The found results are in concord with those of the experiment and the theory.

Keywords: semi-conductor, GaInP/AlGaInP, pseudopotential, energy, alliages

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Authors: Masoumeh Pirhadi

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Air pollution is becoming a major health problem affecting millions. In support of this observation, the world health organization estimates that many people feel unhealthy due to pollution. This is a coupled fact that one of the main global sources of air pollution in cities is greenhouse gas emissions due heavy traffic. Green walls are developed as a sustainable strategy to reduce pollution by increasing vegetation in developed areas without occupying space in the city. This concept an offer advantageous environmental benefits and they can also be proposed for aesthetic purposes, and today they are used to preserve the urban environment. Green walls can also create environments that can promote a healthy lifestyle. Findings of multiple studies also indicate that Green infrastructure in cities is a strategy for improving air quality and increasing the sustainability of cities. Since these green solutions (green walls) act as porous materials that affect the diffusion of air pollution they can also act as a removing air vents that clean the air. Therefore, implementation of this strategy can be considered as a prominent factor in achieving a cleaner environment.

Keywords: green vegetation, air pollution, green wall, urban area

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Authors: Fatemeh Sadat Miri, Morteza Ehsani, Seyed Farshid Hosseini

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The present research is concerned with the optimization of extrusion parameters of ABS sheets by the Taguchi experimental design method. In this design method, three parameters of % recycling ABS, processing temperature and degassing time on mechanical properties, hardness, HDT, and color matching of ABS sheets were investigated. The variations of this research are the dosage of recycling ABS, processing temperature, and degassing time. According to experimental test data, the highest level of tensile strength and HDT belongs to the sample with 5% recycling ABS, processing temperature of 230°C, and degassing time of 3 hours. Additionally, the minimum level of MFI and color matching belongs to this sample, too. The present results are in good agreement with the Taguchi method. Based on the outcomes of the Taguchi design method, degassing time has the most effect on the mechanical properties of ABS sheets.

Keywords: ABS, process optimization, Taguchi, mechanical properties

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Authors: Ehsan Yadegari

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While there are several metaheuristics and exact approaches to solving the Supply Chain Network Design (SCND) problem, there still remains an unfilled gap in using the Teaching-Learning-Based Optimization (TLBO) algorithm. The algorithm has demonstrated desirable results with problems with complicated combinational optimization. The present study introduces a Discrete Self-Study TLBO (DSS-TLBO) with priority-based solution representation that can solve a supply chain network configuration model to lower the total expenses of establishing facilities and the flow of materials. The network features four layers, namely suppliers, plants, distribution centers (DCs), and customer zones. It is designed to meet the customer’s demand through transporting the material between layers of network and providing facilities in the best economic Potential locations. To have a higher quality of the solution and increase the speed of TLBO, a distinct operator was introduced that ensures self-adaptation (self-study) in the algorithm based on the four types of local search. In addition, while TLBO is used in continuous solution representation and priority-based solution representation is discrete, a few modifications were added to the algorithm to remove the solutions that are infeasible. As shown by the results of experiments, the superiority of DSS-TLBO compared to pure TLBO, genetic algorithm (GA) and firefly Algorithm (FA) was established.

Keywords: supply chain network design, teaching–learning-based optimization, improved metaheuristics, discrete solution representation

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Authors: Eduardo F. Mello, Peter H. Bauer

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The primary focus of this paper is the generation of energy-optimal speed trajectories for heterogeneous electric vehicle platoons in urban driving conditions. Optimal speed trajectories are generated for individual vehicles and for an entire platoon under the assumption that they can be executed without errors, as would be the case for self-driving vehicles. It is then shown that the optimization for the “average vehicle in the platoon” generates similar transportation energy savings to optimizing speed trajectories for each vehicle individually. The introduced approach only requires the lead vehicle to run the optimization software while the remaining vehicles are only required to have adaptive cruise control capability. The achieved energy savings are typically between 30% and 50% for stop-to-stop segments in cities. The prime motivation of urban platooning comes from the fact that urban platoons efficiently utilize the available space and the minimization of transportation energy in cities is important for many reasons, i.e., for environmental, power, and range considerations.

Keywords: electric vehicles, energy efficiency, optimization, platooning, self-driving vehicles, urban traffic

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Authors: Mamidi Ramakrishna Rao, Jagdish Mamidi

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With advancing technology, the market environment for wind power generation systems has become highly competitive. The industry has been moving towards higher wind generator power ratings, in particular, off-shore generator ratings. Current off-shore wind turbine generators are in the power range of 10 to 12 MW. Unlike traditional induction motors, slow-speed permanent magnet surface mounted (PMSM) high-power generators are relatively challenging and designed differently. In this paper, PMSM generator design features have been discussed and analysed. The focus attention is on armature windings, harmonics, and permanent magnet. For the power ratings under consideration, the generator air-gap diameters are in the range of 8 to 10 meters, and active material weigh ~60 tons and above. Therefore, material weight becomes one of the critical parameters. Particle Swarm Optimization (PSO) technique is used for weight reduction and performance improvement. Four independent variables have been considered, which are air gap diameter, stack length, magnet thickness, and winding current density. To account for core and teeth saturation, preventing demagnetization effects due to short circuit armature currents, and maintaining minimum efficiency, suitable penalty functions have been applied. To check for performance satisfaction, a detailed analysis and 2D flux plotting are done for the optimized design.

Keywords: offshore wind generator, PMSM, PSO optimization, design optimization

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Authors: A. Mohammadi, M. Moghimi, S. Mohammadi

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Engineers need computational methods which could provide solutions less sensitive to the environmental effects, so the techniques should be used which take the uncertainty to account to control and minimize the risk associated with design and operation. In order to consider uncertainty in engineering problem, the optimization problem should be solved for a suitable range of the each uncertain input variable instead of just one estimated point. Using deterministic optimization problem, a large computational burden is required to consider every possible and probable combination of uncertain input variables. Several methods have been reported in the literature to deal with problems under uncertainty. In this paper, different methods presented and analyzed.

Keywords: uncertainty, Monte Carlo simulated, stochastic programming, scenario method

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Authors: Hanwei Gao, Louis Jezequel, Eric Cabrol, Bernard Vitry

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The chassis system is composed of complex elements that take up all the loads from the tire-ground contact area and thus it plays an important role in numerous specifications such as durability, comfort, crash, etc. During the development of new vehicle projects in Renault, durability validation is always the main focus while deployment of comfort comes later in the project. Therefore, sometimes design choices have to be reconsidered because of the natural incompatibility between these two specifications. Besides, robustness is also an important point of concern as it is related to manufacturing costs as well as the performance after the ageing of components like shock absorbers. In this paper an approach is proposed aiming to realize a multi-objective optimization between chassis endurance and comfort while taking the random factors into consideration. The adaptive-sparse polynomial chaos expansion method (PCE) with Chebyshev polynomial series has been applied to predict responses’ uncertainty intervals of a system according to its uncertain-but-bounded parameters. The approach can be divided into three steps. First an initial design of experiments is realized to build the response surfaces which represent statistically a black-box system. Secondly within several iterations an optimum set is proposed and validated which will form a Pareto front. At the same time the robustness of each response, served as additional objectives, is calculated from the pre-defined parameter intervals and the response surfaces obtained in the first step. Finally an inverse strategy is carried out to determine the parameters’ tolerance combination with a maximally acceptable degradation of the responses in terms of manufacturing costs. A quarter car model has been tested as an example by applying the road excitations from the actual road measurements for both endurance and comfort calculations. One indicator based on the Basquin’s law is defined to compare the global chassis durability of different parameter settings. Another indicator related to comfort is obtained from the vertical acceleration of the sprung mass. An optimum set with best robustness has been finally obtained and the reference tests prove a good robustness prediction of Chebyshev PCE method. This example demonstrates the effectiveness and reliability of the approach, in particular its ability to save computational costs for a complex system.

Keywords: chassis durability, Chebyshev polynomials, multi-objective optimization, polynomial chaos expansion, ride comfort, robust design

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Authors: Ouahab Kadri, Leila Hayet Mouss

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In this paper, we present three diagnostic modules for complex and dynamic systems. These modules are based on three ant colony algorithms, which are AntTreeStoch, Lumer & Faieta and Binary ant colony. We chose these algorithms for their simplicity and their wide application range. However, we cannot use these algorithms in their basement forms as they have several limitations. To use these algorithms in a diagnostic system, we have proposed three variants. We have tested these algorithms on datasets issued from two industrial systems, which are clinkering system and pasteurization system.

Keywords: ant colony algorithms, complex and dynamic systems, diagnosis, classification, optimization

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Authors: Andrey V. Timofeev, Dmitry V. Egorov

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This paper introduces an original method of parametric optimization of the structure for multimodal decision-level fusion scheme which combines the results of the partial solution of the classification task obtained from assembly of the mono-modal classifiers. As a result, a multimodal fusion classifier which has the minimum value of the total error rate has been obtained.

Keywords: classification accuracy, fusion solution, total error rate, multimodal fusion classifier

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Authors: Soudabeh Shemehsavar

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In this paper, we consider the situation under a life test, in which the failure time of the test units are not related deterministically to an observable stochastic time varying covariate. In such a case, the joint distribution of failure time and a marker value would be useful for modeling the step stress life test. The problem of accelerating such an experiment is considered as the main aim of this paper. We present a step stress accelerated model based on a bivariate Wiener process with one component as the latent (unobservable) degradation process, which determines the failure times and the other as a marker process, the degradation values of which are recorded at times of failure. Parametric inference based on the proposed model is discussed and the optimization procedure for obtaining the optimal time for changing the stress level is presented. The optimization criterion is to minimize the approximate variance of the maximum likelihood estimator of a percentile of the products’ lifetime distribution.

Keywords: bivariate normal, Fisher information matrix, inverse Gaussian distribution, Wiener process

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Authors: Charlene Petersen, Herman Grobler, Karel Botha

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Restorative intervention with adolescents from a family violence context is a much needed resource given the limited access to mental health services in South Africa. In this research article the qualitative component which formed part of a mixed methods design of an overall research study, is discussed. A qualitative case study design was used. This article explores a psycho-education strategy, using a visual creative medium as a method for reconstructing identity in the context of family violence. The aim of this psycho-education strategy was to move away from interventions based on the biomedical model, but focused more on meaning-making about violent traumatic events adolescents have experienced, and to develop more positive, adaptive views about themselves in the present, and experience hope about the future. The research question that was asked was how the meaning, that adolescents from a specific community in South Africa give to family violence, contribute to defining their identity? Twelve participants were purposively selected for the study and included both male and female adolescents with ages ranging from 15 to 18 years from three secondary schools. The strategy was applied over five sessions with the intention to bring about awareness of different selves, identifying and naming the selves, and becoming aware of the configuration of identity that could lead to a redefined identity. The data were thematically and visually analyzed. Through the process of tailoring which forms the basis for reconstruction process, participants could identify the different selves, become aware of how they configure in the field. Through the process of tailoring the different self-parts, the sense of self became more self-cohesive and allowed the individual to become aware of the role of certain. Through identifying and naming the future self and resilient self the participants were able to accomplish some order and meaning in their lives. It provided them with a sense of predictability and an optimistic and hopefulness towards the future. The research findings indicated that this strategy can be used as a method for reconstructing identity in the context of family violence.

Keywords: identity, family violence, self-configuration, reconstructing identity, psycho-education strategy

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Authors: Majid Arvan, Peyman Beygi, Sina Rokhsati

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In-time detection of fire in buildings is of great importance. Employing intelligent methods in data processing in fire detection systems leads to a significant reduction of fire damage at lowest cost. In this paper, the raw data obtained from the fire detection sensor networks in buildings is processed by using intelligent methods based on neural networks and the likelihood of fire happening is predicted. In order to enhance the quality of system, the noise in the sensor data is reduced by analyzing wavelets and applying SVD technique. Meanwhile, the proposed neural network is trained using particle swarm optimization (PSO). In the simulation work, the data is collected from sensor network inside the room and applied to the proposed network. Then the outputs are compared with conventional MLP network. The simulation results represent the superiority of the proposed method over the conventional one.

Keywords: intelligent fire detection, neural network, particle swarm optimization, fire sensor network

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Authors: S. N. Susenburger, D. Boecker

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In the age of Volatility, Uncertainty, Complexity, and Ambiguity (VUCA), where digital transformation is challenging long standing traditional hardware and manufacturing companies, innovation needs a different methodology, strategy, mindset, and culture. What used to be a mindset of scaling per quantity is now shifting to orchestrating ecosystems, platform business models and service bundles. While large corporations are trying to mimic the nimbleness and versatile mindset of startups in the core of their digital strategies, they’re at the frontier of facing one of the largest organizational and cultural changes in history. This paper elaborates on how a manufacturing giant transformed its Corporate Information Technology (IT) to enable digital and Internet of Things (IoT) business while establishing the mindset and the approaches of the Innovation Inside-Out/Outside-In Strategy. It gives insights into the core elements of an innovation culture and the tactics and methodologies leveraged to support the cultural shift and transformation into an IoT company. This paper also outlines the core elements for an innovation culture and how the persona 'Connected Engineer' thrives in the digital innovation environment. Further, it explores how tapping domain-focused ecosystems in vibrant innovative cities can be used as a part of the strategy to facilitate partner co-innovation. Therefore, findings from several use cases, observations and surveys led to conclusion for the strategy tandem of Innovation Inside-Out/Outside-In. The findings indicate that it's crucial in which phases and maturity level the Innovation Inside-Out/Outside-In Strategy is activated: cultural aspects of the business and the regional ecosystem need to be considered, as well as cultural readiness from management and active contributors. The 'not invented here syndrome' is a barrier of large corporations that need to be addressed and managed to successfully drive partnerships, as well as embracing co-innovation and a mindset shifting away from physical products toward new business models, services, and IoT platforms. This paper elaborates on various methodologies and approaches tested in different countries and cultures, including the U.S., Brazil, Mexico, and Germany.

Keywords: innovation management, innovation culture, innovation methodologies, digital transformation

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Authors: Pooja Rani, G. S. Mahapatra, S. K. Pandey

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In this paper, we propose a novel approach of Neural Network and Particle Swarm Optimization methods for software reliability prediction. We first explain how to apply compound function in neural network so that we can derive a Flexible Logistic (S-shaped) Growth Curve (FLGC) model. This model mathematically represents software failure as a random process and can be used to evaluate software development status during testing. To avoid trapping in local minima, we have applied Particle Swarm Optimization method to train proposed model using failure test data sets. We drive our proposed model using computational based intelligence modeling. Thus, proposed model becomes Neuro-Particle Swarm Optimization (NPSO) model. We do test result with different inertia weight to update particle and update velocity. We obtain result based on best inertia weight compare along with Personal based oriented PSO (pPSO) help to choose local best in network neighborhood. The applicability of proposed model is demonstrated through real time test data failure set. The results obtained from experiments show that the proposed model has a fairly accurate prediction capability in software reliability.

Keywords: software reliability, flexible logistic growth curve model, software cumulative failure prediction, neural network, particle swarm optimization

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Authors: Jin Ho Kim, Yujeong Shin, Seong-Jin Cho, Dong-Jin Kim, U-Syn Ha

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Due to the development of the information industry and technologies, cellular phones have must not only function to communicate, but also have functions such as the Internet, e-banking, entertainment, etc. These phones are called smartphones. The performance of smartphones has improved, because of the various functions of smartphones, and the capacity of the battery has been increased gradually. Recently, linear generators have been embedded in smartphones in order to recharge the smartphone's battery. In this study, optimization is performed and an array change of permanent magnets is examined in order to increase efficiency. We propose an optimal design using design of experiments (DOE) to maximize the generated induced voltage. The thickness of the poleshoe and permanent magnet (PM), the height of the poleshoe and PM, and the thickness of the coil are determined to be design variables. We made 25 sampling points using an orthogonal array according to four design variables. We performed electromagnetic finite element analysis to predict the generated induced voltage using the commercial electromagnetic analysis software ANSYS Maxwell. Then, we made an approximate model using the Kriging algorithm, and derived optimal values of the design variables using an evolutionary algorithm. The commercial optimization software PIAnO (Process Integration, Automation, and Optimization) was used with these algorithms. The result of the optimization shows that the generated induced voltage is improved.

Keywords: smartphone, linear generator, design of experiment, approximate model, optimal design

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Authors: Ahmed Elrewainy

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Mixing in the hyperspectral imaging occurs due to the low spatial resolutions of the used cameras. The existing pure materials “endmembers” in the scene share the spectra pixels with different amounts called “abundances”. Unmixing of the data cube is an important task to know the present endmembers in the cube for the analysis of these images. Unsupervised unmixing is done with no information about the given data cube. Sparsity is one of the recent approaches used in the source recovery or unmixing techniques. The l₁-norm optimization problem “basis pursuit” could be used as a sparsity-based approach to solve this unmixing problem where the endmembers is assumed to be sparse in an appropriate domain known as dictionary. This optimization problem is solved using proximal method “iterative thresholding”. The l₁-norm basis pursuit optimization problem as a sparsity-based unmixing technique was used to unmix real and synthetic hyperspectral data cubes.

Keywords: basis pursuit, blind source separation, hyperspectral imaging, spectral unmixing, wavelets

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Authors: Gabriel Odeh Ankeli

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The study determined how mentoring enhanced strategy can increase self-efficacy of secondary school students in physics in education zone C of Benue State, Nigeria. The study was guided by two research questions while two hypotheses were formulated and tested at 0.05 level of significance. The design of the study was the quasi-experimental, non-randomized, pre-test and post-test control groups. The population of the study consisted of 4,064 SS two physics students in the 94 schools in Education Zone C. The sample comprised 406 SS two physics students drawn from 10 schools using multi-stage sampling technique. The research instrument adapted and used for data collection was Students Self-Efficacy Scale (SSES). The research instrument was subjected to a reliability analysis using Cronbachs Alpha which yielded a reliability co-efficient of 0.84. Data collected were analyzed using discriptive statistics of mean and standard deviation to answer the research questions while inferential statistics of Analysis of Covariance (ANCOVA) was used to test the hypotheses. The findings revealed that students who were exposed to mentoring exhibited lower self-efficacy levels (F 1,405 = 2.751, P = 0.09˃0.05) than those students who were not exposed to mentoring. There was significant difference between male and female students’ self-efficacy level (F 1,211 = 5.496, P = 0.02˂0.05). Based on these findings, it was recommended among others that longer duration of mentoring period should be encouraged when using the mentoring strategy for better enhancement of Self-efficacy of students.

Keywords: physics, self-efficacy, mentoring enhanced strategy, students

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Authors: Felice De Luca, Vincenzo Galdi, Piera Stella, Vito Calderaro, Adriano Campagna, Antonio Piccolo

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Sustainable mobility has become one of the major issues of recent years. The challenge in reducing polluting emissions as much as possible has led to the production and diffusion of vehicles with internal combustion engines that are less polluting and to the adoption of green energy vectors, such as vehicles powered by natural gas or LPG and, more recently, with hybrid and electric ones. While on the one hand, the spread of electric vehicles for private use is becoming a reality, albeit rather slowly, not the same is happening for vehicles used for public transport, especially those that operate in the congested areas of the cities. Even if the first electric buses are increasingly being offered on the market, it remains central to the problem of autonomy for battery fed vehicles with high daily routes and little time available for recharging. In fact, at present, solid-state batteries are still too large in size, heavy, and unable to guarantee the required autonomy. Therefore, in order to maximize the energy management on the vehicle, the optimization of driving profiles offer a faster and cheaper contribution to improve vehicle autonomy. In this paper, following the authors’ precedent works on electric vehicles in public transport and energy management strategies in the electric mobility area, an eco-driving strategy for electric bus is presented and validated. Particularly, the characteristics of the prototype bus are described, and a general-purpose eco-drive methodology is briefly presented. The model is firstly simulated in MATLAB™ and then implemented on a mobile device installed on-board of a prototype bus developed by the authors in a previous research project. The solution implemented furnishes the bus-driver suggestions on the guide style to adopt. The result of the test in a real case will be shown to highlight the effectiveness of the solution proposed in terms of energy saving.

Keywords: eco-drive, electric bus, energy management, prototype

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Authors: Mohammad H. Almomani

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In this paper, we argue the effect of the initial sample size, and the increment in simulation samples on the performance of a sequential approach that used in selecting the top m designs when the number of alternative designs is very large. The sequential approach consists of two stages. In the first stage the ordinal optimization is used to select a subset that overlaps with the set of actual best k% designs with high probability. Then in the second stage the optimal computing budget is used to select the top m designs from the selected subset. We apply the selection approach on a generic example under some parameter settings, with a different choice of initial sample size and the increment in simulation samples, to explore the impacts on the performance of this approach. The results show that the choice of initial sample size and the increment in simulation samples does affect the performance of a selection approach.

Keywords: Large Scale Problems, Optimal Computing Budget Allocation, ordinal optimization, simulation optimization

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Authors: Odunayo S. Faluse, Roger Telfer

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Customer relationship management is a business strategy that is centred on the idea that ‘Customer is the driving force of any business’ i.e. Customer is placed in a central position in any business. However, this belief coupled with the advancement in information technology in the past twenty years has experienced a change. In any form of business today it can be concluded that customers are the modern dictators to whom the industry always adjusts its business operations due to the increase in availability of information, intense market competition and ever growing negotiating ideas of customers in the process of buying and selling. The most vital role of any organization is to satisfy or meet customer’s needs and demands, which eventually determines customer’s long-term value to the industry. Therefore, this paper analyses and describes the application of customer relationship management operational strategies in some of the major industries in business. Both developed and up-coming companies nowadays value the quality of customer services and client’s loyalty, they also recognize the customers that are not very sensitive when it comes to changes in price and thereby realize that attracting new customers is more tasking and expensive than retaining the existing customers. However, research shows that several factors have recently amounts to the sudden rise in the execution of CRM strategies in the marketplace, such as a diverted attention of some organization towards integrating ideas in retaining existing customers rather than attracting new one, gathering data about customers through the use of internal database system and acquiring of external syndicate data, also exponential increase in technological intelligence. Apparently, with this development in business operations, CRM research in Academia remain nascent; hence this paper gives detailed critical analysis of the recent advancement in the use of CRM and key research opportunities for future development in using the implementation of CRM as a determinant factor for successful business optimization.

Keywords: agriculture, banking, business strategies, CRM, education, healthcare

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Authors: Eny Syatriana

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English proficiency of Indonesian secondary school students is below standard. The low proficiency may come from poor teaching materials that do not meet the students’ need. The main objective for English teachers is to improve the English proficiency of the students. The purpose of this study is to explore the application Mapping, Practicing, Using (MPU) strategy with Instructional Materials Based on the School Curriculum toward the English achievement of Indonesian EFL Students. This paper is part my dissertation entitles 'Designing instructional materials for secondary school students based on the school curriculum' consisting of need analysis, design, development, implementation, and evaluation; this paper discusses need analysis and creates a model of creating instructional materials through deep discussion among teachers of secondary schools. The subject consisted of six English teachers and students of three classes at three different secondary schools in Makassar, South Sulawesi, Indonesia. Pretest and posttest design were administered to see the effectiveness of the MPU strategy. Questionnaires were administered to see the teachers and students’ perception toward the instructional materials. The result indicates that the MPU strategy is effective in improving the English achievement; instructional materials with different strategies improve the English achievement of the students. Both teachers and students argue that the presented instructional materials are effective to be used in the teaching and learning process to increase the English proficiency of the students.

Keywords: proficiency, development, English for secondary school students, instructional materials

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Authors: Ali Shirazi, Robert A. Taylor, Stephen D. White, Graham L. Morrison

Abstract:

In this paper, a detailed simulation model of a solar-powered triple-effect LiBr–H₂O absorption chiller is developed to supply both cooling and heating demand of a large-scale building, aiming to reduce the fossil fuel consumption and greenhouse gas emissions in building sector. TRNSYS 17 is used to simulate the performance of the system over a typical year. A combined energetic-economic-environmental analysis is conducted to determine the system annual primary energy consumption and the total cost, which are considered as two conflicting objectives. A multi-objective optimization of the system is performed using a genetic algorithm to minimize these objectives simultaneously. The optimization results show that the final optimal design of the proposed plant has a solar fraction of 72% and leads to an annual primary energy saving of 0.69 GWh and annual CO₂ emissions reduction of ~166 tonnes, as compared to a conventional HVAC system. The economics of this design, however, is not appealing without public funding, which is often the case for many renewable energy systems. The results show that a good funding policy is required in order for these technologies to achieve satisfactory payback periods within the lifetime of the plant.

Keywords: economic, environmental, multi-objective optimization, solar air-conditioning, triple-effect absorption chiller

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Authors: Tahsin A. H. Nishat, Raquib Ahsan

Abstract:

Sensitivity analysis of design parameters of the optimization procedure can become a significant factor while designing any structural system. The objectives of the study are to analyze the sensitivity of deck slab thickness parameter obtained from both the conventional and optimum design methodology of pre-stressed post-tensioned I-girder and deck system and to compare the relative significance of slab thickness. For analysis on conventional method, the values of 14 design parameters obtained by the conventional iterative method of design of a real-life I-girder bridge project have been considered. On the other side for analysis on optimization method, cost optimization of this system has been done using global optimization methodology 'Evolutionary Operation (EVOP)'. The problem, by which optimum values of 14 design parameters have been obtained, contains 14 explicit constraints and 46 implicit constraints. For both types of design parameters, sensitivity analysis has been conducted on deck slab thickness parameter which can become too sensitive for the obtained optimum solution. Deviations of slab thickness on both the upper and lower side of its optimum value have been considered reflecting its realistic possible ranges of variations during construction. In this procedure, the remaining parameters have been kept unchanged. For small deviations from the optimum value, compliance with the explicit and implicit constraints has been examined. Variations in the cost have also been estimated. It is obtained that without violating any constraint deck slab thickness obtained by the conventional method can be increased up to 25 mm whereas slab thickness obtained by cost optimization can be increased only up to 0.3 mm. The obtained result suggests that slab thickness becomes less sensitive in case of conventional method of design. Therefore, for realistic design purpose sensitivity should be conducted for any of the design procedure of girder and deck system.

Keywords: sensitivity analysis, optimum design, evolutionary operations, PC I-girder, deck system

Procedia PDF Downloads 131

Authors: Tareq Hamadneh, Jochen Merker, Hassan Al-Zoubi

Abstract:

Bernstein's expansion for exponentials in Taylor functions provides lower and upper optimization values for the range of its original function. these values converge to the original functions if the degree is elevated or the domain subdivided. Taylor polynomial can be applied so that the exponential is a polynomial of finite degree over a given domain. Bernstein's basis has two main properties: its sum equals 1, and positive for all x 2 (0; 1). In this work, we prove the existence of fixed points for exponential functions in a given domain using the optimization values of Bernstein. The Bernstein basis of finite degree T over a domain D is defined non-negatively. Any polynomial p of degree t can be expanded into the Bernstein form of maximum degree t ≤ T, where we only need to compute the coefficients of Bernstein in order to optimize the original polynomial. The main property is that p(x) is approximated by the minimum and maximum Bernstein coefficients (Bernstein bound). If the bound is contained in the given domain, then we say that p(x) has fixed points in the same domain.

Keywords: Bernstein polynomials, Stability of control functions, numerical optimization, Taylor function

Procedia PDF Downloads 129