Search results for: optimal smoothing parameter

Authors: Maher Agi, Hardik Soni

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This paper presents a deterministic model for optimized control of the inventory of a perishable product subject to both physical deterioration and degradation of its freshness condition. The demand for the product depends on its current inventory level and freshness condition. Our model allows for any positive amount of end of cycle inventory. Some useful conditions that characterize the optimal solution of the model are derived and an algorithm is presented for finding the optimal values of the price, the inventory cycle, the end of cycle inventory level and the order quantity. Numerical examples are then given. Our work shows how the product freshness in conjunction with the inventory deterioration affects the inventory management decisions.

Keywords: inventory management, lot sizing, perishable products, deteriorating inventory, age-dependent demand, stock-dependent demand

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Authors: Azam Mohammadbagheri, Bahram Fathi

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DEA has become a very popular method of performance measure, but it still suffers from some shortcomings. One of these shortcomings is the issue of having multiple optimal solutions to weights for efficient DMUs. The cross efficiency evaluation as an extension of DEA is proposed to avoid this problem. Lam (2010) is also proposed a mixed-integer linear programming formulation based on linear discriminate analysis and super efficiency method (MILP model) to avoid having multiple optimal solutions to weights. In this study, we modified MILP model to determine more suitable weight sets and also evaluate the energy efficiency of MENA countries as an application of the proposed model.

Keywords: data envelopment analysis, discriminate analysis, cross efficiency, MILP model

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Authors: Seyed Esmail Seyedi Bariran, Khairul Salleh Mohamed Sahari

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Apparent quality defects such as warpage, shrinkage, weld line, etc. are such an irresistible phenomenon in mass production of auto plastic appearance parts. These frequently occurred manufacturing defects should be satisfied concurrently so as to achieve a final product with acceptable quality standards. Determining the significant control factors that simultaneously affect multiple quality characteristics can significantly improve the optimization results by eliminating the deviating effect of the so-called ineffective outliers. Hence, a robust quantitative approach needs to be developed upon which major control factors and their level can be effectively determined to help improve the reliability of the optimal processing parameter design. Hence, the primary objective of current study was to develop a systematic methodology for selection of significant control factors (SCF) relevant to multiple quality optimization of auto plastic appearance part. Auto bumper was used as a specimen with the most identical quality and production characteristics to APAP group. A preliminary failure modes and effect analysis (FMEA) was conducted to nominate a database of pseudo significant significant control factors prior to the optimization phase. Later, CAE simulation Moldflow analysis was implemented to manipulate four rampant plastic injection quality defects concerned with APAP group including warpage deflection, volumetric shrinkage, sink mark and weld line. Furthermore, a step-backward elimination searching method (SESME) has been developed for systematic pre-optimization selection of SCF based on hierarchical orthogonal array design and priority-based one-way analysis of variance (ANOVA). The development of robust parameter design in the second phase was based on DOE module powered by Minitab v.16 statistical software. Based on the F-test (F 0.05, 2, 14) one-way ANOVA results, it was concluded that for warpage deflection, material mixture percentage was the most significant control factor yielding a 58.34% of contribution while for the other three quality defects, melt temperature was the most significant control factor with a 25.32%, 84.25%, and 34.57% contribution for sin mark, shrinkage and weld line strength control. Also, the results on the he least significant control factors meaningfully revealed injection fill time as the least significant factor for both warpage and sink mark with respective 1.69% and 6.12% contribution. On the other hand, for shrinkage and weld line defects, the least significant control factors were holding pressure and mold temperature with a 0.23% and 4.05% overall contribution accordingly.

Keywords: plastic injection moulding, quality optimization, FMEA, ANOVA, SESME, APAP

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Authors: S. Neelima, P. S. Subramanyam

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A distribution system is an interface between the bulk power system and the consumers. Among these systems, radial distributions system is popular because of low cost and simple design. In distribution systems, the voltages at buses reduces when moved away from the substation, also the losses are high. The reason for a decrease in voltage and high losses is the insufficient amount of reactive power, which can be provided by the shunt capacitors. But the placement of the capacitor with an appropriate size is always a challenge. Thus, the optimal capacitor placement problem is to determine the location and size of capacitors to be placed in distribution networks in an efficient way to reduce the power losses and improve the voltage profile of the system. For this purpose, in this paper, two stage methodologies are used. In the first stage, the load flow of pre-compensated distribution system is carried out using ‘dimension reducing distribution load flow algorithm (DRDLFA)’. On the basis of this load flow the potential locations of compensation are computed. In the second stage, Genetic Algorithm (GA) technique is used to determine the optimal location and size of the capacitors such that the cost of the energy loss and capacitor cost to be a minimum. The above method is tested on IEEE 9 and 34 bus system and compared with other methods in the literature.

Keywords: dimension reducing distribution load flow algorithm, DRDLFA, genetic algorithm, electrical distribution network, optimal capacitors placement, voltage profile improvement, loss reduction

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Authors: Edén Bojórquez, Victor Baca, Alfredo Reyes-Salazar, Jorge González

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In this paper, simplified equations to predict maximum inter-story drift demands of steel framed buildings are proposed in terms of two ground motion intensity measures based on the acceleration spectral shape. For this aim, the maximum inter-story drifts of steel frames with 4, 6, 8 and 10 stories subjected to narrow-band ground motion records are estimated and compared with the spectral acceleration at first mode of vibration Sa(T1) which is commonly used in earthquake engineering and seismology, and with a new parameter related with the structural response known as INp. It is observed that INp is the parameter best related with the structural response of steel frames under narrow-band motions. Finally, equations to compute maximum inter-story drift demands of steel frames as a function of spectral acceleration and INp are proposed.

Keywords: intensity measures, spectral shape, steel frames, peak demands

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Authors: Sai Sankalp Vemavarapu

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This paper discusses the application of machine learning in the field of transportation engineering for predicting engineering properties of pavement more accurately and efficiently. Predicting the elastic properties aid us in assessing the current road conditions and taking appropriate measures to avoid any inconvenience to commuters. This improves the longevity and sustainability of the pavement layer while reducing its overall life-cycle cost. As an example, we have implemented differential evolution (DE) in the back-calculation of the elastic modulus of multi-layered pavement. The proposed DE global optimization back-calculation approach is integrated with a forward response model. This approach treats back-calculation as a global optimization problem where the cost function to be minimized is defined as the root mean square error in measured and computed deflections. The optimal solution which is elastic modulus, in this case, is searched for in the solution space by the DE algorithm. The best DE parameter combinations and the most optimum value is predicted so that the results are reproducible whenever the need arises. The algorithm’s performance in varied scenarios was analyzed by changing the input parameters. The prediction was well within the permissible error, establishing the supremacy of DE.

Keywords: cost function, differential evolution, falling weight deflectometer, genetic algorithm, global optimization, metaheuristic algorithm, multilayered pavement, pavement condition assessment, pavement layer moduli back calculation

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Authors: Zheni S. Stefanova, Kandethody M. Ramachandran

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With the increasing dependency on our computer devices, we face the necessity of adequate, efficient and effective mechanisms, for protecting our network. There are two main problems that Intrusion Detection Systems (IDS) attempt to solve. 1) To detect the attack, by analyzing the incoming traffic and inspect the network (intrusion detection). 2) To produce a prompt response when the attack occurs (intrusion prevention). It is critical creating an Intrusion detection model that will detect a breach in the system on time and also challenging making it provide an automatic and with an acceptable delay response at every single stage of the monitoring process. We cannot afford to adopt security measures with a high exploiting computational power, and we are not able to accept a mechanism that will react with a delay. In this paper, we will propose an intrusion response mechanism that is based on artificial intelligence, and more precisely, reinforcement learning techniques (RLT). The RLT will help us to create a decision agent, who will control the process of interacting with the undetermined environment. The goal is to find an optimal policy, which will represent the intrusion response, therefore, to solve the Reinforcement learning problem, using a Q-learning approach. Our agent will produce an optimal immediate response, in the process of evaluating the network traffic.This Q-learning approach will establish the balance between exploration and exploitation and provide a unique, self-learning and strategic artificial intelligence response mechanism for IDS.

Keywords: cyber security, intrusion prevention, optimal policy, Q-learning

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Authors: Abdulhakeem Yusuf, Yomi Monday Aiyesimi, Mohammed Jiya

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The failure in an ordinary heat transfer fluid to meet up with today’s industrial cooling rate has resulted in the development of high thermal conductivity fluid which nanofluids belongs. In this work, the problem of unsteady one dimensional laminar flow of an incompressible fluid within a parallel wall is considered with one wall assumed to be wavy. The model is presented in its rectangular coordinate system and incorporates the effects of thermophoresis and Brownian motion. The local similarity solutions were also obtained which depends on Soret number, Dufour number, Biot number, Lewis number, and heat generation parameter. The analytical solution is obtained in a closed form via the Adomian decomposition method. It was found that the method has a good agreement with the numerical method, and it is also established that the heat generation parameter has to be kept low so that heat energy are easily evacuated from the system.

Keywords: Adomian decomposition method, Biot number, Dufour number, nanofluid

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Authors: M. Amara, M. Tahar Abbes, A. Dokkiche, M. Benbrike

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Shear stresses generate frictional forces thus lead to the reduction of engine performance due to the power losses. This friction can also cause damage to the piston material. Thus, the choice of an optimal material for the piston is necessary to improve the elastohydrodynamical contacts of the piston. In this study, to achieve this objective, an elastohydrodynamical lubrication model that satisfies the best tribological behavior of the piston with the optimum choice of material is developed. Several aluminum alloys composed of different components are studied in this simulation. An application is made on the piston 60 x 120 mm Diesel engine type F8L413 currently mounted on Deutz trucks TB230 by using different aluminum alloys where alloys based on aluminum-silicon have better tribological performance.

Keywords: EHD lubricated contacts, friction, properties of materials, tribological performance

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Authors: Jing Zhao, Yoshiharu Ishikawa, Chuan Xiao, Kento Sugiura

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In order to analyze large-scale scientific data, research on data exploration and visualization has gained popularity. In this paper, we focus on the exploration and visualization of scientific simulation data, and define a spatial V-Optimal histogram for data summarization. We propose histogram construction algorithms based on a general binary hierarchical partitioning as well as a more specific one, the l-grid partitioning. For effective data summarization and efficient data visualization in scientific data analysis, we propose an optimal algorithm as well as a heuristic algorithm for histogram construction. To verify the effectiveness and efficiency of the proposed methods, we conduct experiments on the massive evacuation simulation data.

Keywords: simulation data, data summarization, spatial histograms, exploration, visualization

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Authors: Srijitra Khanpakdee, Teera Butburee, Jung-Ho Yun, Miaoqiang Lyu, Supphasin Thaweesak, Piangjai Peerakiatkhajohn

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The synthesis of titanium dioxide (TiO₂) nanorods (NRs) on fluorine-doped tin oxide (FTO) glass via hydrothermal methods was investigated to determine the optimal reaction time for enhanced photocatalytic and optical performance. Reaction times of 4, 6, and 8 hours were studied. Characterization through SEM, UV-vis, XRD, FTIR, Raman spectroscopy and photoelectrochemical (PEC) techniques revealed significant differences in the properties of the TiO₂ NRs based on the reaction duration. XRD and Raman spectroscopy analysis confirmed the formation of the rutile phase of TiO₂. As photoanodes in PEC cells, TiO₂ NRs synthesized for 4 hours exhibited the best photocatalytic activity, with the highest photocurrent density and superior charge transport properties, attributed to their densely packed vertical structure. Longer reaction times resulted in less optimal morphological and photoelectrochemical characteristics. The bandgap of the TiO₂ NRs remained consistent around 3.06 eV, with only slight variations observed. This study highlights the critical role of reaction time in hydrothermal synthesis, identifying 4 hours as the optimal duration for producing TiO₂ NRs with superior photoelectrochemical performance. These findings provide valuable insights for optimizing TiO₂-based materials for solar energy conversion and renewable energy applications.

Keywords: titanium dioxide, nanorods, hydrothermal, photocatalytic, photoelectrochemical

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Authors: S. Gherbi, F. Bouchareb

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This paper deals with the novel intelligent bio-inspired control strategies, it presents a novel approach based on an optimal fuzzy immune PID parameters tuning, it is a combination of a PID controller, inspired by the human immune mechanism with fuzzy logic. Such controller offers more possibilities to deal with the delayed systems control difficulties due to the delay term. Indeed, we use an optimization approach to tune the four parameters of the controller in addition to the fuzzy function; the obtained controller is implemented in a modified Smith predictor structure, which is well known that it is the most efficient to the control of delayed systems. The application of the presented approach to control a three tank delay system shows good performances and proves the efficiency of the method.

Keywords: delayed systems, fuzzy immune PID, optimization, Smith predictor

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Authors: G. V. Siva Krishna Rao, B. Srinivasa Rao

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Electrical energy is vital in every aspect of day-to-day life. Keen interest is taken on all possible sources of energy from which it can be generated and this led to the encouragement of generating electrical power using renewable energy resources such as solar, tidal waves and wind energy. Due to the increasing interest on renewable sources in recent times, the studies on integration of distributed generation to the power grid have rapidly increased. Distributed Generation (DG) is a promising solution to many power system problems such as voltage regulation, power loss and reduction in operational cost, etc. To reduce production cost, it is important to minimize the losses by determining the location and size of local generators to be placed in the radial distribution systems. In this paper, reduction of production cost by optimal size of DG unit operated at optimal power factor is dealt. The optimal size of the DG unit is calculated analytically using approximate reasoning suitable nodes and DG placement to minimize production cost with minimum loss is determined by fuzzy technique. Total Cost of Power generation is compared with and without DG unit for 1 year duration. The suggested method is programmed under MATLAB software and is tested on IEEE 33 bus system and the results are presented.

Keywords: distributed generation, operational cost, exact loss formula, optimum size, optimum location

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Authors: Mahmoud Samkan

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Dispersed Generation (DG) is a promising solution to many power system problems such as voltage regulation and power loss. This paper proposes a heuristic two-step method to optimize the location and size of DG for reducing active power losses and, therefore, improve the voltage profile in radial distribution networks. In addition to a DG placed at the system load gravity center, this method consists in assigning a DG to each lateral of the network. After having determined the central DG placement, the location and size of each lateral DG are predetermined in the first step. The results are then refined in the second step. This method is tested for 33-bus system for 100% DG penetration. The results obtained are compared with those of other methods found in the literature.

Keywords: optimal location, optimal size, dispersed generation (DG), radial distribution networks, reducing losses

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Authors: Alina Fedossova, Jose Jorge Sierra Molina

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SIP problems are part of non-classical optimization. There are problems in which the number of variables is finite, and the number of constraints is infinite. These are semi-infinite programming problems. Most algorithms for semi-infinite programming problems reduce the semi-infinite problem to a finite one and solve it by classical methods of linear or nonlinear programming. Typically, any of the constraints or the objective function is nonlinear, so the problem often involves nonlinear programming. An investment portfolio is a set of instruments used to reach the specific purposes of investors. The risk of the entire portfolio may be less than the risks of individual investment of portfolio. For example, we could make an investment of M euros in N shares for a specified period. Let yi> 0, the return on money invested in stock i for each dollar since the end of the period (i = 1, ..., N). The logical goal here is to determine the amount xi to be invested in stock i, i = 1, ..., N, such that we maximize the period at the end of ytx value, where x = (x1, ..., xn) and y = (y1, ..., yn). For us the optimal portfolio means the best portfolio in the ratio "risk-return" to the investor portfolio that meets your goals and risk ways. Therefore, investment goals and risk appetite are the factors that influence the choice of appropriate portfolio of assets. The investment returns are uncertain. Thus we have a semi-infinite programming problem. We solve a semi-infinite optimization problem of portfolio selection using the outer approximations methods. This approach can be considered as a developed Eaves-Zangwill method applying the multi-start technique in all of the iterations for the search of relevant constraints' parameters. The stochastic outer approximations method, successfully applied previously for robotics problems, Chebyshev approximation problems, air pollution and others, is based on the optimal criteria of quasi-optimal functions. As a result we obtain mathematical model and the optimal investment portfolio when yields are not clear from the beginning. Finally, we apply this algorithm to a specific case of a Colombian bank.

Keywords: outer approximation methods, portfolio problem, semi-infinite programming, numerial solution

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Authors: Eung-Seok Park, Tae-Chul Jeong, Hyun-Jong Park, Hyun-Woo Jun, Dong-Woo Kang, Ju Lee

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This presents a methodology to determine detail design directions of an 150kW-class IPMSM (interior permanent magnet synchronous motor) and its detail design. The basic design of the stator and rotor was conducted. After dividing the designed models into the best cases and the worst cases based on rotor shape parameters, Sensitivity analysis and 3D Voltage-Inductance Map (3D EL-Map) parameters were analyzed. Then, the design direction for the final model was predicted. Based on the prediction, the final model was extracted with Trend analysis. Lastly, the final model was validated with experiments.

Keywords: PMSM, optimal design, rotor design, voltage-inductance map

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Authors: K. Y. Tseng, C. Y. Wu

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This paper employs a heuristic algorithm to solve engineering problems including truss structure optimization and optimal chiller loading (OCL) problems. Two different type algorithms, real-valued differential evolution (DE) and modified binary differential evolution (MBDE), are successfully integrated and then can obtain better performance in solving engineering problems. In order to demonstrate the performance of the proposed algorithm, this study adopts each one testing case of truss structure optimization and OCL problems to compare the results of other heuristic optimization methods. The result indicates that the proposed algorithm can obtain similar or better solution in comparing with previous studies.

Keywords: differential evolution, Truss structure optimization, optimal chiller loading, modified binary differential evolution

Procedia PDF Downloads 164

Authors: Alvaro Javier Ortega

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A large number of companies face the challenge of matching highly-skilled professionals to high-end positions by human resource deployment professionals. However, when the professional list and tasks to be matched are larger than a few dozens, this process result is far from optimal and takes a long time to be made. Therefore, an automated assignment algorithm for this workforce management problem is needed. The majority of companies are divided into several sectors or departments, where trained employees with different experience levels deal with a large number of tasks daily. Also, the execution order of all tasks is of mater consequence, due to some of these tasks just can be run it if the result of another task is provided. Thus, a wrong execution order leads to large waiting times between consecutive tasks. The desired goal is, therefore, creating accurate matches and a near-optimal execution order that maximizes the number of tasks performed and minimizes the idle time of the expensive skilled employees. The problem described before can be model as a mixed-integer non-linear programming (MINLP) as it will be shown in detail through this paper. A large number of MINLP algorithms have been proposed in the literature. Here, genetic algorithm solutions are considered and a comparison between two different mutation approaches is presented. The simulated results considering different complexity levels of assignment decisions show the appropriateness of the proposed model.

Keywords: employees, genetic algorithm, industry management, workforce

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Authors: Pahirangan Sivapatham, , Esser Barbara

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The impacts of the asphalt mix design, the properties of aggregates and quality differences in the same type of bitumen, as well as the dosage rate of reclaimed asphalt on the relevant material parameter of the analytical pavement design method are not known. Due to that, in this study, the influence of the above mentioned characteristics on relevant material parameters has been determined and analyzed by means of the analytical pavement calculations method. Therefore, material parameters for several asphalt mixes for asphalt wearing course, asphalt binder course and asphalt base course have been determined. Thereby several bitumens of the same type from different producer’s have been used. In addition, asphalt base course materials with three different dosages of reclaimed asphalt have been produced and tested. As material parameter according to the German analytical pavement design guide(RDO Asphalt), the stiffness’s at different temperatures and fatigue behavior have been determined. The findings of asphalt base course materials produced with several pen graded bitumen from different producers and different dosages of reclaimed asphalt indicate the distinct impact on fatigue behaviors and mechanical properties. The calculated test results of the analytical pavement design method show significant differences in the lifetimes. The pavement design calculation is to carry out by means of the actual material parameter. The calculated lifetime of the asphalt base course materials differentiates by the factor 3.2. The determining test results of bitumen characteristics meet the requirement according to the German Standards. But, further investigations of bitumen in different aging conditions show significant differences in their quality. The fatigue behavior and stiffness of asphalt pavement improves with increasing dosage of reclaimed asphalt. Furthermore, the type of aggregates used shows no significant influences.

Keywords: reclaimed asphalt pavement, quality differences in the bitumen, life time calculation, Asphalt mix with RAP

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Authors: Hyunchul Ahn, William X. S. Wong

Abstract:

Corporate credit rating prediction is one of the most important topics, which has been studied by researchers in the last decade. Over the last decade, researchers are pushing the limit to enhance the exactness of the corporate credit rating prediction model by applying several data-driven tools including statistical and artificial intelligence methods. Among them, multiclass support vector machine (MSVM) has been widely applied due to its good predictability. However, heuristics, for example, parameters of a kernel function, appropriate feature and instance subset, has become the main reason for the critics on MSVM, as they have dictate the MSVM architectural variables. This study presents a hybrid MSVM model that is intended to optimize all the parameter such as feature selection, instance selection, and kernel parameter. Our model adopts genetic algorithm (GA) to simultaneously optimize multiple heterogeneous design factors of MSVM.

Keywords: corporate credit rating prediction, Feature selection, genetic algorithms, instance selection, multiclass support vector machines

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Authors: Tao Yang, Yongli Zhao

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Low-temperature cracking is one of the major challenges for asphalt pavement in the cold region. Fracture energy could determine from various test methods, which is a commonly used parameter to evaluate the low-temperature cracking resistance of asphalt mixture. However, the feasibility of evaluating the low-temperature cracking performance of asphalt mixture using fracture energy is not investigated comprehensively. This paper aims to verify whether fracture energy is an appropriate parameter to evaluate the low-temperature cracking performance. To achieve this goal, this paper compared the test results of thermal stress restrained specimen test (TSRST) and semi-circular bending test (SCB) of asphalt mixture with different types of aggregate, TSRST and indirect tensile test (IDT) of asphalt mixture with different additives, and single-edge notched beam test (SENB) and TSRST of asphalt mixture with different asphalt. Finally, the correlation between in-suit cracking performance and fracture energy was surveyed. The experimental results showed the evaluation result of critical cracking temperature and fracture energy are not always consistent; the in-suit cracking performance is also not correlated well with fracture energy. These results indicated that it is not feasible to evaluate low-temperature performance by fracture energy. Then, the composition of fracture energy of TSRST, SCB, disk-shaped compact tension test (DCT), three-point bending test (3PB) and IDT was analyzed. The result showed: the area of thermal stress versus temperature curve is the multiple of fracture energy and could be used to represent fracture energy of TSRST, as the multiple is nearly equal among different asphalt mixtures for a specific specimen; the fracture energy, determined from TSRST, SCB, DCT, 3PB, SENB and IDT, is mainly the surface energy that forms the fracture face; fracture energy is inappropriate to evaluate the low-temperature cracking performance of asphalt mixture, as the relaxation/viscous performance is not considered; if the fracture energy was used, it is recommended to combine this parameter with an index characterizing the relaxation or creep performance of asphalt mixture.

Keywords: asphalt pavement, cold region, critical cracking temperature, fracture energy, low-temperature cracking

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Authors: Y. Kalachyova, O. Lyutakov, V. Svorcik

Abstract:

One of the most significant obstacles for the application of surface enhanced Raman spectroscopy (SERS) is the poor reproducibility of SERS active substrates: SERS intensity can be varied from one substrate to another and moreover along the one substrate surface. High enhancement of the near-field intensity is the key factor for ultrasensitive SERS realization. SERS substrate can be prepared through introduction of highly ordered metal array, where light focusing is achieved through excitation of surface plasmon-polaritons (SPPs). In this work, we report the preparation of silver nanostructures with plasmon absorption peaks tuned by the metal arrangement. Excimer laser modification of poly(methyl methacrylate) followed by silver evaporation is proposed as an effective way for the creation of reproducible and effective surface plasmon-polaritons (SPP)-based SERS substrate. Theoretical and experimental studies were performed to optimize structure parameter for effective SPP excitation. It was found that the narrow range of grating periodicity and metal thickness exist, where SPPs can be most efficiently excited. In spite of the fact, that SERS response was almost always achieved, the enhancement factor was found to vary more with the effectivity of SPP excitation. When the real structure parameters were set to optimal for SPP excitation, a SERS enhancement factor was achieved up to four times. Theoretical and experimental investigation of SPP excitation on the two-dimensional periodical silver array was performed with the aim to make SERS response as high as possible.

Keywords: grating, nanostructures, plasmon-polaritons, SERS

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Authors: Oluwaseun Simon Adekanle, M'hammed Guisser, Elhassane Abdelmounim, Mohamed Aboulfatah

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In this paper, modeling and control of a grid connected 660KW Doubly-Fed Induction Generator wind turbine is presented. Stator flux orientation is used to realize active-reactive power decoupling to enable independent control of active and reactive power. The recursive Integral Backstepping technique is used to control generator speed to its optimum value and to obtain unity power factor. The controller is combined with High Gain Observer to estimate the mechanical torque of the machine. The most important advantage of this combination of High Gain Observer and the Integral Backstepping controller is the annulation of static error that may occur due to incertitude between the actual value of a parameter and its estimated value by the controller. Simulation results under Matlab/Simulink show the robustness of this control technique in presence of parameter variation.

Keywords: doubly-fed induction generator, field orientation control, high gain observer, integral backstepping control

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Authors: Gaoguang Yang, Shan Fu

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Human-machine interface (HMI) intermediate system information to human operators to facilitate human ability to manage and control the system. Well-designed HMI would enhance human ability. An evaluation must be performed to confirm that the designed HMI would enhance but not degrade human ability. However, the prevalent HMI evaluation techniques have difficulties in more thoroughly and accurately evaluating the suitability and fitness of a given HMI for the wide variety of uncertainty contained in both the existing HMI evaluation techniques and the large number of task scenarios. The first limitation should be attributed to the subjective and qualitative analysis characteristics of these evaluation methods, and the second one should be attributed to the cost balance. This study aims to explore the connection between objective HMI characters and ergonomic safety and step forward toward solving these limitations with objective, characterized HMI parameters. A simulation experiment was performed with the time needed for human operators to recognize the HMI information as characterized HMI parameter, and the result showed a strong correlation between the parameter and ergonomic safety level.

Keywords: Human-Machine Interface (HMI), evaluation, objective, characterization, simulation

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Authors: Shryas Bhurat, Aryan Vashistha, Sampreet Dinakar Nayak, Ayush Gupta

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As the technology is advancing more towards high computational resources, there is a paradigm shift in the usage of these resources to optimize the design process. This paper discusses the usage of ‘Generative Design using Artificial Intelligence’ to build better models that adapt the operations like selection, mutation, and crossover to generate results. The human mind thinks of the simplest approach while designing an object, but the intelligence learns from the past & designs the complex optimized CAD Models. Generative Design takes the boundary conditions and comes up with multiple solutions with iterations to come up with a sturdy design with the most optimal parameter that is given, saving huge amounts of time & resources. The new production techniques that are at our disposal allow us to use additive manufacturing, 3D printing, and other innovative manufacturing techniques to save resources and design artistically engineered CAD Models. Also, this paper discusses the Genetic Algorithm, the Non-Domination technique to choose the right results using biomimicry that has evolved for current habitation for millions of years. The computer uses parametric models to generate newer models using an iterative approach & uses cloud computing to store these iterative designs. The later part of the paper compares the topology optimization technology with Generative Design that is previously being used to generate CAD Models. Finally, this paper shows the performance of algorithms and how these algorithms help in designing resource-efficient models.

Keywords: genetic algorithm, bio mimicry, generative modeling, non-dominant techniques

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Authors: Hameed Rukayat Opeyemi, Pericles Pilidis, Pagone Emanuele

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An infinitesimal increase in performance can have immense reduction in operating and capital expenses in a power generation system. Therefore, constant studies are being carried out to improve both conventional and novel power cycles. Globally, power producers are constantly researching on ways to minimize emission and to collectively downsize the total cost rate of power plants. A substantial spurt of developmental technologies of low carbon cycles have been suggested and studied, however they all have their limitations and financial implication. In the area of carbon abatement in power plants, three major objectives conflict: The cost rate of the plant, Power output and Environmental impact. Since, an increase in one of this parameter directly affects the other. This poses a multi-objective problem. It is paramount to be able to discern the point where improving one objective affects the other. Hence, the need for a Pareto-based optimization algorithm. Pareto-based optimization algorithm helps to find those points where improving one objective influences another objective negatively and stops there. The application of Pareto-based optimization algorithm helps the user/operator/designer make an informed decision. This paper sheds more light on areas that multi-objective optimization has been applied in carbon abatement technologies in the last five years, developments and prospects.

Keywords: gas turbine, low carbon technology, pareto optimal, multi-objective optimization

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Authors: Juan Manuel Sanchez-Cartas, Gonzalo Leon

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A price competition algorithm for ABMs based on game theory principles is proposed to deal with the simulation of theoretical market models. The algorithm is applied to the classical Hotelling’s model and to a two-sided market model to show it leads to the optimal behavior predicted by theoretical models. However, when theoretical models fail to predict the equilibrium, the algorithm is capable of reaching a feasible outcome. Results highlight that the algorithm can be implemented in other simulation models to guarantee rational users and endogenous optimal behaviors. Also, it can be applied as a tool of verification given that is theoretically based.

Keywords: agent-based models, algorithmic game theory, multi-sided markets, price optimization

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Authors: Manatchanok Tantiphiphatthana, Lin Peng, Rujira Jitrwung, Kunio Yoshikawa

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Hydrothermal liquefaction (HTL) is a technique for obtaining clean biofuel from biomass in the presence of heat and pressure in an aqueous medium which leads to a decomposition of this biomass to the formation of various products. A role of operating conditions is essential for the bio-oil and other products’ yield and also quality of the products. The effects of these parameters were investigated in regards to the composition and yield of the products. Chlorellaceae microalgae were tested under different HTL conditions to clarify suitable conditions for extracting bio-oil together with value-added co-products. Firstly, different microalgae loading rates (5-30%) were tested and found that this parameter has not much significant to product yield. Therefore, 10% microalgae loading rate was selected as a proper economical solution for conditioned schedule at 250oC and 30 min-reaction time. Next, a range of temperature (210-290oC) was applied to verify the effects of each parameter by keeping the reaction time constant at 30 min. The results showed no linkage with the increase of the reaction temperature and some reactions occurred that lead to different product yields. Moreover, some nutrients found in the aqueous product are possible to be utilized for nutrient recovery.

Keywords: bio-oil, hydrothermal liquefaction, microalgae, aqueous co-product

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Authors: Ouahiba Taamallah, Tarek Litim

Abstract:

The study deals with the burnishing effect of AISI 52100 steel and parameters influence (Py, i and f on surface integrity. The results show that the optimal effects are closely related to the treatment parameters. With a 92% improvement in roughness, SB can be defined as a finishing operation within the machining range. Due to 85% gain in consolidation rate, this treatment constitutes an efficient process for work-hardening of material. In addition, a statistical study based on regression and Taguchi's design has made it possible to develop mathematical models to predict output responses according to the studied burnishing parameters. Response Surface Methodology RSM showed a simultaneous influence of the burnishing parameters and to observe the optimal parameters of the treatment. ANOVA Analysis of results led to validate the prediction model with a determination coefficient R2=94.60% and R2=93.41% for surface roughness and micro-hardness, respectively. Furthermore, a multi-objective optimization allowed to identify a regime characterized by P=20 Kgf, i=5 passes and f=0.08 mm.rev-1, which favors minimum surface roughness and a maximum of micro-hardness. The result was validated by a composite desirability D_i=1 for both surface roughness and microhardness, respectively. Applying optimal parameters, burnishing showed its beneficial effects in fatigue resistance, especially for imposed loading in the low cycle fatigue of the material where the lifespan increased by 90%.

Keywords: AISI 52100 steel, burnishing, Taguchi, fatigue

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Authors: Amanpreet Kaur

Abstract:

Wavelet transform (WT) is a powerful statistical tool used in applied mathematics for signal and image processing. The different mother, wavelet basis function, has been compared to select the optimal wavelet function that represents the electromyogram signal characteristics of upper limb amputees. Four different EMG electrode has placed on different location of shoulder muscles. Twenty one wavelet functions from different wavelet families were investigated. These functions included Daubechies (db1-db10), Symlets (sym1-sym5), Coiflets (coif1-coif5) and Discrete Meyer. Using mean square error value, the significance of the mother wavelet functions has been determined for teres, pectorals, and infraspinatus around shoulder muscles. The results show that the best mother wavelet is the db3 from the Daubechies family for efficient classification of the signal.

Keywords: Daubechies, upper limb amputation, shoulder muscles, Symlets, Coiflets

Procedia PDF Downloads 232