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In order to effectively extract the feature points of mobile network animation images and accurately reflect the main content of the video, an optimization method to extract the feature points of key frame images of mobile network animation is proposed. Firstly, the key frames are selected according to the content change degree of the animation video. The scale invariant feature transformation algorithm is used to describe the feature points of the key frame image of the animation video. The local feature points of the image are estimated by the constraint optimization method to realize the optimization extraction of the feature points of the key frame image of the mobile network animation. The efficiency of feature points extraction is analyzed from the number and effectiveness of feature points extraction, time-consuming and similarity invariance of feature points. The experimental results show that the proposed method has excellent adaptability, and can effectively extract feature points of mobile network animation image.

Since nuclear wastes are the most important wastes in terms of health and the environment, they are evaluated differently within nuclear reactors as well as in terms of their use in medical and industrial applications. In some cases, emergency intervention is necessary due to the amount of radioactivity or the physical and/or chemical conditions. The purpose of this study is to investigate the adsorption properties of nano Ca3Y2B4O12 (CYBO) material synthesized by the sol-gel combustion method for the adsorption of Thorium (IV) from an aqueous medium. We tested how pH (3–8), the concentration of Th (IV) (25–125 mg/L), amount of adsorbent value (0.005–0.08 g) and temperature (20–60 °C), affect adsorption efficiency. The best possible combinations of these parameters were examined by Response Surface Methodology (RSM) and Artificial Neural Network (ANN). R2 values for RSM and ANN were 0.9964 and 0.9666, respectively. According to the models, the adsorption capacity under the optimum conditions determined for the RSM and ANN model was found to be 134.62 mg/g and 125.12 mg/g, respectively. © 2022 Elsevier Ltd, Türkiye Atom Enerjisi Kurumu, TAEK; Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK: 1001-120M235, The authors acknowledge grants from the Scientific and Technological Research Council of Turkey (TUBITAK, project number: 1001-120M235 ). All radionuclide adsorption experiments performed in the study were carried out in Ege University Institute of Nuclear Sciences, which is also a partner in the project, accredited by the Turkish Atomic Energy Authority (TAEK)., The authors acknowledge grants from the Scientific and Technological Research Council of Turkey (TUBITAK, project number: 1001-120M235). All radionuclide adsorption experiments performed in the study were carried out in Ege University Institute of Nuclear Sciences, which is also a partner in the project, accredited by the Turkish Atomic Energy Authority (TAEK).

In the current study, the optimization of ectoine production by Nesterenkonia xinjiangensis and purification of ectoine from the bacterial cell extract were performed for the first time. Various carbon sources (glucose, sucrose, maltose, lactose, mannitol, and xylose) and nitrogen sources (ammonium nitrate, ammonium phosphate, ammonium chloride, ammonium oxalate, ammonium sulphate, and ammonium acetate), were used to optimize ectoine production. Subsequently, the effects of salt, pH and, concentrations of carbon and nitrogen source on ectoine production were optimized by response surface methodology (RSM). Ultimately, high pure (over 99%) and yield (98%) of ectoine from bacterial cells extracted was obtained by a single-step process using cation exchange chromatography. This study provides information that higher ectoine production can be achieved from this bacterial isolate by optimizing the factors influencing ectoine production and thus can be used as a new and alternative ectoine producer. © 2023, BAP- FEF.20.001, This study was supported by Agri Ibrahim Cecen University, Scientific Researches Project (Project number: BAP- FEF.20.001).

The output voltage of microbial fuel cells (MFCs) needs to be controlled effectively to improve the operating efficiency of MFC system against the load variations, disturbances, and uncertainties. The output voltage control is purposed to provide the MFC with an appropriate amount of fuel required to maintain the stability of output voltage. In the applications, the MFC output voltage can be requested to track the variable reference voltage or to track the constant output voltage under changing conditions such as load change. For this purpose, two adaptive fuzzy PID (FPID) controllers powered by optimization algorithms were designed in this study to keep the output voltage value of the MFC at the desired values. This study extends the previous works by using optimization algorithms, such as particle swarm optimization (PSO) and gray wolf optimization (GWO) algorithms, to adjust fuzzy logic parameters, which are used for tuning Proportional-Integral-Derivative (PID) controllers. The optimization-based adaptive FPID controllers play an active role in efficient, fast, and robust tracking of the reference voltage value with its adaptive structure. The designed adaptive control algorithms were tested under different cases, which involve the variation of the reference voltage and load conditions. The results show that both of these controllers can effectively control the output voltage of a MFC by regulating the flow rate of the fuel. In addition, the performances of the designed controller strategies according to the results obtained vary according to the reference signal. While PSO-based FPID gives more successful results for positive step changes, GWO-based FPID gives more successful results for negative step changes. These two control strategies show more efficient, robust, successful, and faster performances compared to the traditional PID controller.

In order to maintain a satisfactory performance in the midst of rapid growth of mobile traffic, the mobile network infrastructure needs to be scaled. Thus there has been significant interest in scalability of mobile core networks and a variety of scaling solutions have been proposed that rely on horizontal scaling or vertical scaling. These solutions handle the scaling of the mobile core networks' elements on virtual machines (which normally take at while to create) with the help of customized modules at the cost of increased overheads. Utilizing Amazon Web Services (AWS) embedded features, we present two predictive horizontal auto-scalers for containerized and non-containerized versions of EPC that scales the two versions of the EPC according to their respective CPU utilization. Additionally, we propose an efficient task assignment scheme for AWS that aims to maximize throughput and achieve fairness among competing instances. In particular, we propose two solutions: Relaxed Optimized Solution (ROS) and a Heuristic Approach (HA). Leveraging AWS environment, we implemented and evaluated the two proposed auto-scaling models based on the attachment success rate, latency, CPU usage and RAM usage. Our findings show the superiority of container-based model over VM-based model in terms of resource utilization. The obtained results for the two proposed task assignment solutions demonstrates a significant improvement both in fairness and throughput compared to other existing solutions. 2013 IEEE. Scopus

Concrete structures are exposed to water throughout their service life. Nowadays, fiber-reinforced concrete is the most preferred concrete due to its superior mechanical properties. However, good mechanical properties alone cannot protect concrete against the environmental conditions it may encounter during its service life. In particular, water is an inevitable factor faced by concrete elements. In this study, the permeability properties of concrete produced with different combinations of four different fibers were investigated. Hybrid fiber concrete (HFC) was made using the central composite design method. Steel, glass fiber, synthetic, and polypropylene fibers were hybridized among themselves, and the aggregate and paste phases of concrete were hybridized with electric arc furnace slag aggregate (EAFS) and fly ash (FA), respectively. Ultrasonic pulse rate, rapid chloride permeability (RCP), capillary water absorption capacity, and carbonation depth (CD) of HFC were determined. Statistically significant and nonsignificant parameters for each response were determined. Simultaneous substitution of EAFS and FA reduced the capillarity coefficient and CD of HFC. The RCP of HFC depends mainly on two factors: binder dosage and steel fiber ratio. The most suitable fiber for reducing CD was glass fiber. The estimated RCP, ultrasonic pulse velocity (UPV), and capillary water absorption capacity (CWAC) results were compared with the control sample, and the results were 81%, 114%, and 86% of the control sample.

Contrary to the increase in energy demand throughout the world, fossil fuels, which are the main energy source, is now more difficult to reach because of the pandemic, transport or bilateral relations, etc. These obstacles in obtaining fossil fuels reveal the need to minimize energy wastage by utilizing low-temperature waste heat sources releasing the atmosphere without use. Especially for the industrial fields usage of advanced heat recovery systems is necessary. In addition, it is a remarkable point that the evaluation of low-temperature thermal energy potential will contribute significantly to the reduction of carbon emissions, which is one of the most important goals in developed and developing countries. In this study, Kalina and Organic Rankine cycles, which are the most important low-temperature energy conversion systems in the utilization of low-temperature industrial waste heat with a temperature of 250 ºC and a mass flow of 10 m/s, were examined and compared to each other in terms of energy, exergy efficiency, economic outlook, and environmental effects. During the study, the turbine inlet pressure of the Kalina cycle was selected as 60, 90 and 120 bar, while the turbine inlet pressure of the organic Rankine cycle increased from 10 bar to the critical pressure of the fluid. For all selected pressure levels, the turbine inlet temperature was increased from the saturated vapor temperature of the fluid to 240 ºC. As a result, although the Kalina cycle was found economically better than the organic Rankine cycle (3.93 years), the organic Rankine cycle using n-Pentane showed a better performance than the Kalina cycle in terms of energy, exergy, and CO2 emission reduction. The thermal efficiency, exergy efficiency, payback period, and CO2 emission reduction value of the organic Rankine cycle with n-Pentane were calculated as 25.95%, 71.77%, 4.03 years, and 207.17 kg-CO2/h, respectively.

Shipyards have large departments or facilities. It is essential to make an effective topological layout plan since the initial investment cost of these departments is high. Topological layout is an optimization problem and Genetic Algorithm (GA) is generally used in the literature. The selection of effective genetic algorithm approaches and operators are very important to improve the performance of the optimization. This study investigates an effective solution to the shipyard topological layout using a Quadratic Assignment Problem (QAP) model with classic and elitist GA approaches. Besides, genetic operators that have significant effects on exploitation and exploration capabilities are analyzed. Therefore, 126 experiments were run with 13 different operators. The results obtained from the classic and elitist GA approach were evaluated individually and compared with each other. It was observed that the elitist GA approach has a superior performance compared to the classic GA approach. This study is the most comprehensive and practical study on the performance of the GA for topological layout of the shipyard in the literature.

Microbial fuel cell (MFC) has become a very important biotechnological tool to produce clean energy in recent years. It is very important to adjust the output voltage and power density in order to obtain the desired energy quickly and smoothly at the output of the MFC. In this study, an optimization-based neuro-fuzzy inference controller is proposed for improving voltage tracking performance of the MFC. A double-chambers MFC model including biochemical reactions, Butler-Volmer expressions and mass/charge balances was studied and Particle Swarm Optimization (PSO) and Improved Grey Wolf Optimization (IGWO) algorithms are used to adjust the parameters of the neuro-fuzzy controller. The results show that PSO and IGWO based controllers have efficient performances to follow the reference voltage pattern quickly and robustly against external load changes, distributions and parameter uncertainties. Moreover, it was observed that IGWO was a more stable and robust controller than PSO according to rise time, overshoot and peak time. (c) 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

This letter presents an impressive optimization method for determining the optimal model hyperparameters of a deep neural network (DNN) targeted to model the characteristics of antennas. In this paper we propose an innovative approach of efficient yield analysis for modeling and sizing antennas. It is based on the long short-term memory (LSTM) DNN aiming to forecast the extended frequency responses, where various stochastic methods are applied for determining the optimal hyperparameters while training a DNN. Among the various methods, the one which models the antenna accurately in terms of input scattering parameter, gain, and radiation patterns is the winner. The proposed method is compact and addresses the problem of heavy reliance to the designer experience in determining the hyperparameters. Additionally, forecasting the future frequency responses of the antenna reduces the designers effort substantially in measuring large frequency band; hence, measuring whole frequency band would not be needed. For validating the effectiveness of the proposed method, the fabricated two element antenna array is used for modeling where the results demonstrate that the Thompson sampling (TS) algorithm can determine optimal hyperparameters with minimum error in comparison with other reported stochastic methods leads to predict the future frequency band accurately. IEEE

This paper describes the nitric acid dissolution process of natural minerals such as ten-nantite, chalcopyrite and sphalerite, with the addition of Fe (III) ions and FeS2. These minerals are typical for the ores of the Ural deposits. The effect of temperature, nitric acid concentration, time, additions of Fe (III) ions and FeS2 was studied. The highest dissolution degree of sulfide minerals (more than 90%) was observed at a nitric acid concentration of 6 mol/dm3, an experiment time of 60 min, a temperature of 80 °C, a concentration of Fe (III) ions of 16.5 g/dm3, and an addition of FeS2 to the total mass minerals at 1.2:1 ratio. The most significant factors in the break-down of minerals were the nitric acid concentration, the concentration of Fe (III) ions and the amount of FeS2. Simultaneous addition of Fe (III) ions and FeS2 had the greatest effect on the leaching process. It was also estab-lished that FeS2 can be an alternative catalytic surface for copper sulfide minerals during nitric acid leaching. This helps to reduce the influence of the passivation layer of elemental sulfur due to the galvanic linkage formed between the minerals, which was confirmed by SEM-EDX. © 2022 by the author. Licensee MDPI, Basel, Switzerland. Russian Science Foundation, RSF: 075-03-2021-051/5, 20-79-00317, FEUZ-2021-0017 Funding: This work was funded by the Russian Science Foundation Project No. 20-79-00317. The SEM-EDX, XRF, XRD analysis were funded by State Assignment, grant No. 075-03-2021-051/5 (FEUZ-2021-0017).

In this study, the performance evaluation of different optimization methods in calculating the change of camera movement, in other words the camera position and angle, was made on images taken from different views with the same camera. For this purpose, gradient-based and population-based optimization techniques have been investigated by using the differentiable rendering methods that have been proposed in recent years for the 3D point cloud created over depth information. It was found that while gradient-based methods are more successful on close movements, population-based methods were more successful in distant changes.

5h International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2021 -- 21 October 2021 through 23 October 2021 -- -- 174473 This paper aims to demonstrate the success of a recently developed metaheuristic algorithm, named slime mould algorithm (SMA), for efficient operation of a magnetic levitation system. A real proportional-integral-derivative (PID) plus second-order derivative (PIDD2) controller was utilized to demonstrate the implementation. This paper is also the first report on SMA based real PIDD2 controller tuning for control of a magnetic levitation system. The transient response analysis was performed to demonstrate the promise of the proposed approach. Besides, a comparative performance analysis in terms of transient stability was also performed by using other available and good performing approaches. The results have shown the SMA based real PIDD2 controller to be a better choice for efficient operation of a magnetic levitation system. © 2021 IEEE.

SPE/IATMI Asia Pacific Oil and Gas Conference and Exhibition 2021, APOG 2021 -- 12 October 2021 through 14 October 2021 -- -- 172791, Reservoir engineering constitutes a major part of the studies regarding oil and gas exploration and production. Reservoir engineering has various duties, including conducting experiments, constructing appropriate models, characterization, and forecasting reservoir dynamics. However, traditional engineering approaches started to face challenges as the number of raw field data increases. It pushed the researchers to use more powerful tools for data classification, cleaning and preparing data to be used in models, which enhances a better data evaluation, thus making proper decisions. In addition, simultaneous simulations are sometimes performed, aiming to have optimization and sensitivity analysis during the history matching process. Multi-functional works are required to meet all these deficiencies. Upgrading conventional reservoir engineering approaches with CPUs, or more powerful computers are insufficient since it increases computational cost and is time-consuming. Machine learning techniques have been proposed as the best solution for strong learning capability and computational efficiency. Recently developed algorithms make it possible to handle a very large number of data with high accuracy. The most widely used machine learning approaches are: Artificial Neural Network (ANN), Support Vector Machines and Adaptive Neuro-Fuzzy Inference Systems. In this study, these approaches are introduced by providing their capability and limitations. After that, the study focuses on using machine learning techniques in unconventional reservoir engineering calculations: Reservoir characterization, PVT calculations and optimization of well completion. These processes are repeated until all the values reach to the output layer. Normally, one hidden layer is good enough for most problems and additional hidden layers usually does not improve the model performance, instead, it may create the risk for converging to a local minimum and make the model more complex. The most typical neural network is the forward feed network, often used for data classification. MLP has a learning function that minimizes a global error function, the least square method. It uses back propagation algorithm to update the weights, searching for local minima by performing a gradient descent (Figure 1). The learning rate is usually selected as less than one. © 2021, Society of Petroleum Engineers.

6th International Conference on Computer Science and Engineering, UBMK 2021 -- 15 September 2021 through 17 September 2021 -- -- 176826, Classification problems have an important role in the field of machine learning and data mining. Classification problems are used in different areas such as disease diagnosis, estimation of bank customers, drug studies, sentiment analysis. Many classification algorithms have been developed in the literature and these algorithms have many different parameter inputs. In this study, it is aimed to increase the classification success by using hyperparameter optimization algorithms. K-nearest neighbor, support vector machines, decision tree and gradient boosting classification algorithms were applied to the frequently used 'heart and iris' datasets in the literature. Grid search and random search algorithms, which are hyperparameter optimization algorithms, are applied to these selected classification algorithms. As a result of the experimental studies, it has been observed that the accuracy of all classification algorithms increases when hyperparameter optimization algorithms are applied. The parameter values that give the best results are shown. © 2021 IEEE

Software programs are used for many critical roles. A bug in those can have a devastating cost, possibly leading to the loss of human lives. Such bugs are usually found at the source level (which can be ruled out with source-level verification methods), but they can also be inserted by the compiler unknowingly. CompCert is the first commercially available optimizing compiler with a formal proof of correctness: compiled programs are proven to behave the same as their source programs. However, because of the challenges involved in proving compiler optimizations, CompCert only has a limited number of them. As such, CompCert usually generates low-performance code compared to classical compilers such as GCC. While this may not significantly impact out-of-order architectures such as x86, on in-order architectures, particularly on VLIW processors, the slowness is significant (code running half as fast as GCC -O2). On VLIW processors, the intra-level parallelism is explicit and specified in the assembly code through “bundles” of instructions: the compiler must bundlize instructions to achieve good performance.In this thesis, we identify, investigate, implement and formally verify several classical optimizations missing in CompCert. We start by introducing a formal model for VLIW bundles executing on an interlocked core and generate those bundles through a postpass (after register allocation) scheduling. Then, we introduce a prepass (before register allocation) superblock scheduling, implementing static branch prediction and tail-duplication along the way. Finally, we further increase the performance of our generated code by implementing loop unrolling, loop rotation and loop peeling – the latter being used for Loop-Invariant Code Motion. These transformations are verified by translation validation, some of them with hash-consing to achieve reasonable compilation time.We evaluate each introduced optimization on benchmarks, including Polybench and TACleBench, on the KV3 VLIW core, ARM Cortex A53, and RISC-V “Rocket” core. Thanks to this work, our version of CompCert is now only 16% slower (respectively 12% slower and 30% slower) thanGCC -O2 on the KV3 (respectively ARM and RISC-V), instead of 50% (respectively 38% and 45%).; On utilise des logiciels pour différents rôles, parfois critiques. La présence d’un bogue dans un logiciel peut avoir un coût dévastateur, pouvant aller jusqu’à la perte de vies humaines dans les cas les plus extrêmes. Les bogues viennent en général du code source (ceux-là peuvent être détectés par des méthodes de vérification formelle sur le langage source), mais ils peuvent être également générés par le compilateur. CompCert est le premier compilateur optimisant commercialisé comportant une preuve formelle de correction : il est formellement prouvé que les programmes compilés avec cet outil ont le même comportement que leurs programmes sources originels. Cependant, comme prouver les optimisations d’un compilateur relève d’un défi considérable, CompCert n’en a qu’un nombre limité. Ainsi, en général CompCert génère du code moins performant comparé à d’autres compilateurs classiques tels que GCC. Bien que cela n’impacte peu les architectures à exécution dans le désordre telles que x86, sur des architectures à exécution dans l’ordre, et en particulier sur des processeurs VLIW, le ralentissement estimportant (le code résultant est deux fois plus lent que GCC -O2). Sur un processeur VLIW , le parallélisme d’instructions est explicite et spécifié dans le code assembleur par des bundles (paquets) d’instructions : le compilateur doit paquétiser les instructions pour obtenir une bonne performance.Le but de cette thèse est d’identifier, d’enquêter, d’implémenter et de vérifier formellement plusieurs optimisations classiques manquantes dans CompCert. Tout d’abord, j’introduis un model formel d’exécution de bundles VLIW sur processeur à pipeline imbriqué. Nous générons ces bundles à partir d’un réordonnancement en postpass (après allocation de registres). Ensuite, j’introduis un réordonnancement de superblocks en prepass (avant allocation de registres). Ce travail implique aussi l’introduction d’une prédiction statique de branchements, et une duplication de queue. Enfin, pour continuer d’augmenter la performance du code généré, j’introduis du déroulage, rotation, et pelage de boucles – ce dernier étant utilisé dans du déplacement d’invariants de boucles. Ces transformations sont vérifiées par validation de translation, et certaines utilisent du partage maximal afin d’obtenir des temps de compilation raisonnables.Nous avons évalué chacune de ces optimisations sur des tests de performance, dont Polybench et TACLeBench, sur le coeur VLIW KV3, l’ARM Cortex A53, et le coeur RISC-V “Rocket”. Grâce à nos travaux, notre version de CompCert est maintenant seulement 16% plus lente (respectivement 12% et 30% plus lente) que GCC -O2 sur KV3 (respectivement ARM et RISC-V), au lieu de 50% (respectivement 38% et 45%).

The main contribution of this thesis is to investigate the behavior of analog quantum computers as commercialized by D-Wave when confronted to instances of the maximum cardinality matching problem which is specifically designed to be hard to solve by means of simulated annealing. We benchmark a D-Wave “Washington” (2X) with 1098 operational qubits on various sizes of such instances and observe that for all but the most trivially small of these it fails to obtain an optimal solution. Thus, our results suggest that quantum annealing, at least as implemented in a D-Wave device, falls in the same pitfalls as simulated annealing and hence provides additional evidences suggesting that there exist polynomial-time problems that such a machine cannot solve efficiently to optimality. Additionally, we investigate the extent to which the qubits interconnection topologies explains these latter experimental results., La principale contribution de cette thèse est d'étudier expérimentalement le comportement des ordinateurs quantiques analogiques tels que ceux commercialisés par D-Wave lorsqu'ils sont confrontés à des cas de problèmes de couplage biparti de cardinalité maximale spécifiquement conçus pour être difficiles à résoudre au moyen d'un recuit simulé. Nous comparons un "Washington" (2X) de D-Wave avec 1098 qubits utilisables sur différentes tailles d'instances et nous observons que pour tous ces cas, sauf les plus triviaux, la machine ne parvient pas à obtenir une solution optimale. Ainsi, nos résultats suggèrent que le recuit quantique, du moins tel qu'il est mis en œuvre dans un dispositif D-Wave, tombe dans les mêmes pièges que le recuit simulé et fournit donc des preuves supplémentaires suggérant qu'il existe des problèmes polynomiaux qu'une telle machine ne peut pas résoudre efficacement pour atteindre l'optimalité. En outre, nous étudions dans quelle mesure les topologies d'interconnexion des qubits expliquent ces derniers résultats expérimentaux.

Stochastic methods attempt to solve problems that cannot be solved by deterministic methods with reasonable time complexity. Optimization algorithms benefit from stochastic methods; however, they do not guarantee to obtain the optimal solution. Many optimization algorithms have been proposed for solving problems with continuous nature; nevertheless, they are unable to solve discrete or binary problems. Adaptation and use of continuous optimization algorithms for solving discrete problems have gained growing popularity in recent decades. In this paper, the binary version of a recently proposed optimization algorithm, Battle Royale Optimization, which we named BinBRO, has been proposed. The proposed algorithm has been applied to two benchmark datasets: the uncapacitated facility location problem, and the maximum-cut graph problem, and has been compared with 6 other binary optimization algorithms, namely, Particle Swarm Optimization, different versions of Genetic Algorithm, and different versions of Artificial Bee Colony algorithm. The BinBRO-based algorithms could rank first among those algorithms when applying on all benchmark datasets of both problems, UFLP and Max-Cut. Publisher's Version WOS:000787281000010

For atomic frequency standards in which fluctuations of the local oscillator (LO) frequency are the dominant noise source, we examine the role of the servo algorithm that predicts and corrects these frequency fluctuations. We derive the optimal linear prediction algorithm, showing how to measure the relevant spectral properties of the noise and optimise servo parameters while the standard is running, using only the atomic error signal. We find that, for realistic LO noise spectra, a conventional integrating servo with a properly chosen gain performs nearly as well as the optimal linear predictor. Using simple analytical models and numerical simulations, we establish optimum probe times as a function of clock atom number and of the dominant noise type in the local oscillator. We calculate the resulting LO-dependent scaling of achievable clock stability with atom number for product states as well as for maximally-correlated states., 16 pages, 7 figures. Added discussion of servo's spectral response and of phase-stabilization vs frequency stabilization. Results unchanged from first version

WOS:000543104900020 This paper investigates the factors affecting the optimum insulation thickness and its pay-back period, such as heating and cooling energy requirements of building, lifetime, present worth factor, costs of insulation material and installation, costs of energy sources for heating and cooling, heating and cooling system efficiencies, and solar radiation. For this purpose, by considering two cities characterizing the hot and cold climatic conditions, the optimum insulation thickness and its payback period have been calculated and a detailed parametric analysis has been carried out. To achieve practical results, the ranges of the parameters considered in the study include the values typically reported in the literature. The variations in the optimum insulation thickness and the pay-back period with all parameters are presented in graphical form. Finally, order of importance and contribution ratios of the examined parameters on the optimum insulation thickness are determined with the help of Taguchi method. It is found that heating degree-days is the most efficient parameter on the optimum insulation thickness with an impact ratio of 27.33% of the total effect while the least efficient parameter is the efficiency of heating system with an impact ratio of 3.21%.

This study introduces an improved ANFIS based MPPT method to maximize the power extraction capability of the FC-connected system. The proposed method is tested in a stand-alone system that consists of an FC in the power rating of 1.9 kW, a boost dc-dc converter, local consumer load, and a processor unit. The energy transfer between FC and load is handled through the adjustment of a duty cycle of the dc-dc converter. In this context, the output voltage of FC is controlled by the duty cycle to track the MPP. The proposed method called GA-ANFIS computes optimum reference voltages to control the FC output voltage optimally. The GA-ANFIS uses a reduced-size training dataset extracted by GA to train the ANFIS in comparison with conventional ANFIS. Unlike the existing methods, the proposed method tracks the MPP by merely monitoring FC voltage during operation. Besides, it performs precise MPP tracking by considering pressure & temperature variations. Thus, the proposed method provides reduced computational load owing to its current features. The performance of the proposed method is compared with the traditional methods like ANFIS and PI. The power extraction ratings and efficiency values validate the viability and effectiveness of the proposed method (>98%).

This study explores the use of carbon nanotubes (CNTs) and nanoclays in cement mortars. The paper presents modelling and optimisation of the compressive and flexural strengths of cement mortars containing CNTs and the compressive, tensile and flexural strengths of cement mortars including nanoclays. The CNT ratio, CNT aspect ratio and functionalisation effects are optimised, as are the montmorillonite nanoclay ratio and temperature effect. Mechanical strengths are modelled using two cascade feed-forward neural networks (NNs), designated CSNN-CNTs and CSNN-CLY for CNTs and nanoclays, respectively. The trained NN CNT and nanoclay models both successfully replicated experiments with significant accuracy. Inputs to the NN models were different mix combinations, optimised using a genetic algorithm to achieve optimal strength results. The optimisation process for mortars with CNTs revealed increases of 20·96% and 54·27% for compressive and flexural strengths, respectively. Similarly, optimisation of the mortar with nanoclays achieved increases of 63·7%, 199% and 133% for compressive, tensile and flexural strengths, respectively. These results demonstrate that the NN integrated genetic algorithm based optimisation is an effective way of determining the best recipe for CNT and nanoclay mortars for optimum strength.

An active aerodynamic feeding system developed at the IFA offers a large potential regarding output rate, reliability and neutrality towards part geometries. In this paper, the procedure of a genetic algorithm's into the feeding system's control is shown. The genetic algorithm automatically identifies optimal values for the feeding system's parameters which need to be adjusted when setting up for new workpieces. The general functioning of the automatic parameter identification is confirmed during tests on the convergence behaviour of the genetic algorithm. Thereby, a trade-off between the adjustment time of the feeding system and the solution quality is revealed. © 2016 The Authors. DFG/NY 4/51-1

This paper presents a layup optimisation algorithm for composite marine propellers, including the hygrothermal effects, using Non-Uniform Rational B-Splines (NURBS) based FEM coupled with real-coded Genetic Algorithm (GA). The use of Iso-Geometric Analysis (IGA) enables accurate representation of complex marine propeller blades, coupled with GA for both continuous and mixed integer ply angle optimisation. The optimisation scheme was further investigated multi-objective, multi-material and multiple layer thickness optimisation scenarios. Furthermore, the IGA-FEM solver was constructed based on constitutive equations that take into account the hygrothermal effects that must be addressed to enable the optimisation of non-symmetric layups. Such non-symmetric layup patterns are proposed to be used to create extension-twist coupling to gain further control in layup optimisation. The optimisation technique is presented here as a method to widen the efficiency envelope of marine propellers. However, the same approach can potentially be adopted to cater many other practical applications such as composite wind turbine blades, aircraft propellers and other general composite ply angle optimisation scenarios. The paper discusses the proposed framework for optimisation of marine propeller, numerical tools used in the process and results under different conditions. � 2014 Elsevier B.V.

ron and steel plants producing steel via the blast furnace-basic oxygen furnace (BF-BOF) route constitute among the largest single point CO2 emitters within the European Union (EU). As the iron ore reduction process in the blast furnace is fully dependent on carbon mainly supplied by coal and coke, bioenergy is the only renewable that presents a possibility for their partial substitution. Using the BeWhere model, this work optimised the mobilization and use of biomass resources within the EU in order to identify the opportunities that bioenergy can bring to the 30 operating BF-BOF plants. The results demonstrate competition for the available biomass resources within existing industries and economically unappealing prices of the bio-based fuels. A carbon dioxide price of 60 € t−1 is required to substitute 20% of the CO2 emissions from the fossil fuels use, while a price of 140 € t−1 is needed to reach the maximum potential of 42%. The possibility to use organic wastes to produce hydrochar would not enhance the maximum emission reduction potential, but it would broaden the available feedstock during the low levels of substitution. The scope for bioenergy integration is different for each plant and so consideration of its deployment should be treated individually. Therefore, the EU-ETS (Emission Trading System) may not be the best policy tool for bioenergy as an emission reduction strategy for the iron and steel industry, as it does not differentiate between the opportunities across the different steel plants and creates additional costs for the already struggling European steel industry. Validerad;2018;Nivå 2;2018-05-22 (andbra)

This paper considers multi-stage train formation with mixed usage tracks at a marshalling yard without departure yard. A novel integer programming model for scheduling shunting tasks as well as allocating arrival yard tracks and classification bowl tracks is presented. By taking a comprehensive view of the marshalling yard operations, more effective schedules can be found, and a variety of characteristics can be optimised, including shunting work effort, number or cost of tracks, and shunting task start times. Two different objective functions are evaluated: minimising work effort in terms of wagon pull-backs and minimising track costs. A procedure for finding a hot-start solution with few wagon pull-backs is also presented. The proposed model is tested on real data from Sävenäs marshalling yard in Sweden. The results show that the method is able to return an optimal schedule for a planning period of 4 days if the hot-start solution is optimal or the remaining problem is tractable for the heuristics in CPLEX.

Automated storage and retrieval systems (AS/RS) are playing an important role in modern manufacturing systems such as flexible manufacturing systems (FMS). The integration of AS I RS systems and FMS is very complex owing to the complexity of the individual elements. Stochastic coloured petri nets (SCPN) can be used to model, simulate and analyse such a system efficiently and realistically. In this paper, SCPN based simulation, aided by the Taguchi method of design of experiments, has been used for analysing the influence of important factors on makespan, unproductive travel time, and the mean flow time, and is supported by an example. ? 1996 Springer-Verlag London Limited.

This paper employs exergy analysis to derive a general equation for the exergy efficiency of flat plate collectors and thus optimize its design and operation. Exergy analysis of a flat-plate solar collector is a more effective method of finding the optimum relationship between flow rate and the collector area. The fixed parameters in this optimization are the collector inlet temperature, available solar radiation, the collector transmittance-absorptance product and the ambient temperature. The collector heat loss coefficient is estimated according to the collector plate temperature, wind convection loss, number of glass covers, collector inclination, ambient temperature and emittance of collector plate and glass cover. In order to find the optimum value of this multivariable problem genetic algorithms are used which are based on the principles of genetics and survival of the fittest. The optimization parameter is exergy efficiency and the objective is to maximize this parameter. Genetic algorithms proved suitable and very quick in obtaining the required results. These results prove that the exergy efficiency of a flat-plate solar collector is maximized for small distances between the riser tubes and for very small diameter of these tubes. By using a more practical distance of 10 centimetres between the tubes, and excluding this parameter from the optimization procedure, very small differences are observed in maximum exergy efficiency and if the cost of the materials is accounted this is a more cost-effective solution. Other findings are that the exergy efficiency increases considerably at higher solar radiation and that the transmittance absorptance product affects to a great extent the exergy efficiency.

Cataloged from PDF version of article. An optimal estimation framework is considered in the presence of cost-constrained measurements. The aim is to maximise the average Fisher information under a constraint on the total cost of measurement devices. An optimisation problem is formulated to calculate the optimal costs of measurement devices that maximise the average Fisher information for arbitrary observation and measurement statistics. In addition, a closed-form expression is obtained in the case of Gaussian observations and measurement noise. Numerical examples are presented to explain the results.

Designs for improved railroad track structure are needed to ensure safety, reliability and profitability as the railroads strive to compete with other means of transport. The large number of precast sleepers produced in the country calls for an optimal design inthe interest of economy. Use of optimisation methods can provide a scientific approach toarrive at a leastcost design. In this paper, optimal design of broad gauge prestressed concrete sleepers for tangent tracks is presented considering the current design standards by Research Designs and Standards Organisation (RDSO). Formulations for optimal design for the conventional 22.9 t and proposed 25 t axle loads are presented along with identification of design variables, objective function and constraints. Optimisation was carriedout in two phases: first, by keeping the geometry constant (for a constant mould size, as presently used) and secondly, also by varying the sleeper cross-section geometry. Among the non-traditional optimisation techniques, genetic algorithm for a constrained optimisation problem using adaptive penalty parameter and adaptive genetic algorithm was used. A MATLAB program has been developed based on the formulations. The optimal solutions are summarised here.

In mobile cellular networks the handover (HO) algorithm is responsible for determining when calls of users that are moving from one cell to another are handed over from the former to the latter. The admission control (AC) algorithm, which is the algorithm that decides whether new (fresh or HO) calls that enter a cell are allowed to the cell or not, often tries to facilitate HO by prioritising HO calls in favour of fresh calls. In this way, a good quality of service (QoS) for calls that are already admitted to the network is pursued. In this paper, the effect of self-optimisation of AC parameters on the HO performance in a long term evolution (LTE) network is studied, both with and without the self-optimisation of HO parameters. Simulation results show that the AC parameter optimisation algorithm considerably improves the HO performance by reducing the amount of calls that are dropped prior to or during HO. © 2011 IEEE.

In mobile cellular networks the handover (HO) algorithm is responsible for determining when calls of users that are moving from one cell to another are handed over from the former to the latter. The admission control (AC) algorithm, which is the algorithm that decides whether new (fresh or HO) calls that enter a cell are allowed to the cell or not, often tries to facilitate HO by prioritising HO calls in favour of fresh calls. In this way, a good quality of service (QoS) for calls that are already admitted to the network is pursued. In this paper, the effect of self-optimisation of AC parameters on the HO performance in a long term evolution (LTE) network is studied, both with and without the self-optimisation of HO parameters. Simulation results show that the AC parameter optimisation algorithm considerably improves the HO performance by reducing the amount of calls that are dropped prior to or during HO. © 2011 IEEE.

This paper presents a framework for re-designing a failed automotive component subjected to cyclic loading. In the automotive industry, some parts fail before completing their design life. These failed components have to be re-designed using modern optimisation tools. In this paper, a failed clutch fork was completely re-designed using topology and the shape optimisation approach. Stress-life fatigue analysis was conducted to correlate the crack location between the failed component and the simulation model. A new design proposal was determined with the topology optimisation approach, and then design optimisation by response surface methodology was effectively used to improve the new clutch fork design. The mass reduction obtained was 24%. A maximum stress reduction of 9% was achieved, and the rigidity was improved up to 37% in comparison to the original clutch fork.

The simultaneous heat exchanger network (HEN) synthesis problem is generally formulated as a mixed integer non-linear programming (MINLP) problem by using superstructures, which embed several possible stream matches and various alternative HEN designs. The superstructures proposed by Yee et al. [1] and by Floudas et al. [2] are used for this purpose. In the present work, the graph theoretic principles used by Shivakumar and Narasimhan [3] to develop an efficient nonlinear program (NLP) formulation for Yee superstructure has been extended to the Floudas superstructure. A comparison of the NLP formulations based on the two superstructures, in terms of efficiency, scalability, convergence and quality of solution has been carried out using several test problems available in the literature. It has been shown that the Floudas superstructure is superior in terms of these performance metrics, and appears to be the best suited for optimisation based HEN synthesis. � 2010 Indian Institute of Chemical Engineers.

Viscosity modifying admixtures (VMAs) are high molecular weight, water soluble organic polymers that are used to stabilise the rheological properties and consistency of self-compacting concrete (SCC). Most studies on VMA focus on the influence of different types and dosages of VMA on the rheological properties and the compatibility issues between superplasticiser and VMA. To obtain the desired rheological properties and to reduce the cost, optimisation of the VMA dosage is essential. In the present study, a simple method was developed for optimising the VMA dosage for the cementitious pastes of different water to powder ratio. The principle of this method was based on Stokes' law. Rheological properties of SCC pastes with VMA were studied using a viscometer. The studies were also extended to concrete for validation of the optimised dosage obtained from the cementitious paste tests. The results obtained clearly indicate that the optimised dosage obtained by using the empirical method yields SCC without segregation. The lower dosages are not sufficient enough to control the segregation. On the other hand, dosages higher than optimal resulted in reduced slump flow. � 2009 RILEM.

The Department of radiology of the Hospital Trousseau (Paris) and IRSN have initiated in 2007 a detailed analysis of doses delivered to children undergoing CT examinations. This study involved three types of CT examination (chest, abdomen + pelvis and petrosal bones) and three age groups (new born to 1 year, 4 to 6 years and 9 to 11 years). A first analysis of doses was realised on a six months period of activity at the end of 2006. It stated that the standardized protocols of the scanner, that satisfied the relevant dosimetric requirements, were regularly modified by the operator to acquire the CT slides: the practice among the department was heterogeneous and the mean values of DLP were systematically higher than those recommended. The whole staff of the department has been informed of these first results and made aware of optimization of delivered doses. The standardized protocols of the scanner have been optimized. A second analysis, similar to the first one, was conducted in 2008. It showed the harmonization of the practice among the department, a significant decrease in the mean DLP (-50% for chest CT) and the respect for the dosimetric requirements for the three examinations concerned. © EDP Sciences, 2010.; En 2007, le service de radiologie de l’Hôpital Trousseau (Paris) et l’IRSN ont initié une analyse détaillée des doses délivrées lors d’examens scanographiques chez l’enfant. Cette étude a porté sur trois types d’examen (thorax, abdomen-pelvis et rochers) et trois tranches d’âge (nouveau né à 1 an, 4 à 6 ans et 9 à 11 ans). Une première analyse des doses a été réalisée a posteriori sur l’activité du second semestre 2006. Elle a montré que les protocoles d’acquisition du scanner, qui satisfaisaient aux recommandations dosimétriques en vigueur, étaient mal respectés dans le service : les pratiques étaient hétérogènes et les valeurs moyennes de PDL supérieures aux recommandations. Après ces premiers résultats, une action de sensibilisation à l’optimisation des doses a été menée auprès du personnel et les protocoles d’acquisition du scanner ont été optimisés. L’analyse dosimétrique reprise en 2008 suivant le schéma de 2006 a montré une harmonisation des pratiques au sein du service, une diminution significative des PDL moyens (-50 % par exemple en scanographie thoracique) et le respect systématique des recommandations dosimétriques pour les trois examens étudiés.

One strategy to minimise petroleum fuel consumption of a Plug-in Hybrid Electric Vehicle (PHEV) is to attain the lowest admissible battery State of Charge (SOC) at the end of driving cycle while following an optimal SOC profile. The challenge of an optimisation algorithm is to find this optimal profile by using least future information about the power demand. An application of Equivalent Consumption Minimisation Strategy (ECMS) for PHEV is presented in this paper and benchmarked against the dynamic programming (DP) for information requirement and optimality. The optimality is assessed in simulation by considering petroleum fuel economy and deviation of the optimal SOC profile from a reference profile for different driving scenarios and battery sizes. Results show that for longer distances and larger battery sizes, ECMS and DP provide similar fuel economy and SOC profiles. A sensitivity analysis with respect to driving distance is presented at the end of the paper.

The Variable-Flow Ducted Rocket (VFDR) is a type of ramjet that can control the fuel mass flow to the combustion chamber. It combines the high efficiency at high-speed of ramjets with the throttlability of turbofans, and this makes VFDR propulsion an excellent choice for high speed, long range missiles. This is especially true because the throttlability enables full flight optimisation, which has the potential to significantly increase the performance (range, terminal velocity, intercept time) with regard to standard ramjets. Previous work demonstrated mid-course guidance of a VFDR powered missile based on offline static flight range optimisation. Using this approach, flight transitions, that can have a large effect on performance, are never quite optimal, and optimisation goals other than range are non-trivial. Offline dynamic flight optimisations, which do incorporate transitions and allow other optimisation criteria, are not practical for use in guidance, because of the large amount of possible scenario's, especially when many target updates are sent. In this paper, the feasibility of online dynamic optimisation of the mid-course flight of a Meteor-like missile is demonstrated, that does not have the limitations of off-line optimisations. A Collocation method is applied to a two-phase non-linear programming problem, in order to optimise 2 controls of a 5 state missile model at intervals of several seconds. Several scenarios are presented, in which the effects are shown of different optimisation criteria, as well as the effects of changes in the target state. Copyright © 2009 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.

Nowadays, the general trend towards to minimally invasive interventions is present in all the medical domains. For the surgical intervertebral spinal disc cutting or removal domain, it is particularly a necessity because the manual methods currently employed are tedious, time consuming and taxing on the hands of the practitioner. In that context, new devices that are small enough to pass through a small opening in the skin or through a small portal are required. A detailed analysis of current cutting methods that are or could be used for disc and disc nucleus removal provided that ultrasonics technology should be investigated as a possible solution. The study of ultrasonics technology to fulfil the overall cutting function needed for spinal annulus and nucleus disc material removal is based on a design methodology that breaks down the overall cutting function in many partial functions. The applied design methodology consists in drawing a complete catalogue of solutions for each partial function. Based on a predetermined choice of criteria for each partial function, the evaluation and the classification of each solution allows determination of the best solutions for each partial function. A new ultrasonic transducer designed device composed of a piezoelectric stack for the source of energy and movement, a transmission partial function with rods or discs, an amplification partial function with exponential horns and the cutting partial function solutions is detailed. Existing analytical methods for the design of ultrasonic transducers are mostly based on quarter wavelength segments used to build the transducer. The modeling of that transducers with a finite element method (FEM) avoids building the prototypes and constitutes progress. Analytical models different from the quarter wavelength approach have already been developed and are very useful when used in an optimization process. Furthermore, when the geometry of the analyzed model is not straightforward, a FEM optimization approach to solve that kind of problems can be a valid solution too. Some existing optimization algorithms and other already developed pseudo-gradient methods applied to optimize the analytical models of the ultrasonic transducers are not valid for numerical optimizations where the computing time is a key factor. This leads to the development a new genetic algorithm (GA) optimization methods. One advantage being that the number of parameters to be optimized does not change the complexity of the algorithm unlike other algorithms. Three GAs with improvements done on different parts are discussed, implemented and tested. One GA is chosen to optimize the transducer model with numerical methods. As the main drawback with FE optimizations is the amount of computation time spent for each simulation, this creates a need to develop numerical 2D models that can be quickly simulated but with accurate results. Ultrasonic transducer prototypes are also built and measured. As the prototype has to be used for the cutting or the removal of spinal disc material, the cutting effect of the prototypes has been tested and evaluated.

The Variable-Flow Ducted Rocket (VFDR) is a type of ramjet that can control the fuel mass flow to the combustion chamber. It combines the high efficiency at high-speed of ramjets with the throttlability of turbofans, and this makes VFDR propulsion an excellent choice for high speed, long range missiles. This is especially true because the throttlability enables full flight optimisation, which has the potential to significantly increase the performance (range, terminal velocity, intercept time) with regard to standard ramjets. Previous work demonstrated mid-course guidance of a VFDR powered missile based on offline static flight range optimisation. Using this approach, flight transitions, that can have a large effect on performance, are never quite optimal, and optimisation goals other than range are non-trivial. Offline dynamic flight optimisations, which do incorporate transitions and allow other optimisation criteria, are not practical for use in guidance, because of the large amount of possible scenario's, especially when many target updates are sent. In this paper, the feasibility of online dynamic optimisation of the mid-course flight of a Meteor-like missile is demonstrated, that does not have the limitations of off-line optimisations. A Collocation method is applied to a two-phase non-linear programming problem, in order to optimise 2 controls of a 5 state missile model at intervals of several seconds. Several scenarios are presented, in which the effects are shown of different optimisation criteria, as well as the effects of changes in the target state. Copyright © 2009 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.

Precast concrete pipe racks are an economically advantageous alternative to steel pipe racks in industrial projects like refineries and petrochemical plants. One major reason for this is the elimination of fire proofing needs for the precast element. Optimal design of the concrete elements would further lower the cost. This paper deals with the optimal design of precast concrete pipe racks, considering design variables such as member sizes, grades of concrete and steel, and reinforcement area. Genetic algorithms (GA) have been used for optimisation and a program in VC++ has been developed to arrive at the optimal solution, which corresponds to overall cost, satisfying design criteria for strength and limiting deflection. Proposals for most favourable jointing mechanisms in terms of cost and constructability have also been given.

The fresh concrete properties of self-compacting concrete (SCC) greatly depend on the paste composition and paste content. As far as high performance concrete is concerned, apart from cement and water, mineral and chemical admixtures play a vital role especially in the paste formulation of SCC. Most of the studies focus only on the influence of the different dosages of mineral and chemical admixtures on the performance of SCC. Moreover, the production cost of SCC is high due to the usage of these admixtures, especially the chemical admixtures for enhanced performance. Hence there is a need for optimised use of the mineral and chemical admixtures. In the present investigation, a new systematic approach based on the concepts of particle packing and rheology was adopted for optimising the powder combinations (cement and fly ash) and superplasticiser (Polycarboxylate based ether) dosage respectively. An empirical test was conducted to optimise the cement - fly ash combination, while the superplasticiser dosage was optimised for 5 different w/p ratios using mini slump tests on cement paste. An attempt was made to understand the influence of optimised dosage of superplasticiser for different w/p ratio on the rheological properties of the cementitious paste. The optimised dosage of mineral and chemical admixtures of the paste was validated in concrete. The results indicate that the slump flow of concrete for the optimised dosage of superplasticiser for different w/p ratio ranges from 550 mm to 650 mm with T500 time ranging from 1 to 6 seconds.

Artificial intelligence has brought new features to unmanned aerial vehicles. Path planning, trajectory optimization and obstacle avoidance are some of the features that artificial intelligence brings to drones. In this study, path planning optimization and collision avoidance with artificial intelligence for an unmanned aerial vehicle are emphasized. Path planning and collision avoidance simulations were performed using MATLAB software under ideal conditions and under noisy conditions. It is shown that the unmanned aerial vehicle finds the shortest path on maps with various obstacles. © 2021 IEEE.

Underwater wireless sensor networks (UWSNs) are utilized for a wide range of monitoring and surveillance applications. Lifetime maximization and maintenance of network reliability are among the most important considerations in the deployment of UWSNs. k-connectivity is a robust approach for reinforcing reliability. However, maintaining k disjoint paths from each sensor node to the BS, inevitably, results in extra energy dissipation, which reduces the network lifetime (NLT). Yet, there is no systematic exploration to determine the extent of lifetime reduction due to the increase in the k value, in the literature, to the best of our knowledge. In this study, we create an optimization framework to be able to explore the trade-off between NLT and k-connectivity based reliability in UWSNs. Through the optimal solutions of the proposed optimization model for a large set of salient parameters, we characterize the inter-play between lifetime and k-connectivity. Our analysis reveals that the k value to be maintained in a UWSN can affect the NLT significantly. IEEE