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EN
The article presents a method of calibration of material parameters of a numerical model based on a genetic algorithm, which allows to match the calculation results with measurements from the geotechnical monitoring network. This method can be used for the maintenance of objects managed by the observation method, which requires continuous monitoring and design alterations. The correctness of the calibration method has been verified on the basis of artificially generated data in order to eliminate inaccuracies related to approximations resulting from the numerical model generation. Using the example of the tailing dam model the quality of prediction of the selected measurement points was verified. Moreover, changes of factor of safety values, which is an important indicator for designing this type of construction, were analyzed. It was decided to exploit the case of dam of reservoir, which is under continuous construction, that is dam height is increasing constantly, because in this situation the use of the observation method is relevant.
EN
Environmental threats of coal usage in the electricity production combined with the consumption of renewable and non-renewable resources had led to worldwide energy challenges. The cost of coal mining and economical and environmentally sustainable usage of mined coal could be optimized by efficient management of coal supply chain. This paper provides a mathematical model for improving coal supply chain sustainability including the cost of exergy destruction (entropy). In the proposed method, exergy analysis is used to formulate the model considering not only economic costs but also destructed exergy cost, while genetic algorithm is applied to efficiently solve the proposed model. In order to validate the proposed methodology, some numerical examples of coal supply chains are presented and discussed to show the usability of the proposed exergetic coal supply chain model and claim its benefits over the existing models. According to the results, the proposed method provides 17.6% saving in the consumed exergy by accepting 2.7% more economic costs. The presented model can be used to improve the sustainability of coal supply chain for either designing new projects or upgrading existing processes.
EN
Under conditions of gravity flow, the performance of a distribution pipe network for drinking water supply can be measured by investment cost and the difference in real and target pressures at each node to ensure fairness of the service. Therefore, the objective function for the optimization in the design of a complex gravity flow pipe network is a multi-purpose equation system set up to minimize the above-mentioned two parameters. This article presents a new model as an alternative solution to solving the optimization equation system by combining the Newton–Raphson and genetic algorithm (GA) methods into a single unit so that the resulting model can work effectively. The Newton–Raphson method is used to solve the hydraulic equation system in pipelines and the GA is used to find the optimal pipe diameter combination in a network. Among application models in a complex pipe network consisting of 12 elements and 10 nodes, this model is able to show satisfactory performance. Considering variations in the value of the weighting factor in the objective function, opti-mal conditions can be achieved at the investment cost factor (ω1) = 0.75 and the relative energy equalization factor at the service node (ω2) = 0.25. With relevant GA input parameters, optimal conditions are achieved at the best fitness value of 1.016 which is equivalent to the investment cost of USD 56.67 thous. with an average relative energy deviation of 1.925 m.
EN
Manual interpretation of heart sounds is insensitive and prone to subjectivity. Automated diagnosis systems incorporating artificial intelligence and advanced signal processing tools can potentially increase the sensitivity of disease detection and reduce the subjectiveness. This study proposes a novel method for the automated binary classification of heart sound signals using the Fano-factor constrained tunable quality wavelet transform (TQWT) technique. Optimal TQWT based decomposition can reveal significant information in subbands for the reconstruction of events of interest. While transforming heart sound signals using TQWT, the Fano-factor is applied as a thresholding parameter to select the subbands for the clinically relevant reconstruction of signals. TQWT parameters and threshold of the Fanofactor are tuned using a genetic algorithm (GA) to adapt to the underlying optimal detection performance. The time and frequency domain features are extracted from the reconstructed signals. Overall 15 unique features are extracted from each sub-frame resulting in a total feature set of 315 features for each epoch. The resultant features are fed to Light Gradient Boosting Machine model to perform binary classification of the heart sound recordings. The proposed framework is validated using a ten-fold cross-validation scheme and attained sensitivity of 89.30%, specificity of 91.20%, and overall score of 90.25%. Further, synthetic minority over-sampling technique (SMOTE) is applied to produce balanced data set which yielded sensitivity and specificity of 86.32% and 99.44% respectively and overall score of 92.88%. Our developed model can be used in digital stethoscopes to automatically detect abnormal heart sounds and aid the clinicians in their diagnosis.
EN
Medical robots with an instant center of rotation mechanism in a trocar are used for operating a human body or servicing artificial organs. The result of the work is the development of a multi-criteria optimization model of a discussed medical robot, considering safety factor, first eigenfrequency and buckling coefficient as a criteria. The article also analyzes two issues of mechanics, the natural frequency and linear buckling. A discrete mesh model of a novel robot design with ten degrees of freedom and ended with a scalpel was developed based on finite element method. For the given loads and supports, a multi-criteria optimization model was evolved, which was solved by using the response surface method and the multi-objective genetic algorithm. The results section shows the Pareto fronts for the criteria and geometrical dimensions of the kinematic chain. The courses of resonant vibrations and buckling strains were also characterized. The solved optimization model gives correct values for the adopted criteria. The values of resonance were defined, which makes it possible to select mechatronic drive systems in terms of the input they generate. Variability of the resonant vibrations phenomena, as well as shapes and directions of buckling, provide information about the displacements taking place in the medical robot system.
EN
Reducing contaminant emissions is an important task of any industry, included the maritime one. In fact, in April 2018, IMO (International Maritime Organization) adopted an Initial Strategy on reduction of Greenhouse gas (GHG) emissions from ships. An essential part responsible for producing these emissions is the diesel engine. For that reason vessels include separation systems for heavy fuel oils. The purpose of this work is to improve the predictive maintenance techniques incorporating new intelligent approaches. An analysis of vibrations of this separation system was made and their characteristics were used in a Genetic Neuro-Fuzzy System in order to design an intelligent maintenance based on condition monitoring. The achieved results show that the proposed method provides an improvement since it indicates if a maintenance operation is necessary before the schedule one or if it could be possible extend the next maintenance service.
PL
W artykule przedstawiono podstawowe właściwości i badania laboratoryjne wysokoczęstotliwościowego falownika klasy EF (20 MHz, 400 W, 91,2%) z ćwierćfalową linią długą dołączoną po stronie zasilania. Falownik ten zawiera jeden tranzystor, przebieg napięcie tranzystora zbliżony jest do prostokątnego oraz występuje przełączanie miękkie tranzystora typowe dla klasy E. Zastosowany tranzystor MOSFET serii DE sterowany jest za pomocą dedykowanego, niskostratnego sterownika bramkowego własnej konstrukcji. Wyjaśniono metodę optymalizacji parametrów falownika klasy EF ze względu na sprawność, którą zrealizowano z wykorzystaniem oprogramowania ANSYS Simplorer i wbudowanego algorytmu genetycznego. Koncepcja falownika klasy EF została pozytywnie zweryfikowana laboratoryjnie. Zarejestrowano przykładowe przebiegi czasowe napięć tranzystora oraz wyznaczono wybrane parametry falownika.
EN
Some basic properties and laboratory tests of high-frequency Class EF inverter (20 MHz, 400 W, 91.2%) with quarter-wave transmission line on the supply side are presented in the article. The inverter contains one transistor, the transistor voltage waveform is close to a rectangular one and the soft-switching of the transistor is realized as typically in Class E. The applied DE-series MOSFET transistor was controlled by a dedicated, low-loss driver of its own design. The optimization method of the Class EF inverter parameters to maximize efficiency was explained. It was implemented using ANSYS Simplorer software and a built-in genetic algorithm. The concept of the Class EF inverter was positively verified in the laboratory. Examples of transistor voltage waveforms were recorded and selected inverter parameters were determined.
EN
The use of wind energy in water pumping is an economically viable and sustainable solution to rural communities without access to the electricity grid. The aim of this paper is to present a detailed modeling of the wind-powered pumping system, propose and compare some control schemes to optimize the performance of the system and enhance the quality of the generated power. The wind energy system used in this paper consists of a permanent magnet synchronous generator (PMSG) and static converters directly coupled to an asynchronous motor that drives a centrifugal pump. A typical control is applied to the proposed configuration for the purpose of controlling the generator to extract maximum wind power. Furthermore, four types of controllers (PI and conventional RST polynomials, adaptive RST-fuzzy and genetic algorithm are designed for the wind energy system and tested under various operating conditions.
PL
Wykorzystanie energii wiatru w pompowaniu wody jest opłacalnym i zrównoważonym rozwiązaniem dla społeczności wiejskich bez dostępu do sieci elektrycznej. Celem tego artykułu jest przedstawienie szczegółowego modelowania systemu pompowania napędzanego wiatrem, zaproponowanie i porównanie niektórych schematów sterowania, aby zoptymalizować wydajność systemu i poprawić jakość generowanej mocy. System energii wiatrowej zastosowany w tym artykule składa się z synchronicznego generatora z magnesami trwałymi (PMSG) i przekształtników statycznych bezpośrednio sprzężonych z silnikiem asynchronicznym, który napędza pompę odśrodkową. Typowe sterowanie jest stosowane do proponowanej konfiguracji w celu sterowania generatorem w celu wydobycia maksymalnej energii wiatru. Ponadto cztery typy sterowników (PI i konwencjonalne wielomiany RST, adaptacyjny algorytm rozmytego RST i genetyczny) są zaprojektowane dla systemu energii wiatrowej i testowane w różnych warunkach pracy).
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EN
The paper presents the optimal design of the electric circuit of a Wireless Power Transfer Systems used to recharge the battery of an electric car. A field-circuit model is developed for the purpose of analysis, while a Pareto-like approach – based on SA-MNSGA-III and µ-BiMO, two nature-inspired algorithms - is used for synthesis. An excellent correspondence between the results obtained with the two methods was found. Then, the optimization algorithms could be applied successfully even in more complicated cases, such as WPTSs design.
PL
W artykule zaprezentowano projekt I optymalizację obwodu do bezprzewodowego transferu energii przeznaczonego do ładowania baterii samochodu elektrycznego. Wykorzystano algorytm Pareto. Uzyskano bardzo dobrą zgodność modelu z wynikami eksperymentu.
EN
This paper proposes a novel hybrid software/hardware system to automatically create filters for image processing based on genetic algorithms and mathematical morphology. Experimental results show that the hybrid system, implemented using a combination of a NIOS-II processor and a custom hardware accelerator in an Altera FPGA device, is able to generate solutions that are equivalent to the software version in terms of quality in approximately one third of the time.
PL
W artykule zaproponowano nowe hybrydowe oprogramowanie do automatycznego tworzenia filtrów grafiki bazuj ˛acych na algorytmach genetycznych i morfologii matematycznej. Eksperymenty wykazały ˙ze proponowany system wykorzystuj ˛acy procesor NIOS-II i Altera FPGA jest w stanie generowa´c rozwi ˛azanie niemal trzy razy szybciej ni˙z dotychczas stosowane systemy.
EN
The purpose of this study is to optimize the location and capacity of PV in the feeder distribution system 20 kV of Central Sulawesi, Indonesia. The proposed method uses the optimization method of development from the genetic algorithm, namely NSGA-II. Optimization is carried out in three scenarios by considering the value of the total active PV power capacity which produces the minimum active power loss and voltage deviation. The simulation result shows that the integration of PV-DG can improve drop voltage of distribution system performance due to load growth effect.
PL
Celem tego badania jest optymalizacja lokalizacji i wydajności PV w systemie dystrybucji zasilania 20 kV w środkowym Sulawesi w Indonezji. Proponowana metoda wykorzystuje optymalizację opartą na algorytmie genetycznym, mianowicie NSGA-II. Optymalizację przeprowadza się w trzech scenariuszach, biorąc pod uwagę wartość całkowitej mocy czynnej PV, która powoduje minimalne straty mocy czynnej i odchylenie napięcia. Wynik symulacji pokazuje, że integracja PV-DG może poprawić wydajność systemu dystrybucji ze względu na efekt wzrostu obciążenia.
PL
W pracy dokonano przeglądu struktur regulatorów PID2DOF, przedstawiono wyniki symulacyjnego procesu optymalizacji nastaw tych regulatorów dla modelu napędu bezpośredniego z silnikiem PMSM z uwzględnieniem tętnień momentu. Przeprowadzono dwie serie optymalizacji nastaw analizowanych struktur za pomocą algorytmu genetycznego: pierwszą pod kątem tłumienia nierównomierności prędkości napędu bezpośredniego wywołanych tętnieniami momentu; drugą – referencyjną – pod kątem minimalizacji kwadratu uchybu z pominięciem modelu tętnień.
EN
This paper reviews structures of the PID2DOF controllers and presents results of a simulation process of optimizing the settings of these controllers for a PMSM direct drive model including torque ripple. Two series of optimization of the settings of these structures with the use of genetic algorithm were executed: first one in terms of minimization of speed unevenness caused by torque ripples, second – referential – in terms of ISE minimization.
EN
Cabin placement layout is an important part of ship cabin layout design. A good cabin placement layout can improve the efficiency of the ship’s cabin arrangement. However, optimisation of the layout of cabin placement is not widely studied and more often relies on the experience of the staff. Thus, a novel methodology combining systematic layout planning and a genetic algorithm to optimise the cabin placement is presented in this paper. First key elements are converted by a systematic planning method that is often applied in factory layout, and a preliminary cabin placement layout model is established according to these key elements. Then the circulation strength and adjacency strength are taken as sub-objectives to establish a mathematical model, and an improved genetic algorithm is used to optimise the model. The result of the optimisation is compared with the initial schemes to verify the validity of the algorithm. Finally, the human factors are introduced according to the actual situation. The AHP method is used to select the layout scheme of the cabin that is most likely to be applied in the actual cabin layout.
EN
Scheduling of multiobjective problems has gained the interest of the researchers. Past many decades, various classical techniques have been developed to address the multiobjective problems, but evolutionary optimizations such as genetic algorithm, particle swarm, tabu search method and many more are being successfully used. Researchers have reported that hybrid of these algorithms has increased the efficiency and effectiveness of the solution. Genetic algorithms in conjunction with Pareto optimization are used to find the best solution for bi-criteria objectives. Numbers of applications involve many objective functions, and application of the Pareto front method may have a large number of potential solutions. Selecting a feasible solution from such a large set is difficult to arrive the right solution for the decision maker. In this paper Pareto front ranking method is proposed to select the best parents for producing offspring’s necessary to generate the new populations sets in genetic algorithms. The bi-criteria objectives minimizing the machine idleness and penalty cost for scheduling process is solved using genetic algorithm based Pareto front ranking method. The algorithm is coded in Matlab, and simulations were carried out for the crossover probability of 0.6, 0.7, 0.8, and 0.9. The results obtained from the simulations are encouraging and consistent for a crossover probability of 0.6.
EN
The rapid global economic development of the world economy depends on the availability of substantial energy and resources, which is why in recent years a large share of non-renewable energy resources has attracted interest in energy control. In addition, inappropriate use of energy resources raises the serious problem of inadequate emissions of greenhouse effect gases, with major impact on the environment and climate. On the other hand, it is important to ensure efficient energy consumption in order to stimulate economic development and preserve the environment. As scheduling conflicts in the different workshops are closely associated with energy consumption. However, we find in the literature only a brief work strictly focused on two directions of research: the scheduling with PM and the scheduling with energy. Moreover, our objective is to combine both aspects and directions of in-depth research in a single machine. In this context, this article addresses the problem of integrated scheduling of production, preventive maintenance (PM) and corrective maintenance (CM) jobs in a single machine. The objective of this article is to minimize total energy consumption under the constraints of system robustness and stability. A common model for the integration of preventive maintenance (PM) in production scheduling is proposed, where the sequence of production tasks, as well as the preventive maintenance (PM) periods and the expected times for completion of the tasks are established simultaneously; this makes the theory put into practice more efficient. On the basis of the exact Branch and Bound method integrated on the CPLEX solver and the genetic algorithm (GA) solved in the Python software, the performance of the proposed integer binary mixed programming model is tested and evaluated. Indeed, after numerically experimenting with various parameters of the problem, the B&B algorithm works relatively satisfactorily and provides accurate results compared to the GA algorithm. A comparative study of the results proved that the model developed was sufficiently efficient.
EN
Background: Under conditions of digital transformation, the effective decision-making process should involve the usage of different mathematical models and methods, one of which is the transportation problem. The transportation problem, as the problem of resource allocation, is applicable in such domains as manufacturing, information technologies, etc. To get more precise solutions, the multi-index transportation problem can be applied, which allows taking into account several variables. Methods: This paper develops an approach for applying the genetic algorithm for solving four-index transportation problems. Results: The steps of the genetic algorithm for solving four-index transportation problems are outlined. The research has proved the steps of the genetic algorithm to be the same for all four-index transportation problem types, except for the first step (initialization), which is described for every type of transportation problem separately. Based on the theoretical results, the program implementation of the genetic algorithm for solving four-index symmetric transportation problems has been developed with the open-source programming language typescript. Conclusions: The paper promotes the application of the genetic algorithm for solving multi-index transportation problems. The investigated problem requires comprehensive studies, specifically, on the influence of change different parameters of the genetic algorithm (population size, the mutation, and crossover rates, etc.) on the efficiency of the algorithm in solving four-index transportation problems.
PL
Wstęp: W warunkach komputerowej transformacji, efektywny proces podejmowania decyzji powinien obejmować wykorzystania modeli metod matematycznych. Przykładem takiej sytuacji jest problem transportowy, który jest problemem alokacji zasobów, występujący w takich obszarach jak produkcji, technologie informatyczne, itp. W celu uzyskania precyzyjniejszych rozwiązań, można zastosować wieloczynnikowy problem transportowy, który umożliwia uwzględnienie wielu zmiennych. Metody: W pracy zastosowano algorytm genetyczny dla rozwiązania czteroczynnikowych problemów transportowych. Wyniki: Wyszczególniono kroki algorytmu genetycznego dla czteroczynnikowego problem transportowego. Udowodnione, że kroki algorytmu genetycznego są takie same dla wszystkich typów czteroczynnikowych problemów transportowych, z wyjątkiem pierwszego kroku (inicjalizacji), który został opisany osobno dla każdego z typów problemu transportowego. W oparciu o wyniki teoretyczne, utworzono programowanie dla algorytmu genetycznego dla rozwiązywania czteroczynnikowych problemów transportowych przy użyciu opensourcowego języka typescript. Wnioski: W pracy zaproponowano zastosowanie algorytmu genetycznego dla rozwiązywania wieloczynnikowych problemów transportowych. Analizowany problem wymaga dalszych badań, szczególnie w zakresie wpływu zmian poszczególnych parametrów algorytmu genetycznego (wielkości populacji, mutacji, współczynnika podziału, itp.) na efektywność algorytmu w rozwiązywaniu czteroczynnikowych problemów transportowych.
EN
Permanent magnet brushless DC motors (PMBLDC) find broad applications in industries due to their huge power density, efficiency, low maintenance, low cost, quiet operation, compact form and ease of control. The motor needs suitable speed controllers to conduct the required level of interpretation. As with PI controller, PID controller, fuzzy logic, genetic algorithms, neural networks, PWM control, and sensorless control, there are several methods for managing the BLDC motor. Generally, speed control is provided by a proportional-integral (PI) controller if permanent magnet motors are involved. Although standard PI controllers are extensively used in industry owing totheir simple control structure and execution, these controller shave a few control complexities such as nonlinearity, load disruption, and parametric variations. Besides, PI controllers need more precise linear mathematical models. This statement reflects the use of Classic Controller and Genetic Algorithm Based PI, PID Controller with the BLDC motor drive. The technique is used to regulate velocity, direct the BLDC motor drive system’s improved dynamic behavior, resolve the immune load problem and handle changes in parameters. Classical control & GA-based control provides qualitative velocity reaction enhancement. This article focuses on exploring and estimating the efficiency of a continuous brushless DC motor (PMBLDC) drive, regulated as a current controller by various combinations of Classical Controllers such as PI, GA-based PI, PID Controller. The controllers are simulated using MATLAB software for the BLDC motor drive.
EN
In the present work, the optimal balancing of the planar six-bar mechanism is investigated to minimize the fluctuations of shaking force and shaking moment. An optimization problem is formulated for balancing the planar six-bar mechanism by developing an objective function. The genetic algorithm and MINITAB software were used to solve the optimization problem. The selection of weighting factors has a crucial role to obtain the optimum values of design parameters. Two sets of weighting factors were considered as per the contribution of X and Y components of the shaking force and shaking moments. Shaking force and shaking moments were minimized drastically and were compared with the original values.
EN
A lot of uncertainties and complexities exist in real life problem. Unfortunately, the world approaches such intricate realistic life problems using traditional methods which has failed to offer robust solutions. In recent times, researchers look beyond classical techniques. There is a model shift from the use of classical techniques to the use of standardized intelligent biological systems or evolutionary biology. Genetic Algorithm (GA) has been recognized as a prospective technique capable of handling uncertainties and providing optimized solutions in diverse area, especially in homes, offices, stores and industrial operations. This research is focused on the appraisal of GA and its application in real life problem. The scenario considered is the application of GA in 0-1 knapsack problem. From the solution of the GA model, it was observed that there is no combination that would give the exact weight or capacity the 35 kg bag can carry but the possible range from the solution model is 34 kg and 36 kg. Since the weight of the bag is 35 kg, the feasible or near optimal solution weight of items the bag can carry would be 34 kg at benefit of 16. Additional load beyond 34 kg could lead to warping of the bag.
EN
A scheduling problem in considered on unrelated machines with the goal of total late work minimization, in which the late work of a job means the late units executed after its due date. Due to the NP-hardness of the problem, we propose two meta-heuristic algorithms to solve it, namely, a tabu search (TS) and a genetic algorithm (GA), both of which are equipped with the techniques of initialization, iteration, as well as termination. The performances of the designed algorithms are verified through computational experiments, where we show that the GA can produce better solutions but with a higher time consumption. Moreover, we also analyze the influence of problem parameters on the performances of these metaheuristics.
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