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PL
Suwnica 3D jest obiektem stosowanym w różnych gałęziach przemysłu. Z punktu widzenia sterowania, jest to system dynamiczny, nieliniowy i wielowymiarowy. W artykule zaprojektowano dwa układy regulacji: z klasycznym regulatorem PID oraz z regulatorem PID ułamkowego rzędu. Przedstawiono analizę porównawczą zaprojektowanych układów regulacji.
EN
The 3D crane is an object used in various industries. From a control point of view, it is a dynamic, non-linear and multidimensional system. In this paper, two control systems are designed: with a classical PID controller and with a fractional-order PID controller. A comparative analysis of the designed control systems is presented.
EN
This paper presents the results of studies on the transformation of geraniol (GA) in the presence of the natural mineral bentonite. The paper determines the influence of temperature, catalyst content, and reaction time on the course of the process. In order to determine the most favorable process conditions, the catalytic tests were carried out without solvent and under atmospheric pressure. Three functions were chosen to determine the most favorable process conditions: GA conversion and the selectivities of the main products: linalool – LO and beta-pinene – BP. In addition, the paper optimize GA transformation process based on response surface methodology (RSM). The impact of the most relevant process indicators was presented. For all factors of the method, their effects on all primary parameters were determined in the form of second-degree polynomials, and such process conditions were determined to achieve their maximum.
EN
Motivated by applications, we consider new operator-theoretic approaches to conditional mean embedding (CME). Our present results combine a spectral analysis-based optimization scheme with the use of kernels, stochastic processes, and constructive learning algorithms. For initially given non-linear data, we consider optimization-based feature selections. This entails the use of convex sets of kernels in a construction of optimal feature selection via regression algorithms from learning models. Thus, with initial inputs of training data (for a suitable learning algorithm), each choice of a kernel K in turn yields a variety of Hilbert spaces and realizations of features. A novel aspect of our work is the inclusion of a secondary optimization process over a specified convex set of positive definite kernels, resulting in the determination of “optimal” feature representations.
EN
In this work, we propose a multi-server queuing system for modeling the processes that occur in a maritime container terminal. In our study, the main operations that take place at the quay and in the yard are first disaggregated into several elementary activities. Then we propose the step-by-step calculation of the times of each operation that influences both the unloading and the loading of a container. Next, we analyze the vessel cycle time while separately investigating the STS (ship to shore) crane cycle time, the RTG (rubber tyred gantry) cycle time, as well as the IMV (internal movement vehicle) transfer time. Finally, we apply two process-driven simulation experiments to the system analysis. The paper demonstrates the proposed model’s effectiveness with data from the BCT Gdynia container terminal. We show that, among others, even with properly planned work of STS cranes and RTGs, there is still a high probability that the quay will become a bottleneck of the described processes.
5
EN
Optimization of machine learning architectures is essential in determining the efficacy and the applicability of any neural architecture to real world problems. In this work a generalized Newton's method (GNM) is presented as a powerful approach to learning in deep neural networks (DNN). This technique was compared to two popular approaches, namely the stochastic gradient descent (SGD) and the Adam algorithm, in two popular classification tasks. The performance of the proposed approach confirmed it as an attractive alternative to state-of-the-art first order solutions. Due to the good results presented in the case of shallow DNN, in the last part of the article an hybrid optimization Method is presented. This method consists in combining two optimization algorithms, i.e. GNM and Adam or GNM and SGD, during the training phase within the layers of the neural network. This configuration aims to benefit from the strengths of both first- and second-order algorithms. In this case a convolutional neural network is considered and its parameters are updated with a different optimization algorithm. Also in this case, the hybrid approach returns the best performance with respect to the first order algorithms.
EN
In this work, a study focusing on proposing generalization metrics for Deep Reinforcement Learning (DRL) algorithms was performed. The experiments were conducted in DeepMind Control (DMC) benchmark suite with parameterized environments. The performance of three DRL algorithms in selected ten tasks from the DMC suite has been analysed with existing generalization gap formalism and the proposed ratio and decibel metrics. The results were presented with the proposed methods: average transfer metric and plot for environment normal distribution. These efforts allowed to highlight major changes in the model’s performance and add more insights about making decisions regarding models’ requirements.
EN
The innovative approach to the issues of integration of an electricity storage, heat storage and an electrode heating boiler in the heating system in this paper is presented. In recent years, a growing share of renewable energy sources in heating has been observed, which may result in the dynamics of electricity price variability being greater and more frequent than in daily and annual periods. This may apply in particular to the price of heat from electrode boilers. The proposed solution to optimize heat prices at an acceptable level for end users, consisting in connecting an electrode heating boiler with heat and electricity storage facilities is presented.
PL
W artykule przedstawiono innowacyjne podejście do zagadnień integracji magazynu energii elektrycznej, magazynu ciepła i elektrodowego kotła ciepłowniczego w systemie ciepłowniczym. W ostatnich latach można zaobserwować rosnący udział odnawialnych źródeł energii w ciepłownictwie, co może spowodować, że dynamika zmienności cen energii elektrycznej będzie większa i częstsza niż w okresach dobowych oraz rocznych. Może to dotyczyć w szczególności ceny ciepła z kotłów elektrodowych. W artykule przedstawiono propozycję rozwiązania dla optymalizacji cen ciepła, na akceptowalnym poziomie dla odbiorców końcowych, polegające na połączeniu elektrodowego kotła ciepłowniczego z magazynami ciepła i energii elektrycznej.
EN
Background: Continuous modifications, suboptimal software design practices, and stringent project deadlines contribute to the proliferation of code smells. Detecting and refactoring these code smells are pivotal to maintaining complex and essential software systems. Neglecting them may lead to future software defects, rendering systems challenging to maintain, and eventually obsolete. Supervised machine learning techniques have emerged as valuable tools for classifying code smells without needing expert knowledge or fixed threshold values. Further enhancement of classifier performance can be achieved through effective feature selection techniques and the optimization of hyperparameter values. Aim: Performance measures of multiple machine learning classifiers are improved by fine tuning its hyperparameters using various type of meta-heuristic algorithms including swarm intelligent, physics, math, and bio-based etc. Their performance measures are compared to find the best meta-heuristic algorithm in the context of code smell detection and its impact is evaluated based on statistical tests. Method: This study employs sixteen contemporary and robust meta-heuristic algorithms to optimize the hyperparameters of two machine learning algorithms: Support Vector Machine (SVM) and k-nearest Neighbors (k-NN). The No Free Lunch theorem underscores that the success of an optimization algorithm in one application may not necessarily extend to others. Consequently, a rigorous comparative analysis of these algorithms is undertaken to identify the best-fit solutions for code smell detection. A diverse range of optimization algorithms, encompassing Arithmetic, Jellyfish Search, Flow Direction, Student Psychology Based, Pathfinder, Sine Cosine, Jaya, Crow Search, Dragonfly, Krill Herd, Multi-Verse, Symbiotic Organisms Search, Flower Pollination, Teaching Learning Based, Gravitational Search, and Biogeography-Based Optimization, have been implemented. Results: In the case of optimized SVM, the highest attained accuracy, AUC, and F-measure values are 98.75%, 100%, and 98.57%, respectively. Remarkably, significant increases in accuracy and AUC, reaching 32.22% and 45.11% respectively, are observed. For k-NN, the best accuracy, AUC, and F-measure values are all perfect at 100%, with noteworthy hikes in accuracy and ROC-AUC values, amounting to 43.89% and 40.83%, respectively. Conclusion: Optimized SVM exhibits exceptional performance with the Sine Cosine Optimization algorithm, while k-NN attains its peak performance with the Flower Optimization algorithm. Statistical analysis underscores the substantial impact of employing meta-heuristic algorithms for optimizing machine learning classifiers, enhancing their performance significantly. Optimized SVM excels in detecting the God Class, while optimized k-NN is particularly effective in identifying the Data Class. This innovative fusion automates the tuning process and elevates classifier performance, simultaneously addressing multiple longstanding challenges.
EN
The constant increase in energy demand and the need to reduce the carbon footprint on the environment has put countries in a race against time, looking for alternative resources or ways to supply this need. One of the main resources is solar radiation, which can be used to generate energy, initially on a small scale, but in recent years has been directed towards supplying large cities. The economic, political, and social investment must respond to planning and expansion criteria in order to generate feasible proposals. Through simulation of a real electrical system, the voltage instability was determined, which was corrected at software level by entering a photovoltaic solar plant, being this dimensioned from the PV curve obtained. Finally, the optimal location for the development of a solar photovoltaic plant among four possible scenarios was obtained through the application of an optimization algorithm. This approach was converted into an alternative applicable to different geographical locations.
EN
Changing the ignition advance angle has a significant impact on the performance of a combustion engine. Optimization of ignition advance angle is a major task of adjusting the engine concerning emission standards, fuel consumption, torque value, etc. The results of the research showed that the process of optimizing the ignition advance curve can noticeably increase engine efficiency, as well as torque and power output from the engine while reducing fuel consumption as a result of lower indications of the air flow mass per second from MAF sensor (mass air flow sensor). The highest impact of the ignition advanced angle modifications can be seen in the area of the highest volumetric efficiency of the tested combustion engine. Almost no impact is observed within high engine speed levels. Simultaneously increasing engine load and rotation speed increases the possibility of engine knocking, which has a devastating effect on engine durability.
EN
Glycidylazide polymer (GAP) tetraol or tetra functional GAP (t-GAP) is a potential energetic binder, capable of exhibiting superior mechanical properties and better curing behaviour for application in high energy propellants. t-GAP is conventionally prepared through azidation of tetra functional poly-epichlorohydrin (t-PECH). Azidation reactions using a metal azide are known to be sensitive to temperature. The present study was aimed at a systematic evaluation of the safe temperature limit for the preparation of t-GAP and to derive optimized reaction conditions using a thermal screening unit (TSU), through both dynamic and isothermal heating experiments. The thermal hazard studies suggested that the azidation reaction is fairly stable at temperatures above 100 °C as it did not exhibit any abrupt rise in reaction temperature or pressure. The process was validated using laboratory scale batches and completion of the reaction was verified using FTIR spectroscopy.
EN
The power sector confronts a crucial challenge in identifying sustainable and environmentally friendly energy carriers, with hydrogen emerging as a promising solution. This paper focuses on the modeling, analysis, and techno-economic evaluation of an independent photovoltaic (PV) system. The system is specifically designed to power industrial loads while simultaneously producing green hydrogen through water electrolysis. The emphasis is on utilizing renewable sources to generate hydrogen, particularly for fueling hydrogen-based cars. The study, conducted in Skikda, Algeria, involves a case study with thirty-two cars, each equipped with a 5 kg hydrogen storage tank. Employing an integrated approach that incorporates modeling, simulation, and optimization, the techno-economic analysis indicates that the proposed system provides a competitive, cost-effective, and environmentally friendly solution, with a rate of 0.239 $/kWh. The examined standalone PV system yields 24.5 GWh/year of electrical energy and produces 7584 kg/year of hydrogen. The findings highlight the potential of the proposed system to address the challenges in the power sector, offering a sustainable and efficient solution for both electricity generation and hydrogen production.
EN
To improve the dynamic adaptability and flexibility of the process route during manufacturing, a dynamic optimization method of the multi-process route based on an improved ant colony algorithm driven by digital twin is proposed. Firstly, based on the analysis of the features of the manufacturing part, the machining methods of each process are selected, and the fuzzy precedence constraint relationship between machining metas and processes is constructed by intuitionistic fuzzy information. Then, the multi-objective optimization function driven by the digital twin is established with the optimization objectives of least manufacturing cost and lowest carbon emission, also the ranking of processing methods is optimized by an improved adaptive ant colony algorithm to seek the optimal processing sequence. Finally, the transmission shaft of some equipment is taken as an engineering example for verification analysis, which shows that this method can obtain a process route that gets closer to practical production.
EN
In this study, cost optimization of a 4-storey school building is carried out. For the optimization, ACDOS (Automated Cost and Design Optimization of Structures) program – which is a computing platform created by the authors – is used. The Rao-1 algorithm is the optimization method used. As a result, a cost analysis of the RC building was performed and 12% cost savings were achieved.
15
Content available Introduction to the "Theory of Compensation"
EN
In this article, we systematize and emphasize the information elements that are the starting point for step-by-step actions related to the improvement of technological maps in accordance with the “energy model” to ensure sustainable sanitary and hygienic standards throughout the life cycle of buildings. In particular, we draw attention to the lack of analytical methodologies to visually or technically incorporate optimization measures to achieve the final building outcome in accordance with energy models A, B and C throughout the life cycle of buildings. These findings serve as a basis for research aimed at improving the analytical methods and ensuring their compliance with the established legal standards.
EN
The main goal of robot path planning is to design an optimal path for a robot to navigate from its starting point to its goal while avoiding obstacles and optimizing certain criteria. A novel method using marine predator algorithm which is used in the field of robot path planning is presented. The proposed method has two steps. First step is to build a mathematical model of path planning while second step is optimization process using marine predator algorithm. Simulation results show that the proposed method works well and has good performance in different situations. Therefore, this method is an effective method for robot path planning and related applications.
EN
This study, centered around the engineering context of the Wuxue Yangtze River Bridge, addresses the challenge of significant temperature-induced secondary internal forces in the short lower tower column. A novel open lower corbel tower scheme is proposed as a solution. Firstly, comprehensive finite element models are established for both the open lower corbel pylon scheme and the traditional lower continuous beam pylon scheme. These models are employed for finite element analysis to derive bending moments and displacements of the bridge pylon under various loads, including permanent, vehicle, temperature, and wind loads. Subsequently, considering internal force distribution and stiffness, a comparative assessment is made between the open lower corbel cable pylon scheme and the traditional lower continuous beam cable pylon scheme. The findings reveal that the open corbel structure bridge pylon exhibits lower transverse bending moment values under the influence of permanent load, vehicle load, temperature load, and wind load. This reduction is advantageous for mitigating the issue of significant temperature-induced secondary internal forces in the bridge pylon. Additionally, the transverse bridge stiffness of the open lower corbel cable pylon scheme is found to be on par with that of the lower continuous beam cable pylon scheme. Moreover, topology optimization of the original corbel design is accomplished using the relative density method. The computational results demonstrate that the corbel’s stress and deformation under vertical loads meet code requirements. These research findings offer valuable insights for the design and construction of similar projects.
EN
Plants belonging to the Apiaceae family (including Levisticum officinale WDJ Koch) are rich sources of phytochemicals and secondary metabolites, with possible health-promoting and agrochemical potential. The objective of this work was to provide important guidelines for controlling conventional aqueous extraction to obtain Levisticum officinale root extracts with maximised levels of bioactive compounds. The ultimate goal was to optimise the total phenolic compounds, flavonoid content, sugars, and total antioxidant capacity to identify the process conditions necessary to produce highly bioactive extracts that could be used in a wide range of industries. Biomass extraction of lovage root was carried out using water as the extraction solvent. To perform the optimisation of the aqueous extraction, multivariate regression models were used and multi-criteria analysis was performed using Pareto set navigation. Pareto front analysis showed that for the maximum extraction efficiency of bioactive compounds from Levisticum officinale, the optimal extraction process parameters were 0.0714 g⸱mL-1 as biomass/water ratio and a time of 35.7142 min, at the highest analysed temperature. For the highest analysed value of plant biomass/solvent ratio (0.075 g⸱mL-1) and maximum process temperature (95ºC), extraction could be carried out for 20 min or in the range 37.1429-38.5714 min. On the other hand, if the extraction time reaches 40 min and the sam-ple/solvent ratio 0.075 g⸱mL-1, the optimum process temperature is be-tween 75ºC and 95ºC.
PL
Rośliny należące do rodziny Apiaceae (w tym Levisticum officinale WDJ Koch) są bogatym źródłem fitochemikaliów i metabolitów wtórnych o potencjalnym potencjale prozdrowotnym i agrochemicznym. Celem niniejszej pracy było dostarczenie ważnych wytycznych dotyczących kontrolowania konwencjonalnej ekstrakcji wodnej w celu uzyskania ekstraktów z korzenia Levisticum officinale o zmaksymalizowanych poziomach związków bioaktywnych. Dlatego też w niniejszym badaniu oceniono i zoptymalizowano potencjał przeciwutleniający wodnych ekstraktów z Levisticum officinale pod kątem analizy wpływu parametrów procesu ekstrakcji, tj. temperatury, czasu i stosunku biomasy roślinnej do rozpuszczalnika. Ostatecznym celem była optymalizacja całkowitej zawartości związków fenolowych, flawonoidów, cukrów i całkowitej zdolności przeciwutleniającej w celu zidentyfikowania warunków procesu niezbędnych do wytworzenia wysoce bioaktywnych ekstraktów, które mogłyby być stosowane w wielu gałęziach przemysłu. Ekstrakcję biomasy korzenia lubczyku przeprowadzono przy użyciu wody jako rozpuszczalnika ekstrakcyjnego. Aby przeprowadzić optymalizację ekstrakcji wodnej, zastosowano wielowymiarowe modele regresji i przeprowadzono analizę wielokryterialną przy użyciu nawigacji zestawu Pareto.Procedury optymalizacyjne wykazały dużą złożoność rozważanego problemu badawczego, co bezpośrednio utrudnia wybór jednego najlepszego rozwiązania. W związku z tym wyznaczono zbiory rozwiązań. Analiza frontu Pareto wykazała, że dla maksymalnej wydajności ekstrakcji związków bioaktywnych z Levisticum officinale, optymalnymi parametrami procesu ekstrakcji były 0,0714 g⸱ml-1 jako stosunek biomasy do wody oraz czas 35,7142 min, w najwyższej analizowanej temperaturze. Dla najwyższej analizowanej wartości stosunku biomasy roślinnej do rozpuszczalnika (0,075 g⸱ml-1) i maksymalnej temperatury procesu (95ºC), ekstrakcję można było prowadzić przez 20 min lub w zakresie 37,1429-38,5714 min. Z drugiej strony, jeśli czas ekstrakcji osiągnie 40 min, a stosunek próbki do rozpuszczalnika 0,075 g⸱ml-1, optymalna temperatura procesu wynosi od 75ºC do 95ºC.
EN
Hot storage tanks (HST) are known for their high energy consumption, attributed to variations in usage, heat dissipation within the tank, and heat losses to the surroundings. This study proposes a chimney-type electrically heated HST, which is investigated under static mode to enhance its thermal performance. Different natural circulation areas (chimney areas) with large (9.5 cm diameter), medium (2.5 cm diameter), and small (1.5 cm diameter) sizes were utilized to examine the effect of natural circulation on the HST performance. Additionally, the influence of chimney insulation on the HST performance was also studied. The experiments revealed that the chimney significantly affected the thermal stratification within the tank. Different chimney contact diameters (9.5 cm, 2.5 cm, and 1.5 cm) were tested, showing varying degrees of thermal stratification. The results indicated that smaller chimney contact diameters led to higher thermal stratification and more rapid heating of the top layer temperatures. However, the impact of insulation on thermal performance was inconclusive, suggesting the need for more effective insulation and further investigation into the dynamic mode of operation. The findings also highlighted the faster heating of the top outer layer compared to the larger diameter, emphasizing the significance of the chimney type electrical heater in the hot storage tank.
PL
Artykuł dotyczy wykorzystania algorytmu optymalizacji rojem cząstek do rozwiązywania układów równań nieliniowych. Przeprowadzona została eksperymentalna analiza efektywności i skuteczności działania algorytmu w zależności od ustawień jego parametrów.
EN
The article concerns the use of a particle swarm optimization algorithm for solving nonlinear equation systems. An experimental analysis of the effectiveness and efficiency of the algorithm has been conducted, considering various settings of its parameters.
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