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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.
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
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.
11
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
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.
EN
The paper deals with the implementation of research concerning the collection routes of a selected company and subsequent streamlining processes in the field of distribution logistics. Specifically, the paper is focused on the optimization of collection routes of textile waste for the customer, i.e., a contractual partner of the company under investigation. The objective of the paper is to analyse step by step the current state of logistics of supplying a specific warehouse, followed by the application of the centre of gravity method for proposing a warehouse relocation. Finally, the individual routes to collection points are optimized. In general, optimization is done for two basic reasons: profit maximization and logistics costs minimization, which ultimately has a positive impact on earnings. The outcome is to determine the optimal routes with respect to costs and traffic while considering complications that may occur in a given transport territory.
EN
This study aims to improve an earlier safety analysis of port and maritime transportation systems in two cases. The first case does not consider outside impacts and the second case operates under the assumption that they are impacted by their operation processes. New and original suggestions on separate and joint system safety and operation cost optimization are also described and future research is also outlined. Probabilistic modeling methods are used as the research methods. The proposed research procedures enable the determination of the safety function and risk function for the port oil terminal critical infrastructure and the maritime ferry technical system in both examined cases, based on the strictly exact statistical data about their operation processes and on the improved approximate evaluations of their components safety parameters through expert opinion methods that originate directly from the users of these systems. Other proposed practically significant safety and resilience indicators are the mean lifetime up to the exceeding of a critical safety state, the moment when the risk function value exceeds the acceptable safety level, the intensity of ageing/degradation in both cases, the coefficient of operation process impact on system safety, and the coefficient of system resilience to operation process impact in the second case. As a result of this research, it is originally found that the proposed cost optimization procedures and the finding of the corresponding system safety indicators deliver an important possibility for the system total operation cost minimizing and keep fixed the corresponding conditional safety indicators during the operation. It was also established that the proposed system safety optimization procedures, and corresponding system operation total costs, deliver an important possibility for the system safety indicators maximization and keep fixed the corresponding system operation total costs during the operation.
PL
Możliwości zastosowania sztucznej inteligencji w sektorze energetycznym są dziś szerokie. Ogromna ilość danych przechodzących przez ten sektor stwarza potrzebę wdrażania automatycznej, inteligentnej analizy oraz potencjał rozwoju tych technologii. Chcąc zapewnić bezpieczeństwo energetyczne rozumiane jako zapewnienie ciągłości dostaw energii i paliw, należy mieć pełną kontrolę nad ich dystrybucją i możliwymi zagrożeniami. Korzyści płynące z kontroli nad danymi, prognozowania kluczowych w tym sektorze wartości czy optymalizacji działań i operacji na sieci są nieocenione. Celem niniejszego artykułu jest przegląd konkretnych obszarów energetyki, w których metody obliczeniowe i sztuczna inteligencja mają największy potencjał. Ponadto, wskazanie konkretnych metod, które sprawdzone w innych sektorach lub zbadane w nauce mają zastosowanie również tutaj.
EN
The possibilities for using artificial intelligence in the energy sector are vast today. The massive amount of data passing through this sector creates the need to implement automatic, intelligent analysis and the potential for developing these technologies. In order to ensure energy security, understood as ensuring the continuity of energy and fuel supplies, it is necessary to have complete control over their distribution and possible threats. The benefits of controlling data, forecasting critical values in this sector, or optimizing activities and operations on the network are invaluable. The purpose of this article is to review specific areas of the energy sector where computational methods and artificial intelligence have the most significant potential. In addition, specific methods that have been proven in other sectors or studied in science are indicated to apply here.
17
EN
The form of modern guitars were shaped by Spanish luthiers in the XIX century. Especially Antonio de Torres Jurado is the one, whose designs are an inspiration for modern constructions. From the very beginning, guitars are struggling with not sufficient sound levels for all the desired applications. Apart from electroacoustic amplification, there were several attempts to modify the construction of the sound hole or the soundboard. Higher sound pressure levels were often connected with distorted sound, sometimes not acceptable to musicians. In this paper, inequalities in the frequency characteristics of the sound generated by the guitar with modern sound holes are presented. Resonant frequencies of the soundboard were pointed as being responsible for the too high amplitude of sound in the 600-800 Hz frequency range. Using optimization and finite element method modelling, the best patterns of bracings were proposed to equalize the frequency spectrum and improve the sound of the instrument.
EN
It is essential to check whether the surgical robot end effector is safe to use due to phenomena such as linear buckling and mechanical resonance. The aim of this research is to build an multi criteria optimization model based on such criteria as the first natural frequency, buckling factor and mass, with the assumption of the basic constraint in the form of a safety factor. The calculations are performed for a serial structure of surgical robot end effector with six degrees of freedom ended with a scalpel. The calculation model is obtained using the finite element method. The issue of multi-criteria optimization is solved based on the response surface method, Pareto fronts and the genetic algorithm. The results section illustrates deformations of a surgical robot end effector occurring during the resonance phenomenon and the buckling deformations for subsequent values of the buckling coefficients. The dependencies of the geometrical dimensions on the criteria are illustrated with the continuous functions of the response surface, i.e. metamodels. Pareto fronts are illustrated, based on which the genetic algorithm finds the optimal quantities of the vector function. The conducted analyzes provide a basis for selecting surgical robot end effector drive systems from the point of view of their generated inputs.
EN
Precise and efficient localization of sound sources is essential in many applications. Traditionally, methods that use beamforming tend to scan the entire space with fixed level of precision. Although effective, this approach is inefficient when searching for a single source. In this paper we propose an iterative algorithm for localizing a single sound source utilizing signals from a 4th order ambisonic microphone array. Two beamformers were implemented: one based on signals in A-format, incorporating delay-and-sum method, commonly used for sound source localization, and the second one based on B-format, operating in the spherical harmonic domain. By utilizing an iterative algorithm, we have significantly decreased the number of points to be evaluated to localize the sound source. For the delay-and-sum beamformer, the best outcome was obtained by using all 32 channels in every iteration. For the spherical-harmonics-based beamformer, the best strategy was to use first-order harmonics in the initial iteration and fourth-order harmonics in subsequent iterations.
EN
This article presents a Train on Railway Track simulation model and program developed by the authors. The model implements the module for multiple-criteria optimization with a set of proposed objective functions allowing reductions in train passing time, total costs, energy consumption, and adverse environmental impacts. The Train on Railway Track simulator has been developed to allow both the simulation itself and the ride optimization. The main achievement is the development of an algorithm that simulates the passage of a train over 500 km, the duration of which did not exceed two minutes. We present an analysis of the impact of model changes on the duration of the simulation and the accuracy of the results obtained. This allows the use of these achievements in simulations carried out for the railway, automotive, or aviation industries as well. Changes in the classical approach to optimization proposed by the authors made it possible to obtain results directly by solving classical systems of equations. The change in the approach to the optimization and system algorithm has reduced the operating time of the optimization system from thousands of simulations to a single simulation with an additional optimization process that takes several minutes to calculate. This article is a continuation of the description of the work performed, and basic information about the developed simulation model and software functionality is included in a separate publication [13].
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