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Indoor positioning system based on bluetooth low energy technology and a nature-inspired optimization algorithm
Primož Bencak, Darko Hercog, Tone Lerher, 2022, original scientific article

Abstract: Warehousing is one of the most important activities in the supply chain, enabling competitive advantage. Effective management of warehousing processes is, therefore, crucial for achieving minimal costs, maximum efficiency, and overall customer satisfaction. Warehouse Management Systems (WMS) are the first steps towards organizing these processes; however, due to the human factor involved, information on products, vehicles and workers may be missing, corrupt, or misleading. In this paper, a cost-effective Indoor Positioning System (IPS) based on Bluetooth Low Energy (BLE) technology is presented for use in Intralogistics that works automatically, and therefore minimizes the possibility of acquiring incorrect data. The proposed IPS solution is intended to be used for supervising order-picker movements, movement of packages between workstations, and tracking other mobile devices in a manually operated warehouse. Only data that are accurate, reliable and represent the actual state of the system, are useful for detailed material flow analysis and optimization in Intralogistics. Using the developed solution, IPS technology is leveraged to enhance the manually operated warehouse operational efficiency in Intralogistics. Due to the hardware independence, the developed software solution can be used with virtually any BLE supported beacons and receivers. The results of IPS testing in laboratory/office settings show that up to 98% of passings are detected successfully with time delays between approach and detection of less than 0.5 s.
Keywords: warehousing, indoor positioning systems, bluetooth low energy, particle swarm optimization, nature–inspired algorithm, intralogistics
Published in DKUM: 17.08.2023; Views: 348; Downloads: 33
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Empirical modeling of liquefied nitrogen cooling impact during machining Inconel 718
Matija Hriberšek, Lucijano Berus, Franci Pušavec, Simon Klančnik, 2020, original scientific article

Abstract: This paper explains liquefied nitrogen’s cooling ability on a nickel super alloy called Inconel 718. A set of experiments was performed where the Inconel 718 plate was cooled by a moving liquefied nitrogen nozzle with changing the input parameters. Based on the experimental data, the empirical model was designed by an adaptive neuro-fuzzy inference system (ANFIS) and optimized with the particle swarm optimization algorithm (PSO), with the aim to predict the cooling rate (temperature) of the used media. The research has shown that the velocity of the nozzle has a significant impact on its cooling ability, among other factors such as depth and distance. Conducted experimental results were used as a learning set for the ANFIS model’s construction and validated via k-fold cross-validation. Optimization of the ANFIS’s external input parameters was also performed with the particle swarm optimization algorithm. The best results achieved by the optimized ANFIS structure had test root mean squared error (test RMSE) = 0.2620, and test R$^2$ = 0.8585, proving the high modeling ability of the proposed method. The completed research contributes to knowledge of the field of defining liquefied nitrogen’s cooling ability, which has an impact on the surface characteristics of the machined parts.
Keywords: cryogenic machining, cooling impact, Inconel 718, machine learning, adaptive neuro-fuzzy inference system, particle swarm optimization
Published in DKUM: 14.07.2023; Views: 401; Downloads: 15
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Prediction of dimensional deviation of workpiece using regression, ANN and PSO models in turning operation
David Močnik, Matej Paulič, Simon Klančnik, Jože Balič, 2014, original scientific article

Abstract: As manufacturing companies pursue higher-quality products, they spend much of their efforts monitoring and controlling dimensional accuracy. In the present work for dimensional deviation prediction of workpiece in turning 11SMn30 steel, the conventional deterministic approach, such as multiple linear regression and two artificial intelligence techniques, back-propagation feed-forward artificial neural network (ANN) and particle swarm optimization (PSO) have been used. Spindle speed, feed rate, depth of cut, pressure of cooling lubrication fluid and number of produced parts were taken as input parameters and dimensional deviation of workpiece as an output parameter. Significance of a single parameter and their interactive influences on dimensional deviation were statistically analysed and values predicted from regression, ANN and PSO models were compared with experimental results to estimate prediction accuracy. A predictive PSO based model showed better predictions than two remaining models. However, all three models can be used for the prediction of dimensional deviation in turning.
Keywords: artificial neural network, dimensional dviation, particle swarm optimization, regression
Published in DKUM: 12.07.2017; Views: 1154; Downloads: 153
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Resolution of the stochastic strategy spatial prisoner's dilemma by means of particle swarm optimization
Jianlei Zhang, Chunyan Zhang, Tianguang Chu, Matjaž Perc, 2011, original scientific article

Abstract: We study the evolution of cooperation among selfish individuals in the stochastic strategy spatial prisoner's dilemma game. We equip players with the particle swarm optimization technique, and find that it may lead to highly cooperative states even if the temptations to defect are strong. The concept of particle swarm optimization was originally introduced within a simple model of social dynamics that can describe the formation of a swarm, i.e., analogous to a swarm of bees searching for a food source. Essentially, particle swarm optimization foresees changes in the velocity profile of each player, such that the best locations are targeted and eventually occupied. In our case, each player keeps track of the highest payoff attained within a local topological neighborhood and its individual highest payoff. Thus, players make use of their own memory that keeps score of the most profitable strategy in previous actions, as well as use of the knowledge gained by the swarm as a whole, to find the best available strategy for themselves and the society. Following extensive simulations of this setup, we find a significant increase in the level of cooperation for a wide range of parameters, and also a full resolution of the prisoner's dilemma. We also demonstrate extreme efficiency of the optimization algorithm when dealing with environments that strongly favor the proliferation of defection, which in turn suggests that swarming could be an important phenomenon by means of which cooperation can be sustained even under highly unfavorable conditions. We thus present an alternative way of understanding the evolution of cooperative behavior and its ubiquitous presence in nature, and we hope that this study will be inspirational for future efforts aimed in this direction.
Keywords: cooperation, prisoner's dilemma, particle swarm optimization, stochastic strategies
Published in DKUM: 19.06.2017; Views: 1171; Downloads: 377
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Particle swarm optimization for automatic creation of complex graphic characters
Iztok Fister, Matjaž Perc, Karin Fister, Salahuddin M. Kamal, Andres Iglesias, Iztok Fister, 2015, original scientific article

Abstract: Nature-inspired algorithms are a very promising tool for solving the hardest problems in computer sciences and mathematics. These algorithms are typically inspired by the fascinating behavior at display in biological systems, such as bee swarms or fish schools. So far, these algorithms have been applied in many practical applications. In this paper, we present a simple particle swarm optimization, which allows automatic creation of complex two-dimensional graphic characters. The method involves constructing the base characters, optimizing the modifications of the base characters with the particle swarm optimization algorithm, and finally generating the graphic characters from the solution. We demonstrate the effectiveness of our approach with the creation of simple snowman, but we also outline in detail how more complex characters can be created.
Keywords: optimizacija roja, kompleksni sistem, kaos, particle swarm optimization, complex system, graphics, chaos
Published in DKUM: 07.04.2017; Views: 1582; Downloads: 98
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Optimiranje značilnih parametrov frezanja z uporabo razvojne tehnike optimizacije jate delcev
Uroš Župerl, Franc Čuš, Valentina Gečevska, 2007, original scientific article

Abstract: Izbira rezalnih parametrov je najpomembnejši korak pri postopku načrtovanja proizvodnje, zato izdelamo novo tehniko razvojnega računanja za optimiranje procesa odrezovanja. V prispevku je uporabljena tehnika, ki oponaša dinamiko delcev v velikih skupinah (optimizacija PSO), za učinkovito in simultano optimiranje postopkov frezanja. V omenjenih postopkih smo soočeni s problemom več ciljnih dejavnikov. Najprej uporabimo umetno nevronsko mrežo (UNM) za napovedovanje rezalnih sil, nato z algoritmom PSO pridobimo optimalno rezalno hitrost in podajanja. Cilj optimizacije je, ob upoštevanju omejitev, določiti ekstrem ciljne funkcije (napovedna površina največjih sil). Med optimizacijo delci, s svojo inteligenco, letijo po prostoru rešitev in iščejo optimalne rezalne pogoje po strategiji algoritma PSO. Rezultati pokažejo, da je integriran sistem nevronskih mrež in kolektivne inteligence učinkovita metoda pri reševanju večciljnih optimizacijskih problemov. Njena velika učinkovitost na širokem območju rezalnih parametrov potrjuje, da sistem lahko praktično uporabimo v proizvodnji. Rezultati simulacij nakazujejo, da predlagan algoritem v primerjavi z rodovnimi algoritmi (GA) in simulacijskim (SA) ohlajanjem lahko poveča natančnost rešitve in konvergenco postopka. Nova tehnika razvojnega računanja ima nekoliko prednosti ter koristi in je primerna za uporabo v kombinaciji z modeli na osnovi umetnih nevronskih vezij, pri katerih niso na voljo izrecne relacije med vhodi in izhodi. Raziskava odpre vrata na področju obdelave z odrezovanjem za nov razred optimizacijskih tehnik, ki slonijo na razvojnem računanju.
Keywords: odrezovanje, končno frezanje, rezalni parametri, nevronske mreže, razvojne tehnike, optimizacija jate delcev, cutting, end-milling, cutting parameters, neural networks, evolution techniques, particle swarm optimization
Published in DKUM: 10.07.2015; Views: 1501; Downloads: 39
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Post insulator optimization based on dynamic population size
Peter Kitak, Arnel Glotić, Igor Tičar, 2012, published scientific conference contribution

Abstract: This paper suggests the use of dynamic population size throughout the optimization process which is applied on the numerical model of a medium voltage post insulator. The main objective of the dynamic population is reducing population size, to achieve faster convergence. Change of population size can be done in any iteration by proposed method. The multiobjective optimization process is based on the PSO algorithm, which is suitably modifiedin order to operate with the principle of the optimal Pareto front.
Keywords: dynamic population size, insulation elements, multi-objective optimization, particle swarm optimization
Published in DKUM: 10.07.2015; Views: 1515; Downloads: 31
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