Wydawnictwa Uczelniane / TUL Press
Stały URI zbioruhttp://hdl.handle.net/11652/17
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Pozycja On the relationship between urban form and amenities: A new perspective from Qom (Iran)(Lodz University of Technology Press, 2023) Venerandi Alessandro; Zamani Vahid; Porta SergioAmenities are fundamental for urban life as they promote socio-economic interactions and enhance city dynamics. Previous studies investigated the relationship between metrics of street network centrality and urban amenities. However, they hardly focused on further aspects of the built environment. A further drawback is that relationships were mainly assessed through linear models even though more complex and non-linear relationships plausibly exist. In this work, we, first, comprehensively describe the urban form of our case study, the city of Qom (Iran), through a set of 55 morphometrics computed at the plot level; second, we investigate the relationship between these metrics and density of amenities, through a set of machine learning techniques that handle non-linear behaviours. The best model explains up to 45% of the variance of the density measure, with coverage ratio, plot size, floor area ratio, street canyon width, and betweenness centrality being the top five explanatory factors. While the findings of this work do not have universal value, the methodology can be replicated to explore the same research question in different contexts. It can also be used as an evidence-based tool to inform design choices in urban redevelopment affecting the location of amenities in cities.Pozycja Comparison analysis on typical historic cultural districts with AI machine learning technology – Taking Portuguese and Macao districts as examples(Lodz University of Technology Press, 2023) Jiang Shan; Zheng Liang; Chen Yile; Zheng JianyiWith the rapid development of technology, artificial intelligence has gone into every field, and its development has been further expanded with machine learning as the core technology. How does this help urban analysis and urban form research? This study aims to introduce a new method for analysing and comparing urban morphological layouts using machine learning technology and to explore the possibility and potential of combining urban morphology analysis with machine learning technology. In this exploratory study, several typical Portuguese cities with historical and cultural characteristics are used as learning samples for comparison. Through the combination of urban morphology theory and machine learning, the urban morphological samples are clipped out from the Portuguese city maps, then morphological features are extracted from the samples, establishing training labels as typical Portuguese urban fabric, lastly compared the result with the typical urban areas of Macao using the YOLOv4 object detection algorithm. Through the research, it is found that Macao in the early stage is more morphologically similar to the city of Evora due to their same privilege; after the early 20th century, influence by contemporary Portuguese engineers and urban development strategy, Macao's urban morphology shows a higher degree of similarity to that of Lisbon.Pozycja Supporting Surgical Training with the Help of Computer Vision and Machine Learning Methods(Wydawnictwo Politechniki Łódzkiej, 2023) Forczmański, Paweł; Ryder, Yoonhee C; Mott, Nicole M; Gross, Christopher L.; Yu, Joon B.; Rooney, Deborah M.; Jeffcoach, David R.; Bidwell, Serena; Anidi, Chioma; Rosenthal, Lindsay; Kim, Grace J.The paper presents a novel concept of laparoscopic skills evaluation based on the automated analysis of videos recorded during simulationbased training exercises via an artificial intelligence algorithm. It has been tested on data collected during the training of actual surgeons. Its performance is promising, providing an opportunity to build an automatic system used mainly in developing countries.Pozycja Performance Analysis of Machine Learning Platforms Using Cloud Native Technology on Edge Devices(Wydawnictwo Politechniki Łódzkiej, 2023) Cłapa, Konrad; Grudzień, Krzysztof; Sierszeń, ArturThis article presents the results of an experiment performed on a machine learning edge computing platform composed of a virtualized environment with a K3s cluster and Kubeflow software. The study aimed to analyze the effectiveness of executing Kubeflow pipelines for simulated parallel executions. A benchmarking environment was developed for the experiment to allow system performance measurements based on parameters, including the number of pipelines and nodes. The results demonstrate the impact of the number of cluster nodes on computational time, revealing insights that could inform future decisions regarding increasing the effectiveness of running machine learning pipelines on edge devices.Pozycja Learning Non-Differentiable Graphs of Utility AI(Wydawnictwo Politechniki Łódzkiej, 2023) Świechowski, MaciejUtility AI is an approach to modelling AI players in computer games. Its structure is a graph that computes the utility values of possible actions and chooses the one with the highest value. Currently, such graphs are created by experts manually. This paper presents the first attempts to create them automatically – through learning from data. The problem is similar to training neural networks except that the utility graphs are non-differentiable and contain various types of nodes (more complex than neurons). We present the most promising methods, preliminary experiments and results.Pozycja Digital Twin for Training Set Generation for Unexploded Ordnance Classification(Wydawnictwo Politechniki Łódzkiej, 2023) Ściegienka, Piotr; Blachnik, MarcinThe use of machine learning methods for unexploded ordnance (UXO) detection and classification is very limited. This limitation derives from the lack of representative and enough large training data. To overcome this issue we propose a construction of a digital twin where UXO and non-UXO objects are represented using mathematical models in a simulated Earth magnetic field. The use of digital twins allows for simulating and collecting a large training set which can be used for training machine learning models. In the conducted research we discuss obtained results and point out several of the detected problems.Pozycja Carbon Footprint Reduction of a Petrochemical Process Supported by ML and Digital Twin modelling(Wydawnictwo Politechniki Łódzkiej, 2023) Kulikowski, Sławomir; Romanowski, Andrzej; Sierszeń, ArturThis article aims to present a concept of an Artificial Intelligence application in the form of pre-trained Machine Learning modules to reduce the carbon footprint of a chemical recycling process. Chemical recycling of plastic is energy-consuming as it requires relatively high temperatures and calibration cycles based on a constantly changing structure of raw materials. Due to that fact, complex process parameters must be tuned to allow the production of the required fraction of gasoline. In general, the designed IoT system enables a massive collection of technology and environmental data and the processing of parameters to feed the Digital Twin of a petrochemical plant. The scientific part of the project consists of Digital Twin modelling, experiments, simulations, and training of machine learning modules to predict the optimal set of production line parameters based on the specific structure of raw materials to reduce the number of calibrations and lower energy consumption indirectly which will lead to carbon footprint reduction. There is an here is an estimate that that deployed solution will allow reduction of energy consumption on a monthly level of 10-15% which could generate direct savings on a cost of energy but also savings in a field of carbon emission and related credits. The project also includes the evaluation of predictions supported by machine learning modules, training techniques and comparison to expert settings to assess the quality of the application.Pozycja Knowledge-based personalized pattern design of legging for mass customization using support vector machine modelling(Wydawnictwo Politechniki Łódzkiej, 2022) Wang, Zhujun; Xing, Yingmei; Tao, Xuyuan; Zeng, Xianyi; Bruniaux, Pascal; Xu, ZhenzhenIn this study, we proposed a knowledge-based intelligent approach for pattern design of personalized garment toward mass customization using support vector machine modelling. This approach has been described and validated in the scenario of personalized legging design. The proposed models have been set up by learning from quantitative relationships between garment structure lines and controlling points, and then simulated for pattern parameters prediction. Finally, the performance of the presented approach was compared with the traditional grading method. From the experimental results, the design effect of the proposed approach is equivalent to the existing grading method. It indicates that our approach provides a feasible and valuable solution for promoting the personalized garment pattern design and facilitating the process of garment mass customization.Pozycja Synergy of Convolutional Neural Networks and Geometric Active Contours(Wydawnictwo Politechniki Łódzkiej, 2021) Tomczyk, Arkadiusz; Pankiv, Oleksandr; Szczepaniak, Piotr S.Hybrid approach to machine learning techniques could potentially provide improvements in image segmentation results. In this paper, a model of cooperation of convolutional neural networks and geometric active contours is proposed and developed. The novelty of the approach lies in combining deep neural networks and active contour model in order to improve CNN output results. The method is examined on the image segmentation task and applied to the detection and extraction of nuclei of HL60 cell line. The model had been tested on both 2-D and 3-D images. Because of feature learning characteristics of convolutional neural networks, the proposed solution should perform well in multiple scenarios and can be considered generic.