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Potencjał wykorzystania sztucznej inteligencji (SI) jako wsparcie w projektowaniu i wizualizacji odzieży

Frasoński Tomasz

Przegląd Włókienniczy - Włókno, Odzież, Skóra

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2023

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nr 2

18--22

PL

Rozwój technologii cyfrowej w tym sztucznej inteligencji ulega ciągłemu rozwojowi. Dzięki temu procesowi poszerza się perspektywa zastosowania technologii oraz twórczych poszukiwań w obszarze sztucznej inteligencji (SI). Wzmożone zainteresowanie tym narzędziem nie ominęło branży przemysłu mody oraz innych dyscyplin kreatywnych. Wykorzystanie technik sztucznej inteligencji (SI) umożliwia szereg rozwiązań w procesach projektowania odzieży, planowania produkcji, optymalizacji w branży tekstylno-odzieżowej a także w branży e-commerce. W niniejszym artykule zostały przedstawione badania łączące sztuczną inteligencję, uwzględniając zbadanie skryptu generatywnego MidJourney w praktyce tworzenia modelu odzieży. Cyfrowa wizualizacja generowana przez sztuczną inteligencję umożliwia szybką prezentację trendów, koncepcji czy założeń projektowych odzieży i może znacznie przyśpieszyć proces prototypowania oraz podjęcie decyzji projektowej w przyszłości.

EN

The development of digital technology, including artificial intelligence, is constantly evolving. Thanks to this process, the prospect of using technology and engaging in the creative exploration of artificial intelligence (AI) is expanding. The increased interest in this tool has not spared the fashion industry or other creative disciplines. The use of artificial intelligence (AI) techniques enables several solutions in the processes of clothing design, production planning, and optimization in the textile and clothing industries, as well as in the e-commerce industry. This article presents research combining artificial intelligence, including examining the MidJourney generative script in creating a clothing model. Digital visualization generated by artificial intelligence enables the quick presentation of trends, concepts, or design assumptions for clothing. It can significantly speed up the prototyping process and inform design decisions in the future.

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Wynalazczość wspomagana sztuczną inteligencją a ochrona patentowa w prawie polskim i europejskim. Perspektywa porównawcza

Szkaradek Michał, Telenga Justyna

Pomiary Automatyka Robotyka

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2023

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R. 27, nr 4

79--84

PL

W artykule przedstawiono analizę złożonej relacji między sztuczną inteligencją (AI) a prawem patentowym. Jak twierdzą autorzy, chociaż wynalazki są generalnie uprawnione do ochrony patentowej, te w większości lub w pełni stworzone przez AI nie kwalifikują się do takiej ochrony w ramach obecnych polskich i europejskich regulacji. W artykule przytoczono definicję AI i przedstawiono kryteria zdolności patentowej, podkreślając, że wynalazki generowane przez AI stanowią wyzwanie dla istniejących ram prawnych.

EN

The article scrutinises the intricate interplay between artificial intelligence (AI) and patent jurisprudence. According to the authors, while inventions conventionally qualify for patent protection, those predominantly or entirely conceived by AI fall outside the purview of extant Polish and European legislative frameworks. The article delineates the parameters of AI and explicates the criteria for patentability, accentuating that inventions engendered by AI present a conundrum for the prevailing legal paradigms.

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Solar chargers based on new dye-based photovoltaic modules and new supercapacitors

Plebankiewicz Ireneusz, Bogdanowicz Krzysztof. A., Kwaśnicki Paweł, Skunik-Nuckowska Magdalena, Rączka Patryk M., Kulesza Paweł, Iwan Agnieszka, Przybył Wojciech

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2023

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Vol. 71, nr 4

art. no. e146452

EN

Electricity storage is one of the best-known methods of balancing the energy supply and demand at a given moment. The article presents an innovative solution for the construction of an electric energy storage device obtained from an innovative photovoltaic panel made of new dye-based photovoltaic modules and newly developed supercapacitors – which can be used as an emergency power source. In the paper, for the first time, we focused on the successful paring of new dye-sensitized solar cell (DSSC) with novel supercapacitors. In the first step, a microprocessor stand was constructed using Artificial Intelligence algorithms to control the parameters of the environment, as well as the solar charger composed of six DSSC cells with the dimensions of 100_100 mm and 126 CR2032 coin cells with a total capacitance of 60 F containing redox-active aqueous electrolyte. It was proven that the solar charger store enough energy to power, i.e. SOS transmitter or igniters, using a 5 V signal.

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Classification of parametric rolling for seagoing ships and percentage distribution for multiple sea state parameters

Chhoeung S., Hahn A.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

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2022

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Vol. 16 No. 4

769--777

EN

In this paper, parametric rolling is divided into 5 heavy grades, establishing a classification for parametric rolling. This is achieved by a multi-parameter application in the simulations. For this purpose, parametric rolling, established criteria and the state of the art are considered and the results are summarized. Based on this, parametric rolling is successfully simulated using discrete simulation in MARIN [1]. Five heavy grades are introduced so different classes of parametric rolling can be distinguished. Furthermore, dependencies and probabilities for the occurrence of parametric roles in relation to the see are determined numerically. This will later be used in an assistance system on board ships to compute the prediction of parametric roll using an AI.

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A Robust CNN Model for Diagnosis of COVID-19 Based on CT Scan Images and DL Techniques

Eldeeb Ahmed H., Amr Mohammed Nagah, Ibrahim Amin S., Kamel Hesham, Fouad Sara

International Journal of Electronics and Telecommunications

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2022

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Vol. 68. No. 4

731--739

EN

The 2019 Coronavirus (COVID-19) virus has caused damage on people's respiratory systems over the world. Computed Tomography (CT) is a faster complement for RT-PCR during peak virus spread times. Nowadays, Deep Learning (DL) with CT provides more robust and reliable methods for classifying patterns in medical pictures. In this paper, we proposed a simple low training proposed customized Convolutional Neural Networks (CNN) customized model based on CNN architecture that layers which are optionals may be included such as the layer of batch normalization to reduce time taken for training and a layer with a dropout to deal with overfitting. We employed a huge dataset of chest CT slices images from diverse sources COVIDx-CT, which consists of a 16,146-image dataset with 810 patients of various nationalities. The proposed customized model's classification results compared to the VGG-16, Alex Net, and ResNet50 Deep Learning models. The proposed CNN model shows robustness by achieving an overall accuracy of 93% compared to 88%, 89%, and 95% for the VGG-16, Alex Net, and ResNet50 DL models for the classification of 3 classes. When this relates to binary classification, the classification accuracy of the proposed model and the VGG-16 models were identical (almost 100% accurate), with 0.17% of misclassification in the class of Non-Covid-19, the Alex Net model achieved almost 100% classification accuracy with 0.33% misclassification in the class of Non-Covid-19. Finally, ResNet50 achieved 95% classification accuracy with 5% misclassification in the Non-Covid-19 class.

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Safety-Assisted Driving Technology Based on Artificial Intelligence and Machine Learning for Moving Vehicles in Vietnam

Vu Hong-Son, Nguyen Van-Hien

Annals of Computer Science and Information Systems

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2022

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Vol. 33

279--284

EN

ADAS (Advanced Driver Assistance Systems) plays an important role in building a safe and modern traffic system. For these systems, precise detection performance and response speed are critical. However, the detection of mobile vehicles is facing many difficulties due to the density of vehicles, the complex background scene in the city, etc. In addition, the detection and identification requirements respond in real time is also a challenge for current systems. This paper proposes a model using deep learning algorithms and artificial intelligence to increase accuracy and improve response speed for intelligent driving assistance systems. Accordingly, this paper proposes the YOLO (You Only Look One) model together with a sample data set collected and classified separately suitable for Vietnam traffic and our training algorithm. The experimental results were then performed on an NVIDIA Jetson TX2 embedded computer. The experimental results show that, the proposed method has increased the speed by at least 1.5 times with the detection rate reaching 79\% for the static camera system; and speed up at least 1.5x with a detection rate of 89\% for the dynamic camera system at 1280x720px high resolution images.

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State-of-the-Art Techniques in Artificial Intelligence for Continual Learning: A Review

Salami Bukola, Haataja Keijo, Toivanen Pekka

Annals of Computer Science and Information Systems

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2021

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Vol. 26

23--32

EN

Artificial neural networks are used in many state-of-the-art systems for perception, and they thrive at solving classification problems, but they lack the ability to transfer that learning to a new task. Human and animals both have the capability of acquiring knowledge and transfer them continually throughout their lifespan. This term is known as continual learning. Continual learning capabilities are important to Artificial Neural Network in the real world especially with the increasing stream of data. However, it remains a challenge to be achieved because they are prone to catastrophic forgetting. Fixing this problem is critical, so that ANN incrementally learn and improve when deployed to real life situations. In this paper, we did a taxonomy of continual learning first in human by introducing plasticity-stability dilemma and some other learning and forgetting process in the brain. We did a state-of-the-art review of three different approaches to continual learning to mitigate catastrophic forgetting.

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Framework of Using Machine Leaning Techniques in Human Resource Management in Vietnamese Companies

Thu Thuy Nguyen Thi, Nguyen Bich-Loan, Chi Trung Nguyen

Annals of Computer Science and Information Systems

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2021

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Vol. 28

295--300

EN

The COVID-19 pandemic requires many companies changing the way of their operations because of the lock down of movement and limitation of facing contact, etc. This also is an opportunity for companies to utilize technology and use digital space to keep their works and companies' activities. Companies can encourage their labors via policies of promotions. However, how to evaluate employee performance fairly, unbiasedly, and transparently? The existence of AI (Artificial Intelligence), especially machine learning, might be a solution for this due to the fact that it can automatically evaluate individual workers' performance. Therefore, the performance evaluation process ensures that the system works without interference from people outside of the system. In the paper, a framework of KPIs building systems to evaluate individual workers is represented as a solution for applying the advantage of AI (machine learning techniques) in Vietnamese companies. It can help them to improve the labor productivity and increase the workers' quality in the labor market in general.

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Data Mining for Bankruptcy Prediction: An Experiment in Vietnam

Hung Dang Ngoc, Binh Vu Thi Thanh

Annals of Computer Science and Information Systems

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2021

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Vol. 28

175--184

EN

In the history of the world economy, the bankruptcy of some large companies has caused global financial crises. The study aimed to postulate a model of bankruptcy prediction for listed companies on Vietnam's stock market. The research used six popular algorithms in data mining to predict bankruptcy risk with data collected from 4693 observations in the period 2009-2020. The research results showed that Logistic algorithms, Artificial Neural Network, Decision Tree have a high level of predicting bankruptcy with an accuracy of 98%. The study identified the three most important indicators: inventory turnover ratio, debt to equity ratio, and debt ratio that affect the corporate bankruptcy prediction. The study showed the threshold points of 10-indicators to avoid bankruptcy likelihood. These results recommended that the model could be applied in practice to reduce risks for businesses and investors in the Vietnamese market.

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Conception of 4-component architecture of information systems on example of Artificial Neural Networks

Gakh Dmitriy

Annals of Computer Science and Information Systems

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2021

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Vol. 26

159--166

EN

Nowadays Information Systems (IS) become more and more distributed, complex, and heterogeneous. Such nature of IS make them or their components a Black Box. Although classical software operates according understandable logic, modern complex software often shows non-determinism in its operation. Artificial Intelligence (AI) based on Artificial Neural Networks (ANN) is an example of such systems. This paper considers IS architecture consisting of 4 components, one of which represents non-determinism as an "Machine Intuition". The architecture is derived from 3-tier computer architecture and based on psychological findings. This approach allowed building a simple and user/developer friendly model. Practical value of the architecture is concluded in ability to better understand, design, and develop the IS containing units with non-deterministic behavior, deal with AI overfitting, underfitting, and threat problems. Architecture and principles represented in this paper can be applied not only to AI/ANN but different IS types.