The article is devoted to the development of a software controller for automation of access to tools and object model of the multifunctional graphic editor Adobe Photoshop. The work of the graphic editor is initiated in the form of a COM object, which contains methods available to the software controller through the COM interface, which allows the software to use the functionality of the editor. To restrict unauthorized access, a software authorization control protocol is proposed, which is based on the use of binding to the computer hardware and encryption by a 128-bit MD5 public key hashing algorithm.
{"title":"INTEGRATION WITH THE SOFTWARE INTERFACE OF THE COM SERVER FOR AUTHORIZED USER","authors":"D. Ratov","doi":"10.35784/acs-2021-09","DOIUrl":"https://doi.org/10.35784/acs-2021-09","url":null,"abstract":"The article is devoted to the development of a software controller for automation of access to tools and object model of the multifunctional graphic editor Adobe Photoshop. The work of the graphic editor is initiated in the form of a COM object, which contains methods available to the software controller through the COM interface, which allows the software to use the functionality of the editor. To restrict unauthorized access, a software authorization control protocol is proposed, which is based on the use of binding to the computer hardware and encryption by a 128-bit MD5 public key hashing algorithm.","PeriodicalId":36379,"journal":{"name":"Applied Computer Science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43696052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Saha Reno, Sheikh Surfuddin Reza Ali Chowdhury, Iqramuzzaman Sadi
Lending systems in real world are not much secure and reliable as the borrower and third parties involved in this aspect may create various deceitful situations. Blockchain is a secure system where the utilization of smart contract can avoid deceptive phenomena involved in lending but the decline in exchange rate of cryptocurrency can create the opportunity to pay back less than the borrowed amount in terms of fiat money. In this paper, a blockchain and smart contract-based lending framework is designed which requires the borrower to provide Ethereum Request for Comments (ERC)-20 standard tokens as collateral to mitigate the associated risks. The smart contract feature is utilized to automate the system without any third-party management. Besides, transaction stored in the blocks creates transparency among the users of the system. To tackle the aforementioned issues, ERC-20 token value is increased periodically and the instability of the exchange rate is surveilled by the system. By the end of this paper, some test cases and charts relevant to the data set are evaluated to assess the effectiveness of the system.
{"title":"MITIGATING LOAN ASSOCIATED FINANCIAL RISK USING BLOCKCHAIN BASED LENDING SYSTEM","authors":"Saha Reno, Sheikh Surfuddin Reza Ali Chowdhury, Iqramuzzaman Sadi","doi":"10.35784/acs-2021-16","DOIUrl":"https://doi.org/10.35784/acs-2021-16","url":null,"abstract":"Lending systems in real world are not much secure and reliable as the borrower and third parties involved in this aspect may create various deceitful situations. Blockchain is a secure system where the utilization of smart contract can avoid deceptive phenomena involved in lending but the decline in exchange rate of cryptocurrency can create the opportunity to pay back less than the borrowed amount in terms of fiat money. In this paper, a blockchain and smart contract-based lending framework is designed which requires the borrower to provide Ethereum Request for Comments (ERC)-20 standard tokens as collateral to mitigate the associated risks. The smart contract feature is utilized to automate the system without any third-party management. Besides, transaction stored in the blocks creates transparency among the users of the system. To tackle the aforementioned issues, ERC-20 token value is increased periodically and the instability of the exchange rate is surveilled by the system. By the end of this paper, some test cases and charts relevant to the data set are evaluated to assess the effectiveness of the system.","PeriodicalId":36379,"journal":{"name":"Applied Computer Science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44658246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Coronavirus Disease 2019 (COVID-19) has caused massive infections and death toll. Radiological imaging in chest such as computed tomography (CT) has been instrumental in the diagnosis and evaluation of the lung infection which is the common indication in COVID-19 infected patients. The technological advances in artificial intelligence (AI) furthermore increase the performance of imaging tools and support health professionals. CT, Positron Emission Tomography – CT (PET/CT), X-ray, Magnetic Resonance Imaging (MRI), and Lung Ultrasound (LUS) are used for diagnosis, treatment of COVID-19. Applying AI on image acquisition will help automate the process of scanning and providing protection to lab technicians. AI empowered models help radiologists and health experts in making better clinical decisions. We review AI-empowered medical imaging characteristics, image acquisition, computer-aided models that help in the COVID-19 diagnosis, management, and follow-up. Much emphasis is on CT and X-ray with integrated AI, as they are first choice in many hospitals.
{"title":"A SURVEY OF AI IMAGING TECHNIQUES FOR COVID-19 DIAGNOSIS AND PROGNOSIS","authors":"K. K. P. Tellakula, R. S. Kumar, S. Deb","doi":"10.23743/acs-2021-12","DOIUrl":"https://doi.org/10.23743/acs-2021-12","url":null,"abstract":"The Coronavirus Disease 2019 (COVID-19) has caused massive infections and death toll. Radiological imaging in chest such as computed tomography (CT) has been instrumental in the diagnosis and evaluation of the lung infection which is the common indication in COVID-19 infected patients. The technological advances in artificial intelligence (AI) furthermore increase the performance of imaging tools and support health professionals. CT, Positron Emission Tomography – CT (PET/CT), X-ray, Magnetic Resonance Imaging (MRI), and Lung Ultrasound (LUS) are used for diagnosis, treatment of COVID-19. Applying AI on image acquisition will help automate the process of scanning and providing protection to lab technicians. AI empowered models help radiologists and health experts in making better clinical decisions. We review AI-empowered medical imaging characteristics, image acquisition, computer-aided models that help in the COVID-19 diagnosis, management, and follow-up. Much emphasis is on CT and X-ray with integrated AI, as they are first choice in many hospitals.","PeriodicalId":36379,"journal":{"name":"Applied Computer Science","volume":"77 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41289837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Prof. Mohammed Abdalla Hussein, Ekhlas H. Karam, R. S. Habeeb
The rapid and uncontrollable cell division that spreads to surrounding tissues medically termed as malignant neoplasm, cancer is one of the most common diseases worldwide. The need for effective cancer treatment arises due to the increase in the number of cases and the anticipation of higher levels in the coming years. Oncolytic virotherapy is a promising technique that has shown encouraging results in several cases. Mathematical models of virotherapy have been widely developed, and one such model is the interaction between tumor cells and oncolytic virus. In this paper an artificially optimized Immune- Linear Quadratic Regulator (LQR) is introduced to improve the outcome of oncolytic virotherapy. The control strategy has been evaluated in silico on number of subjects. The crow search algorithm is used to tune immune and LQR parameters. The study is conducted on two subjects, S1 and S3, with LQR and Immune-LQR. The experimental results reveal a decrease in the number of tumor cells and remain in the treatment area from day ten onwards, this indicates the robustness of treatment strategies that can achieve tumor reduction regardless of the uncertainty in the biological parameters.
{"title":"CANCER GROWTH TREATMENT USING IMMUNE LINEAR QUADRATIC REGULATOR BASED ON CROW SEARCH OPTIMIZATION ALGORITHM","authors":"Prof. Mohammed Abdalla Hussein, Ekhlas H. Karam, R. S. Habeeb","doi":"10.35784/acs-2021-13","DOIUrl":"https://doi.org/10.35784/acs-2021-13","url":null,"abstract":"The rapid and uncontrollable cell division that spreads to surrounding tissues medically termed as malignant neoplasm, cancer is one of the most common diseases worldwide. The need for effective cancer treatment arises due to the increase in the number of cases and the anticipation of higher levels in the coming years. Oncolytic virotherapy is a promising technique that has shown encouraging results in several cases. Mathematical models of virotherapy have been widely developed, and one such model is the interaction between tumor cells and oncolytic virus. In this paper an artificially optimized Immune- Linear Quadratic Regulator (LQR) is introduced to improve the outcome of oncolytic virotherapy. The control strategy has been evaluated in silico on number of subjects. The crow search algorithm is used to tune immune and LQR parameters. The study is conducted on two subjects, S1 and S3, with LQR and Immune-LQR. The experimental results reveal a decrease in the number of tumor cells and remain in the treatment area from day ten onwards, this indicates the robustness of treatment strategies that can achieve tumor reduction regardless of the uncertainty in the biological parameters.","PeriodicalId":36379,"journal":{"name":"Applied Computer Science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48416194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Manikandan Sridharan, Delphin Carolina RANI ARULANANDAM, R. K. Chinnasamy, Suma Thimmanna, Sivabalaselvamani Dhandapani
This paper proposes a deep learning approach to recognize Tamil Letter from images which contains text. This is recognition process, the text in the images are divided to letter or characters. Each recognized letters are sending to recognition system and filter the text using deep learning algorithms. Our proposed algorithm is used to separate letter from the text using convolution neural network approach. The filtering system is used for identifying font based on that letters are found. The Tamil letters are test data and loaded in recognition systems. The trained data are input which contains filtered letter from image. For example, Tamil letters such as are available in test dataset. The trained data are applied into deep convolution neural network process. The two dataset are created which contains test data with Tamil letter and second one for recognized input data or trained data. 15 thousands of letters are taken and 512 X 512 X 3 size deep convolution network is created with font and letters. As the result, 85% Tamil letters are recognized and 82% are tested using font. TensorFlow is used for testing the accuracy and success rate.
本文提出了一种深度学习方法从包含文本的图像中识别泰米尔字母。这是识别过程，将图像中的文字划分为字母或字符。每个被识别的字母被发送到识别系统，并使用深度学习算法过滤文本。该算法采用卷积神经网络方法实现了字母与文本的分离。过滤系统用于根据找到的字母来识别字体。泰米尔字母是测试数据，并加载到识别系统中。训练后的数据输入包含从图像中过滤的字母。例如，泰米尔字母如在测试数据集中可用。将训练后的数据应用于深度卷积神经网络处理。创建了两个数据集，其中包含带有泰米尔字母的测试数据，第二个数据集用于识别输入数据或训练数据。取1.5万个字母，用字体和字母创建512 X 512 X 3大小的深度卷积网络。结果，85%的泰米尔字母被识别，82%的泰米尔字母使用字体进行测试。TensorFlow用于测试准确率和成功率。
{"title":"RECOGNITION OF FONT AND TAMIL LETTER IN IMAGES USING DEEP LEARNING","authors":"Manikandan Sridharan, Delphin Carolina RANI ARULANANDAM, R. K. Chinnasamy, Suma Thimmanna, Sivabalaselvamani Dhandapani","doi":"10.35784/acs-2021-15","DOIUrl":"https://doi.org/10.35784/acs-2021-15","url":null,"abstract":"This paper proposes a deep learning approach to recognize Tamil Letter from images which contains text. This is recognition process, the text in the images are divided to letter or characters. Each recognized letters are sending to recognition system and filter the text using deep learning algorithms. Our proposed algorithm is used to separate letter from the text using convolution neural network approach. The filtering system is used for identifying font based on that letters are found. The Tamil letters are test data and loaded in recognition systems. The trained data are input which contains filtered letter from image. For example, Tamil letters such as are available in test dataset. The trained data are applied into deep convolution neural network process. The two dataset are created which contains test data with Tamil letter and second one for recognized input data or trained data. 15 thousands of letters are taken and 512 X 512 X 3 size deep convolution network is created with font and letters. As the result, 85% Tamil letters are recognized and 82% are tested using font. TensorFlow is used for testing the accuracy and success rate.","PeriodicalId":36379,"journal":{"name":"Applied Computer Science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48094603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Controlling the contact force between the pantograph and the catenary has come to be a requirement for improving the performances and affectivity of high-speed train systems Indeed, these performances can also significantly be decreased due to the fact of the catenary equal stiffness variation. In addition, the contact force can also additionally differ and ought to end up null, which may additionally purpose the loss of contact. Then, in this paper, we current an active manipulate of the minimize order model of pantograph-catenary system .The proposed manipulate approach implements an optimization technique, like particle swarm (PSO), the usage of a frequent approximation of the catenary equal stiffness. All the synthesis steps of the manipulate law are given and a formal evaluation of the closed loop stability indicates an asymptotic monitoring of a nominal steady contact force. Then, the usage of Genetic Algorithm with Proportional-Integral-derivative (G.A-PID) as proposed controller appeared optimum response where, the contacts force consequences to be virtually equal to its steady reference. Finally it seems the advantageous of suggestion approach in contrast with classical manipulate strategies like, Internal mode control(IMC) method, linear quadratic regulator (LQR).The outcomes via the use of MATLAB simulation, suggests (G.A-PID) offers better transient specifications in contrast with classical manipulate.
{"title":"GENETIC ALGORITHM-PID CONTROLLER FOR MODEL ORDER REDUCTION PANTOGRAPHCATENARY SYSTEM","authors":"N. A. Al-awad, Izz K. Abboud, M. Al-Rawi","doi":"10.23743/ACS-2021-11","DOIUrl":"https://doi.org/10.23743/ACS-2021-11","url":null,"abstract":"Controlling the contact force between the pantograph and the catenary has come to be a requirement for improving the performances and affectivity of high-speed train systems Indeed, these performances can also significantly be decreased due to the fact of the catenary equal stiffness variation. In addition, the contact force can also additionally differ and ought to end up null, which may additionally purpose the loss of contact. Then, in this paper, we current an active manipulate of the minimize order model of pantograph-catenary system .The proposed manipulate approach implements an optimization technique, like particle swarm (PSO), the usage of a frequent approximation of the catenary equal stiffness. All the synthesis steps of the manipulate law are given and a formal evaluation of the closed loop stability indicates an asymptotic monitoring of a nominal steady contact force. Then, the usage of Genetic Algorithm with Proportional-Integral-derivative (G.A-PID) as proposed controller appeared optimum response where, the contacts force consequences to be virtually equal to its steady reference. Finally it seems the advantageous of suggestion approach in contrast with classical manipulate strategies like, Internal mode control(IMC) method, linear quadratic regulator (LQR).The outcomes via the use of MATLAB simulation, suggests (G.A-PID) offers better transient specifications in contrast with classical manipulate.","PeriodicalId":36379,"journal":{"name":"Applied Computer Science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41633261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper the method of improving production control in engineer to order [ETO] small and medium sized enterprises is presented. Briefly, the strategy of Mass Customization [MC] and a concept of the hybrid MC-ETO production system are demonstrated. Thereafter, a method of choosing components for small batch manu-facturing in advance, under conditions of single unit ETO production system, with application of fuzzy logic is described. This approach can be used in ETO companies during their transition into the hybrid MC-ETO production systems. The research was done in a collaboration with experts from the real ETO pro-duction system, in Polish SME, which manufactures mechanical parts.
{"title":"PRACTICAL APPLICATION OF FUZZY LOGIC IN PRODUCTION CONTROL SYSTEMS OF ENGINEER TO ORDER SMES","authors":"Bartosz Cieśla, J. Mleczko","doi":"10.35784/acs-2021-02","DOIUrl":"https://doi.org/10.35784/acs-2021-02","url":null,"abstract":"In this paper the method of improving production control in engineer to order [ETO] small and medium sized enterprises is presented. Briefly, the strategy of Mass Customization [MC] and a concept of the hybrid MC-ETO production system are demonstrated. Thereafter, a method of choosing components for small batch manu-facturing in advance, under conditions of single unit ETO production system, with application of fuzzy logic is described. This approach can be used in ETO companies during their transition into the hybrid MC-ETO production systems. The research was done in a collaboration with experts from the real ETO pro-duction system, in Polish SME, which manufactures mechanical parts.","PeriodicalId":36379,"journal":{"name":"Applied Computer Science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49534237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The changes caused by Industry 4.0 determine the decisions taken by manufacturing companies. Their activities are aimed at adapting processes and products to dynamic market requirements. Additive manufacturing technologies (AM) are the answer to the needs of enterprises. The implementation of AM technology brings many benefits, although for most 3D printing techniques it is also relatively expensive. Therefore, the implementation process should be preceded by an appropriate analysis, in order, finally, to assess the solution. This article presents the concept of using the Bayesian network when planning the implementation of AM technology. The use of the presented model allows the level of the success of the implementation of selected AM technology, to be estimated under given environmental conditions.
{"title":"ASSESSMENT OF THE POSSIBILITY OF USING BAYESIAN NETS AND PETRI NETS IN THE PROCESS OF SELECTING ADDITIVE MANUFACTURING TECHNOLOGY IN A MANUFACTURING COMPANY","authors":"M. Topczak, Małgorzata Śliwa","doi":"10.23743/ACS-2021-01","DOIUrl":"https://doi.org/10.23743/ACS-2021-01","url":null,"abstract":"The changes caused by Industry 4.0 determine the decisions taken by manufacturing companies. Their activities are aimed at adapting processes and products to dynamic market requirements. Additive manufacturing technologies (AM) are the answer to the needs of enterprises. The implementation of AM technology brings many benefits, although for most 3D printing techniques it is also relatively expensive. Therefore, the implementation process should be preceded by an appropriate analysis, in order, finally, to assess the solution. This article presents the concept of using the Bayesian network when planning the implementation of AM technology. The use of the presented model allows the level of the success of the implementation of selected AM technology, to be estimated under given environmental conditions.","PeriodicalId":36379,"journal":{"name":"Applied Computer Science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48275097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The leaf is one of the plant organs, contains chlorophyll, and functions as a catcher of energy from sunlight which is used for photosynthesis. Perfect leaves are composed of three parts, namely midrib, stalk, and leaf blade. The way to identify the type of plant is to look at the shape of the leaf edges. The shape, color, and texture of a plant's leaf margins may influence its leaf veins, which in this vein morphology carry information useful for plant classification when shape, color, and texture are not noticeable. Humans, on the other hand, may fail to recognize this feature because they prefer to see plants solely based on leaf form rather than leaf margins and veins. This research uses the Wavelet method to denoise existing images in the dataset and the Convolutional Neural Network classifies through images. The results obtained using the Wavelet Convolutional Neural Network method are equal to 97.13%.
{"title":"PLANT CLASSIFICATION BASED ON LEAF EDGES AND LEAF MORPHOLOGICAL VEINS USING WAVELET CONVOLUTIONAL NEURAL NETWORK","authors":"Wulan Dewi, W. H. Utomo","doi":"10.35784/acs-2021-08","DOIUrl":"https://doi.org/10.35784/acs-2021-08","url":null,"abstract":"The leaf is one of the plant organs, contains chlorophyll, and functions as a catcher of energy from sunlight which is used for photosynthesis. Perfect leaves are composed of three parts, namely midrib, stalk, and leaf blade. The way to identify the type of plant is to look at the shape of the leaf edges. The shape, color, and texture of a plant's leaf margins may influence its leaf veins, which in this vein morphology carry information useful for plant classification when shape, color, and texture are not noticeable. Humans, on the other hand, may fail to recognize this feature because they prefer to see plants solely based on leaf form rather than leaf margins and veins. This research uses the Wavelet method to denoise existing images in the dataset and the Convolutional Neural Network classifies through images. The results obtained using the Wavelet Convolutional Neural Network method are equal to 97.13%. ","PeriodicalId":36379,"journal":{"name":"Applied Computer Science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49106234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Information technologies allow for improving production systems functioning especially thanks to a possibility of solving complex production problems in a very short time. The production system designing is increasingly based on virtual reality, and more specifically on the concept of a digital factory. It enables to create virtual models of real objects and use them for visualization of products and manufacturing processes. The presented examples of new information technologies, which are used in production practice, are the main object of this paper.
{"title":"VIRTUAL REALITY IN PRODUCTION LAYOUT DESIGNING","authors":"D. Plinta, Karolina Kłaptocz","doi":"10.35784/acs-2021-06","DOIUrl":"https://doi.org/10.35784/acs-2021-06","url":null,"abstract":"Information technologies allow for improving production systems functioning especially thanks to a possibility of solving complex production problems in a very short time. The production system designing is increasingly based on virtual reality, and more specifically on the concept of a digital factory. It enables to create virtual models of real objects and use them for visualization of products and manufacturing processes. The presented examples of new information technologies, which are used in production practice, are the main object of this paper.","PeriodicalId":36379,"journal":{"name":"Applied Computer Science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46379645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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