Pub Date : 2022-05-16DOI: 10.1017/S089006042200004X
I. Horváth
Abstract Machlup used the words alpha, beta, and gamma to identify humanities, science, and social science as three distinct fields of academic learning and knowing, in addition to general knowledge. Gilles and Paquet identified a fourth type of disciplinary knowledge and labeled it as delta. This includes the knowledge of creative disciplines such as design, law, and economy. Since the time of these road-paving works, a lot has changed. In the last two decades, various concepts and manifestations of intellectualized engineered systems have appeared. A paradigmatic feature of these systems, exemplified by smart cyber-physical systems, is that they collect, infer, or extract massive amount of synthetic system knowledge (M-SSK) based on some pre-programmed human knowledge. The amount of this type of knowledge grows continuously. It can be aggregated on system level and on system of systems level. This paper argues that this aggregated M-SSK is not covered by the abovementioned four genres of knowledge. In fact, it represents a new genre. The conducted literature study underpins this claim. Therefore, the paper suggests dealing with it as a new genre, called epsilon-knowledge. Artificial intelligence, system engineering, cyber-physical systems, and knowledge engineering are the disciplines dealing with epsilon-knowledge. The paper refers to sympérasmology as the proper conceptual framework of studying this genre of knowledge.
{"title":"The epsilon-knowledge: an emerging complement of Machlup's types of disciplinary knowledge","authors":"I. Horváth","doi":"10.1017/S089006042200004X","DOIUrl":"https://doi.org/10.1017/S089006042200004X","url":null,"abstract":"Abstract Machlup used the words alpha, beta, and gamma to identify humanities, science, and social science as three distinct fields of academic learning and knowing, in addition to general knowledge. Gilles and Paquet identified a fourth type of disciplinary knowledge and labeled it as delta. This includes the knowledge of creative disciplines such as design, law, and economy. Since the time of these road-paving works, a lot has changed. In the last two decades, various concepts and manifestations of intellectualized engineered systems have appeared. A paradigmatic feature of these systems, exemplified by smart cyber-physical systems, is that they collect, infer, or extract massive amount of synthetic system knowledge (M-SSK) based on some pre-programmed human knowledge. The amount of this type of knowledge grows continuously. It can be aggregated on system level and on system of systems level. This paper argues that this aggregated M-SSK is not covered by the abovementioned four genres of knowledge. In fact, it represents a new genre. The conducted literature study underpins this claim. Therefore, the paper suggests dealing with it as a new genre, called epsilon-knowledge. Artificial intelligence, system engineering, cyber-physical systems, and knowledge engineering are the disciplines dealing with epsilon-knowledge. The paper refers to sympérasmology as the proper conceptual framework of studying this genre of knowledge.","PeriodicalId":50951,"journal":{"name":"Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45771647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-06DOI: 10.1017/S0890060422000014
G. Vasantha, David Purves, J. Quigley, J. Corney, A. Sherlock, Geevin Randika
Abstract This research envisages an automated system to inform engineers when opportunities occur to use existing features or configurations during the development of new products. Such a system could be termed a "predictive CAD system" because it would be able to suggest feature choices that follow patterns established in existing products. The predictive CAD literature largely focuses on predicting components for assemblies using 3D solid models. In contrast, this research work focuses on feature-based predictive CAD system using B-rep models. This paper investigates the performance of predictive models that could enable the creation of such an intelligent CAD system by assessing three different methods to support inference: sequential, machine learning, or probabilistic methods using N-Grams, Neural Networks (NNs), and Bayesian Networks (BNs) as representative of these methods. After defining the functional properties that characterize a predictive design system, a generic development methodology is presented. The methodology is used to carry out a systematic assessment of the relative performance of three methods each used to predict the diameter value of the next hole and boss feature type being added during the design of a hydraulic valve body. Evaluating predictive performance providing five recommendations ($k = 5$) for hole or boss features as a new design was developed, recall@k increased from around 30% to 50% and precision@k from around 50% to 70% as one to three features were added. The results indicate that the BN and NN models perform better than those using N-Grams. The practical impact of this contribution is assessed using a prototype (implemented as an extension to a commercial CAD system) by engineers whose comments defined an agenda for ongoing research in this area.
{"title":"Assessment of predictive probability models for effective mechanical design feature reuse","authors":"G. Vasantha, David Purves, J. Quigley, J. Corney, A. Sherlock, Geevin Randika","doi":"10.1017/S0890060422000014","DOIUrl":"https://doi.org/10.1017/S0890060422000014","url":null,"abstract":"Abstract This research envisages an automated system to inform engineers when opportunities occur to use existing features or configurations during the development of new products. Such a system could be termed a \"predictive CAD system\" because it would be able to suggest feature choices that follow patterns established in existing products. The predictive CAD literature largely focuses on predicting components for assemblies using 3D solid models. In contrast, this research work focuses on feature-based predictive CAD system using B-rep models. This paper investigates the performance of predictive models that could enable the creation of such an intelligent CAD system by assessing three different methods to support inference: sequential, machine learning, or probabilistic methods using N-Grams, Neural Networks (NNs), and Bayesian Networks (BNs) as representative of these methods. After defining the functional properties that characterize a predictive design system, a generic development methodology is presented. The methodology is used to carry out a systematic assessment of the relative performance of three methods each used to predict the diameter value of the next hole and boss feature type being added during the design of a hydraulic valve body. Evaluating predictive performance providing five recommendations ($k = 5$) for hole or boss features as a new design was developed, recall@k increased from around 30% to 50% and precision@k from around 50% to 70% as one to three features were added. The results indicate that the BN and NN models perform better than those using N-Grams. The practical impact of this contribution is assessed using a prototype (implemented as an extension to a commercial CAD system) by engineers whose comments defined an agenda for ongoing research in this area.","PeriodicalId":50951,"journal":{"name":"Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44077659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-01DOI: 10.1017/S0890060400001037
J. Gualtieri, L. Sharon, Riedel, Ashok K. Goel, H. Howard, Andrew see Seddon, Mary Lou, S. Fenves, Andrew B. Conru
Bailin, Sidney C. see Henderson, Scott, 163 Bajpaj, Atul. An Expert System Approach to Design of Automotive Air-Conditioning Systems, 1 Bhatta, Sambasiva R. and Ashok K. Goel. Discovery of Physical Principles from Design Experiences, 113 Bicharra, A. Christina, H. Craig Howard, and Mark J. Stefik. Improving Design and Documentation by Using Partially Automated Synthesis, 335 Brereton, Pearl, see Seddon, Andrew, 13 Brown, David C , see Maher, Mary Lou, 81, see also Chabot (Nee Homer), Rosemary, 125
{"title":"Author-Title Index","authors":"J. Gualtieri, L. Sharon, Riedel, Ashok K. Goel, H. Howard, Andrew see Seddon, Mary Lou, S. Fenves, Andrew B. Conru","doi":"10.1017/S0890060400001037","DOIUrl":"https://doi.org/10.1017/S0890060400001037","url":null,"abstract":"Bailin, Sidney C. see Henderson, Scott, 163 Bajpaj, Atul. An Expert System Approach to Design of Automotive Air-Conditioning Systems, 1 Bhatta, Sambasiva R. and Ashok K. Goel. Discovery of Physical Principles from Design Experiences, 113 Bicharra, A. Christina, H. Craig Howard, and Mark J. Stefik. Improving Design and Documentation by Using Partially Automated Synthesis, 335 Brereton, Pearl, see Seddon, Andrew, 13 Brown, David C , see Maher, Mary Lou, 81, see also Chabot (Nee Homer), Rosemary, 125","PeriodicalId":50951,"journal":{"name":"Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing","volume":"8 1","pages":"371 - 375"},"PeriodicalIF":2.1,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/S0890060400001037","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49341251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-21DOI: 10.1017/S0890060421000287
Jacob Rodda, C. Ranscombe, B. Kuys
Abstract An engaging user experience is an increasingly important design characteristic in the automotive industry. Compared with physical design characteristics (form, material, mechanical design, appearance), automotive designers find UX (user experience) challenging to communicate during the early stages of the design process without investing in expensive prototypes and/or models. This paper presents the development of a method to explore strategies to communicate UX through the medium of storyboards early in the design process. The method enables links to be drawn between the design tool of storyboarding and the attributes of theoretical UX outlined in theoretical frameworks. By applying this method in a case study of a storyboard created by Ford Design Asia Pacific, we identify how the theoretical attributes of UX are manifested, and we also highlight certain attributes of UX that are difficult to convey during the early phases of automotive design. This research thus contributes a method relevant to practice that assists with effectively communicating UX in early-stage automotive design where higher fidelity prototyping is unviable. Additionally, it enables the study of storyboard outcomes in the design process to assess the degree to which the intended UX is communicated. In doing so, it contributes a first step toward formalizing the analysis of UX in concept design, which in turn opens up this highly subjective area to further research in the automated analysis of conceptual design and even generative design.
{"title":"A method to explore strategies to communicate user experience through storyboards: an automotive design case study","authors":"Jacob Rodda, C. Ranscombe, B. Kuys","doi":"10.1017/S0890060421000287","DOIUrl":"https://doi.org/10.1017/S0890060421000287","url":null,"abstract":"Abstract An engaging user experience is an increasingly important design characteristic in the automotive industry. Compared with physical design characteristics (form, material, mechanical design, appearance), automotive designers find UX (user experience) challenging to communicate during the early stages of the design process without investing in expensive prototypes and/or models. This paper presents the development of a method to explore strategies to communicate UX through the medium of storyboards early in the design process. The method enables links to be drawn between the design tool of storyboarding and the attributes of theoretical UX outlined in theoretical frameworks. By applying this method in a case study of a storyboard created by Ford Design Asia Pacific, we identify how the theoretical attributes of UX are manifested, and we also highlight certain attributes of UX that are difficult to convey during the early phases of automotive design. This research thus contributes a method relevant to practice that assists with effectively communicating UX in early-stage automotive design where higher fidelity prototyping is unviable. Additionally, it enables the study of storyboard outcomes in the design process to assess the degree to which the intended UX is communicated. In doing so, it contributes a first step toward formalizing the analysis of UX in concept design, which in turn opens up this highly subjective area to further research in the automated analysis of conceptual design and even generative design.","PeriodicalId":50951,"journal":{"name":"Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2022-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45076660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-18DOI: 10.1017/S0890060421000330
Md. Fashiar Rahman, Tzu‐Liang Bill Tseng, Jianguo Wu, Yuxin Wen, Yirong Lin
Abstract Automatic extraction of filler morphology (size, orientation, and spatial distribution) in Scanning Electron Microscopic (SEM) images is essential in many applications such as automatic quality inspection in composite manufacturing. Extraction of filler morphology greatly depends on accurate segmentation of fillers (fibers and particles), which is a challenging task due to the overlap of fibers and particles and their obscure presence in SEM images. Convolution Neural Networks (CNNs) have been shown to be very effective at object recognition in digital images. This paper proposes an automatic filler detection system in SEM images, utilizing a Mask Region-based CNN architecture. The proposed system can simultaneously classify, detect, and segment fillers in SEM images, making it suitable for morphology analysis of fillers and automatic quality inspection. We also propose a novel SEM image simulation procedure to overcome the data scarcity for training a deep CNN architecture. The proposed filler detection system is trained on the simulated images. It is shown that the trained network can detect and segment fillers with higher accuracy even in the overlapping and obscure situations. The performance and robustness of the proposed system are evaluated using both simulated and real microscopic images.
{"title":"A deep learning-based approach to extraction of filler morphology in SEM images with the application of automated quality inspection","authors":"Md. Fashiar Rahman, Tzu‐Liang Bill Tseng, Jianguo Wu, Yuxin Wen, Yirong Lin","doi":"10.1017/S0890060421000330","DOIUrl":"https://doi.org/10.1017/S0890060421000330","url":null,"abstract":"Abstract Automatic extraction of filler morphology (size, orientation, and spatial distribution) in Scanning Electron Microscopic (SEM) images is essential in many applications such as automatic quality inspection in composite manufacturing. Extraction of filler morphology greatly depends on accurate segmentation of fillers (fibers and particles), which is a challenging task due to the overlap of fibers and particles and their obscure presence in SEM images. Convolution Neural Networks (CNNs) have been shown to be very effective at object recognition in digital images. This paper proposes an automatic filler detection system in SEM images, utilizing a Mask Region-based CNN architecture. The proposed system can simultaneously classify, detect, and segment fillers in SEM images, making it suitable for morphology analysis of fillers and automatic quality inspection. We also propose a novel SEM image simulation procedure to overcome the data scarcity for training a deep CNN architecture. The proposed filler detection system is trained on the simulated images. It is shown that the trained network can detect and segment fillers with higher accuracy even in the overlapping and obscure situations. The performance and robustness of the proposed system are evaluated using both simulated and real microscopic images.","PeriodicalId":50951,"journal":{"name":"Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2022-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47817868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-01DOI: 10.1017/S0890060421000408
M. Meyer, Timm Fichtler, C. Koldewey, R. Dumitrescu
Abstract The successful planning of future product generations requires reliable insights into the actual products’ problems and potentials for improvement. A valuable source for these insights is the product use phase. In practice, product planners are often forced to work with assumptions and speculations as insights from the use phase are insufficiently identified and documented. A new opportunity to address this problem arises from the ongoing digitalization that enables products to generate and collect data during their utilization. Analyzing these data could enable their manufacturers to generate and exploit insights concerning product performance and user behavior, revealing problems and potentials for improvement. However, research on analyzing use phase data in product planning of manufacturing companies is scarce. Therefore, we conducted an exploratory interview study with decision-makers of eight manufacturing companies. The result of this paper is a detailed description of the potentials and challenges that the interviewees associated with analyzing use phase data in product planning. The potentials explain the intended purpose and generic application examples. The challenges concern the products, the data, the customers, the implementation, and the employees. By gathering the potentials and challenges through expert interviews, our study structures the topic from the perspective of the potential users and shows the needs for future research.
{"title":"Potentials and challenges of analyzing use phase data in product planning of manufacturing companies","authors":"M. Meyer, Timm Fichtler, C. Koldewey, R. Dumitrescu","doi":"10.1017/S0890060421000408","DOIUrl":"https://doi.org/10.1017/S0890060421000408","url":null,"abstract":"Abstract The successful planning of future product generations requires reliable insights into the actual products’ problems and potentials for improvement. A valuable source for these insights is the product use phase. In practice, product planners are often forced to work with assumptions and speculations as insights from the use phase are insufficiently identified and documented. A new opportunity to address this problem arises from the ongoing digitalization that enables products to generate and collect data during their utilization. Analyzing these data could enable their manufacturers to generate and exploit insights concerning product performance and user behavior, revealing problems and potentials for improvement. However, research on analyzing use phase data in product planning of manufacturing companies is scarce. Therefore, we conducted an exploratory interview study with decision-makers of eight manufacturing companies. The result of this paper is a detailed description of the potentials and challenges that the interviewees associated with analyzing use phase data in product planning. The potentials explain the intended purpose and generic application examples. The challenges concern the products, the data, the customers, the implementation, and the employees. By gathering the potentials and challenges through expert interviews, our study structures the topic from the perspective of the potential users and shows the needs for future research.","PeriodicalId":50951,"journal":{"name":"Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42205474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-16DOI: 10.1017/S089006042100041X
Yulaing Li, Wei Zhao, Wenqi Zhang, Meng Chen
Abstract To accurately predict propagation dynamics for single or multiple change propagations across different product development stages in a sequential or concurrent way is critical for decision-making of implementing change requests. In this paper, a change propagation dynamic model is built based on the compartmentalization of engineering entities into susceptible engineering entities and affected engineering entities (SA), the ordinary differential equations for describing the rate of affected entities with respect to the total ones and the duration for resolving all the changes for every moment are presented by combining the calculations of change impacts with different split and joint junctions. Considering the difficulty of finding analytical solutions to the differential equations, algorithms for sequential and concurrent simulations of change propagations across different development stages, and random and GA (Genetic Algorithm)-based optimal selections of feasible propagation paths are developed to obtain numerical solutions for single and multiple change requests. Simulation results show that change ripples and blossoms can be observed in both sequential and concurrent change propagations, and these propagation patterns are not sensitive to the initial change effect and the threshold value for propagations, while critical change propagation paths and the number of initiated changes have important effects on both concurrent and sequential change propagation process. It is also demonstrated that concurrent propagation strategy is advantageous for processing single or few of initiated changes since it can shorten product redevelopment time, sequential propagation strategy has an advantage of robustness for handling multiple initiated change requests.
{"title":"A dynamic model for engineering change propagations in multiple product development stages","authors":"Yulaing Li, Wei Zhao, Wenqi Zhang, Meng Chen","doi":"10.1017/S089006042100041X","DOIUrl":"https://doi.org/10.1017/S089006042100041X","url":null,"abstract":"Abstract To accurately predict propagation dynamics for single or multiple change propagations across different product development stages in a sequential or concurrent way is critical for decision-making of implementing change requests. In this paper, a change propagation dynamic model is built based on the compartmentalization of engineering entities into susceptible engineering entities and affected engineering entities (SA), the ordinary differential equations for describing the rate of affected entities with respect to the total ones and the duration for resolving all the changes for every moment are presented by combining the calculations of change impacts with different split and joint junctions. Considering the difficulty of finding analytical solutions to the differential equations, algorithms for sequential and concurrent simulations of change propagations across different development stages, and random and GA (Genetic Algorithm)-based optimal selections of feasible propagation paths are developed to obtain numerical solutions for single and multiple change requests. Simulation results show that change ripples and blossoms can be observed in both sequential and concurrent change propagations, and these propagation patterns are not sensitive to the initial change effect and the threshold value for propagations, while critical change propagation paths and the number of initiated changes have important effects on both concurrent and sequential change propagation process. It is also demonstrated that concurrent propagation strategy is advantageous for processing single or few of initiated changes since it can shorten product redevelopment time, sequential propagation strategy has an advantage of robustness for handling multiple initiated change requests.","PeriodicalId":50951,"journal":{"name":"Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46511424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-15DOI: 10.1017/S0890060421000202
Sónia da Silva Vieira, M. Benedek, J. Gero, S. Li, G. Cascini
Abstract This paper presents results from a design neurocognition study on the effect of gender on EEG frequency band power when performing constrained and open design. We used electroencephalography to measure the brain activity of 84 professional designers. We investigated differences in frequency power associated with gender of 38 female and 46 male designers, while performing two prototypical design tasks. The aim of the study was to explore whether gender moderates brain activity while performing a constrained versus an open design task. Neurophysiological results for aggregate activations across genders and between tasks indicate a main effect of gender for theta, alpha 2, and beta 1 frequency bands. Females show higher theta, alpha 2, and beta 1, namely in the right dorsolateral prefrontal cortex, right occipitotemporal cortex, secondary visual cortex, and prefrontal cortex in both tasks. Females show higher beta bands than males, in areas of the left prefrontal cortex, in the constrained design. While in the open design, females showed higher theta, alpha, and beta 2 in the left prefrontal cortex and secondary visual cortex for all frequency bands. Results within gender between tasks indicate higher theta and alpha in the prefrontal cortex in the constrained design for both genders. Whilst for open design, results indicate higher theta and alpha 1 in the right hemisphere and higher alpha 2 and beta bands across hemispheres for both genders. Results within gender reveal common brain areas and frequency bands in distinguishing constrained from open design.
{"title":"Brain activity in constrained and open design: the effect of gender on frequency bands","authors":"Sónia da Silva Vieira, M. Benedek, J. Gero, S. Li, G. Cascini","doi":"10.1017/S0890060421000202","DOIUrl":"https://doi.org/10.1017/S0890060421000202","url":null,"abstract":"Abstract This paper presents results from a design neurocognition study on the effect of gender on EEG frequency band power when performing constrained and open design. We used electroencephalography to measure the brain activity of 84 professional designers. We investigated differences in frequency power associated with gender of 38 female and 46 male designers, while performing two prototypical design tasks. The aim of the study was to explore whether gender moderates brain activity while performing a constrained versus an open design task. Neurophysiological results for aggregate activations across genders and between tasks indicate a main effect of gender for theta, alpha 2, and beta 1 frequency bands. Females show higher theta, alpha 2, and beta 1, namely in the right dorsolateral prefrontal cortex, right occipitotemporal cortex, secondary visual cortex, and prefrontal cortex in both tasks. Females show higher beta bands than males, in areas of the left prefrontal cortex, in the constrained design. While in the open design, females showed higher theta, alpha, and beta 2 in the left prefrontal cortex and secondary visual cortex for all frequency bands. Results within gender between tasks indicate higher theta and alpha in the prefrontal cortex in the constrained design for both genders. Whilst for open design, results indicate higher theta and alpha 1 in the right hemisphere and higher alpha 2 and beta bands across hemispheres for both genders. Results within gender reveal common brain areas and frequency bands in distinguishing constrained from open design.","PeriodicalId":50951,"journal":{"name":"Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2022-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47826357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-09DOI: 10.1017/S0890060421000238
Djordje Kristic
Abstract The formal approach to shapes and their algebras, as it appears in shape grammar theory, has been reviewed. It starts with geometric elements and their partial algebras, continues to shapes, their algebras, and boundaries, as well as algebras that calculate with shapes and their boundaries. There is a number of new concepts introduced along the way. These include diagonal decompositions and their algebras which simplify calculations with shapes, b-paired diagonal decompositions which extend calculations with shapes and their boundaries from diagonal shapes only to all shapes, and m-order boundaries which extend the concept of shape boundaries and allow for calculations with multiple representations of shapes. It also shows that algebras of shapes are infinite direct sums of diagonal algebras.
{"title":"Diagonal decompositions of shapes and their algebras","authors":"Djordje Kristic","doi":"10.1017/S0890060421000238","DOIUrl":"https://doi.org/10.1017/S0890060421000238","url":null,"abstract":"Abstract The formal approach to shapes and their algebras, as it appears in shape grammar theory, has been reviewed. It starts with geometric elements and their partial algebras, continues to shapes, their algebras, and boundaries, as well as algebras that calculate with shapes and their boundaries. There is a number of new concepts introduced along the way. These include diagonal decompositions and their algebras which simplify calculations with shapes, b-paired diagonal decompositions which extend calculations with shapes and their boundaries from diagonal shapes only to all shapes, and m-order boundaries which extend the concept of shape boundaries and allow for calculations with multiple representations of shapes. It also shows that algebras of shapes are infinite direct sums of diagonal algebras.","PeriodicalId":50951,"journal":{"name":"Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2022-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47813190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-09DOI: 10.1017/S0890060421000421
S. Kotsopoulos
Abstract Shapes are perceived unanalyzed, without rigid representation of their parts. They do not comply with standard symbolic knowledge representation criteria; they are treated and judged by appearance. Resolving the relationship of parts to parts and parts to wholes has a constructive role in perception and design. This paper presents a computational account of part–whole figuration in design. To this end, shape rules are used to show how a shape is seen, and shape decompositions having structures of topologies and Boolean algebras reveal alternative structures for parts. Four examples of shape computation are presented. Topologies demonstrate the relationships of wholes, parts, and subparts, in the computations enabling the comparison and relativization of structures, and lattice diagrams are used to present their order. Retrospectively, the topologies help to recall the generative history and establish computational continuity. When the parts are modified to recognize emergent squares locally, other emergent shapes are highlighted globally as the topology is re-adjusted. Two types of emergence are identified: local and global. Seeing the local parts modifies how we analyze the global whole, and thus, a local observation yields a global order.
{"title":"Design without representation","authors":"S. Kotsopoulos","doi":"10.1017/S0890060421000421","DOIUrl":"https://doi.org/10.1017/S0890060421000421","url":null,"abstract":"Abstract Shapes are perceived unanalyzed, without rigid representation of their parts. They do not comply with standard symbolic knowledge representation criteria; they are treated and judged by appearance. Resolving the relationship of parts to parts and parts to wholes has a constructive role in perception and design. This paper presents a computational account of part–whole figuration in design. To this end, shape rules are used to show how a shape is seen, and shape decompositions having structures of topologies and Boolean algebras reveal alternative structures for parts. Four examples of shape computation are presented. Topologies demonstrate the relationships of wholes, parts, and subparts, in the computations enabling the comparison and relativization of structures, and lattice diagrams are used to present their order. Retrospectively, the topologies help to recall the generative history and establish computational continuity. When the parts are modified to recognize emergent squares locally, other emergent shapes are highlighted globally as the topology is re-adjusted. Two types of emergence are identified: local and global. Seeing the local parts modifies how we analyze the global whole, and thus, a local observation yields a global order.","PeriodicalId":50951,"journal":{"name":"Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2022-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48305962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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