Pub Date : 2021-05-12DOI: 10.14733/CADCONFP.2021.349-353
Wei Huang, Xiangzhi Wei, Jiaye Zhu, K. Dai
{"title":"A Parking Pose and Trajectory Selection Algorithm Based on Artificial Potential Field and Particle Swarm Optimization","authors":"Wei Huang, Xiangzhi Wei, Jiaye Zhu, K. Dai","doi":"10.14733/CADCONFP.2021.349-353","DOIUrl":"https://doi.org/10.14733/CADCONFP.2021.349-353","url":null,"abstract":"","PeriodicalId":166025,"journal":{"name":"CAD'21 Proceedings","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116663852","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}
Pub Date : 2021-05-12DOI: 10.14733/CADCONFP.2021.192-197
É. Martin, Remus Tutunea-Fatan, R. Gergely, D. Okonski
Introduction: Many governments have adopted stringent emissions standards for automobiles in order to reduce the detrimental effects they have on the environment [9]. The reduction of vehicle weight is one avenue to reduce vehicle emissions [7],[13] and composite components can play a key role in many potential solutions [1]. However, there are still many challenges to be overcome when it comes to the widespread use of the composite materials, particularly with respect to the mass production of composite parts and assemblies. One option for composite part fabrication is the long fiber thermoplastic direct (LFT-D) manufacturing process. According to this technology, parts can be produced from individual matrix and fiber components. This approach eliminates the need for a semi-finished product like glass mat thermoplastic (GMT) to be acquired from a material supplier and thus translates into cost savings for the composite part manufacturer [5]. Like many other thermal forming processes, the parts fabricated through LFT-D can experience a significant amount of deformation. Warpage can make parts difficult to incorporate in downstream assemblies. However, despite the importance of this problem, the quantitative characterization of the warpage presented in the surveyed literature is relatively simplistic. While no clear metric for part warpage exists at this time, prior studies have highlighted that part warpage can be reduced by changing molding processing parameters [3],[4],[6],[10],[11]. For many of these studies, part warpage is typically evaluated by the maximum deviation between the nominal and fabricated parts. Nonetheless, while the maximum value of the deviation between the two shapes is indeed important, warpage could be defined in many other ways. One alternative is to evaluate specific regions of the part, particularly those that are involved in subsequent assembly operations. Along these lines, the current study aims to propose alternative metrics capable of evaluating part warpage.
{"title":"Quantitative Characterization of Warpage for Composite Components","authors":"É. Martin, Remus Tutunea-Fatan, R. Gergely, D. Okonski","doi":"10.14733/CADCONFP.2021.192-197","DOIUrl":"https://doi.org/10.14733/CADCONFP.2021.192-197","url":null,"abstract":"Introduction: Many governments have adopted stringent emissions standards for automobiles in order to reduce the detrimental effects they have on the environment [9]. The reduction of vehicle weight is one avenue to reduce vehicle emissions [7],[13] and composite components can play a key role in many potential solutions [1]. However, there are still many challenges to be overcome when it comes to the widespread use of the composite materials, particularly with respect to the mass production of composite parts and assemblies. One option for composite part fabrication is the long fiber thermoplastic direct (LFT-D) manufacturing process. According to this technology, parts can be produced from individual matrix and fiber components. This approach eliminates the need for a semi-finished product like glass mat thermoplastic (GMT) to be acquired from a material supplier and thus translates into cost savings for the composite part manufacturer [5]. Like many other thermal forming processes, the parts fabricated through LFT-D can experience a significant amount of deformation. Warpage can make parts difficult to incorporate in downstream assemblies. However, despite the importance of this problem, the quantitative characterization of the warpage presented in the surveyed literature is relatively simplistic. While no clear metric for part warpage exists at this time, prior studies have highlighted that part warpage can be reduced by changing molding processing parameters [3],[4],[6],[10],[11]. For many of these studies, part warpage is typically evaluated by the maximum deviation between the nominal and fabricated parts. Nonetheless, while the maximum value of the deviation between the two shapes is indeed important, warpage could be defined in many other ways. One alternative is to evaluate specific regions of the part, particularly those that are involved in subsequent assembly operations. Along these lines, the current study aims to propose alternative metrics capable of evaluating part warpage.","PeriodicalId":166025,"journal":{"name":"CAD'21 Proceedings","volume":"319 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122703262","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}
Pub Date : 2021-05-12DOI: 10.14733/CADCONFP.2021.134-138
Hui Liu, Yuxiu Lin, L. Shanshan, Caiming Zhang
Introduction: Medical imaging technologies are essential to disease diagnosis and surgery planning, such as computational tomography (CT), which expresses the acquired tomographic medical image data as a set of slice sequences. In order to decrease the radiation amount received by the patients, it is a common practice to reduce the sampling rate and improve the scanning speed, which resulting in the loss of some valuable temporal information and remarkably large slice interval. Therefore, most medical imaging volumes are taken anisotropically with a high intra-slice resolution and a low inter-slice resolution. This phenomenon leads to problems such as rough or even broken tissue boundaries in 3D reconstructed models, which will undoubtedly a ect the accuracy of lesion analysis result. As such, an accurate and reliable method to upsample the low inter-slice resolution, which we refer to as the medical image slice interpolation techniques, is much needed in research. In addition to generating accurate 3D reconstructions, medical slice interpolation can also be widely used in medical image segmentation, multi-frame super-resolution (MFSR) reconstruction , and other elds. By adding new virtual slices between every two consecutive images, as shown in Fig. 1, the number and information of experimental data sets are increased, in order to boost MFSR and medical image segmentation accuracy. Especially, increasing the amount of training samples is indispensable for the popular research method such as neural network. Therefore, it is necessary to improve the inter-slice interpolation techniques to increase the axial spatial resolution of the data acquired using medical imaging modalities. Image interpolation technology has been wide-spread used in various area of image processing, especially in the eld of medical image processing. The methods for this task can be categorized into four groups. (1) Grayscale-based interpolation methods [12, 13] directly use the grayscale information of two consecutive images, to interpolate the inter-layer images through a set of basis functions. Nearest neighbor interpolation [2] , linear interpolation [1] and cubic B spline interpolation [11] are the common types of such interpolation methods. This method is widely used in image interpolation because of its computational simplicity and less computationally expensive. However, the interpolated images obtained by these methods are usually too smooth and contain the artifacts. (2) The shape-based interpolation methods [3, 7] generate contours of the image to be interpolated directly based on the contour shapes of two consecutive images. Compared with the grayscale-based interpolation methods, it can e ectively
{"title":"Slice Interpolation for Medical Image based on Spatial Geometry Polynomial Fitting","authors":"Hui Liu, Yuxiu Lin, L. Shanshan, Caiming Zhang","doi":"10.14733/CADCONFP.2021.134-138","DOIUrl":"https://doi.org/10.14733/CADCONFP.2021.134-138","url":null,"abstract":"Introduction: Medical imaging technologies are essential to disease diagnosis and surgery planning, such as computational tomography (CT), which expresses the acquired tomographic medical image data as a set of slice sequences. In order to decrease the radiation amount received by the patients, it is a common practice to reduce the sampling rate and improve the scanning speed, which resulting in the loss of some valuable temporal information and remarkably large slice interval. Therefore, most medical imaging volumes are taken anisotropically with a high intra-slice resolution and a low inter-slice resolution. This phenomenon leads to problems such as rough or even broken tissue boundaries in 3D reconstructed models, which will undoubtedly a ect the accuracy of lesion analysis result. As such, an accurate and reliable method to upsample the low inter-slice resolution, which we refer to as the medical image slice interpolation techniques, is much needed in research. In addition to generating accurate 3D reconstructions, medical slice interpolation can also be widely used in medical image segmentation, multi-frame super-resolution (MFSR) reconstruction , and other elds. By adding new virtual slices between every two consecutive images, as shown in Fig. 1, the number and information of experimental data sets are increased, in order to boost MFSR and medical image segmentation accuracy. Especially, increasing the amount of training samples is indispensable for the popular research method such as neural network. Therefore, it is necessary to improve the inter-slice interpolation techniques to increase the axial spatial resolution of the data acquired using medical imaging modalities. Image interpolation technology has been wide-spread used in various area of image processing, especially in the eld of medical image processing. The methods for this task can be categorized into four groups. (1) Grayscale-based interpolation methods [12, 13] directly use the grayscale information of two consecutive images, to interpolate the inter-layer images through a set of basis functions. Nearest neighbor interpolation [2] , linear interpolation [1] and cubic B spline interpolation [11] are the common types of such interpolation methods. This method is widely used in image interpolation because of its computational simplicity and less computationally expensive. However, the interpolated images obtained by these methods are usually too smooth and contain the artifacts. (2) The shape-based interpolation methods [3, 7] generate contours of the image to be interpolated directly based on the contour shapes of two consecutive images. Compared with the grayscale-based interpolation methods, it can e ectively","PeriodicalId":166025,"journal":{"name":"CAD'21 Proceedings","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122095876","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}
Pub Date : 2021-05-12DOI: 10.14733/CADCONFP.2021.187-191
Yanlin Shi, Q. Peng, Jian Zhang
Introduction: Rehabilitation devices help patients to recover injured body parts such as elbow and knee joints [3]. Trajectory planning of rehabilitation exercises determines a suitable moving path to guide patients in daily recovery activities for body parts based on injured levels and joints [4]. It is expected that the rehabilitation process is smooth and comfortable. The existing trajectory planning are mainly manual methods that require physicians to plan the rehabilitation exercise trajectory [7], which is inefficient and inaccurate [1]. Reinforcement learning (RL) uses intelligent agents to plan actions in environments for maximum rewards [5]. Using RL, a rehabilitation device can autonomously learn and plan a trajectory for required exercise actions in different conditions. Based on the range of rotation angles and movement speed required in the rehabilitation of patients, a reward function can generate the optimal trajectory for patients to approach the target position in rehabilitation exercises efficiently and accurately [6]. An integrated reward function is proposed in this paper to plan the trajectory of rehabilitation exercises. Based on injured joints of a patient recorded by motion sensors, the range of rotation angles and movement speeds are restricted and planed for the patient using RL. The rotation angles and movement speeds are reset for injured joints based on the daily progress of the patient recovery to improve performance of the rehabilitation.
{"title":"Trajectory Planning of Rehabilitation Exercises using an Integrated Reward Function in Reinforcement Learning","authors":"Yanlin Shi, Q. Peng, Jian Zhang","doi":"10.14733/CADCONFP.2021.187-191","DOIUrl":"https://doi.org/10.14733/CADCONFP.2021.187-191","url":null,"abstract":"Introduction: Rehabilitation devices help patients to recover injured body parts such as elbow and knee joints [3]. Trajectory planning of rehabilitation exercises determines a suitable moving path to guide patients in daily recovery activities for body parts based on injured levels and joints [4]. It is expected that the rehabilitation process is smooth and comfortable. The existing trajectory planning are mainly manual methods that require physicians to plan the rehabilitation exercise trajectory [7], which is inefficient and inaccurate [1]. Reinforcement learning (RL) uses intelligent agents to plan actions in environments for maximum rewards [5]. Using RL, a rehabilitation device can autonomously learn and plan a trajectory for required exercise actions in different conditions. Based on the range of rotation angles and movement speed required in the rehabilitation of patients, a reward function can generate the optimal trajectory for patients to approach the target position in rehabilitation exercises efficiently and accurately [6]. An integrated reward function is proposed in this paper to plan the trajectory of rehabilitation exercises. Based on injured joints of a patient recorded by motion sensors, the range of rotation angles and movement speeds are restricted and planed for the patient using RL. The rotation angles and movement speeds are reset for injured joints based on the daily progress of the patient recovery to improve performance of the rehabilitation.","PeriodicalId":166025,"journal":{"name":"CAD'21 Proceedings","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128123685","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}
Pub Date : 2021-05-12DOI: 10.14733/CADCONFP.2021.283-287
M. Held, Peter Palfrader
Introduction: Consider a set S of points in the plane, called sites, and a signal that is sent out from each site. Now assume that each signal starts at the same time, say time t := 0, and propagates with unit speed uniformly in all directions. The locations at time t ≥ 0 that are reached by a signal sent out from a site s ∈ S is given by a circle ( o set circle ) of radius t centered at s, and the area that has been covered by that signal by time t is the corresponding circular disc. For t su ciently small, no pair of these discs will intersect. However, as t increases, intersections will occur. Apparently, intersections of two such circles correspond to points of the plane that are reached by two di erent signals at the same time. Assigning each locus of the plane to the site whose signal reached it rst yields a partition of the plane that is well-known as the Voronoi diagram of S; cf. Fig. 1(a). Adjacent regions of this partition are separated by straight-line segments. (We refer to the textbook [11] for more background information on Voronoi diagrams.) The boundary of the area covered by at least one signal by time t is called the wavefront of S at time t. It is easy to see that every wavefront of S consists of circular arcs whose endpoints lie on the Voronoi diagram of S. Voronoi diagrams can be generalized to settings where the signals no longer all travel at the same speed. To each site s a weight σ(s) is assigned that speci es how fast the signal travels: In this modi ed setting, the signal has reached points at distance σ(s) · t (from s) at time t. The corresponding Voronoi diagram is known asmultiplicatively weighted Voronoi diagram [5]. The common boundary of two adjacent regions is no longer a line segment but is a circular arc. Also, the region associated with a speci c site s can now be disconnected or multiply-connected; cf. Fig. 1(b). In a similar way, one can generalize the Voronoi diagram by allowing the sites to start emitting their signals at di erent points in time. This leads to the concept of additively weighted Voronoi diagrams. Voronoi diagrams have become an important geometric tool for modeling and analyzing coverage areas of sensors and transmitters. We refer to [4, 10, 12] for sample publications on this application. Common to these publications is the fact that the signal propagation is assumed to be uniform both over all sites and over all directions for each site.
{"title":"Modeling Coverage Areas of Anisotropic Transmitters by Voronoi-like Structures","authors":"M. Held, Peter Palfrader","doi":"10.14733/CADCONFP.2021.283-287","DOIUrl":"https://doi.org/10.14733/CADCONFP.2021.283-287","url":null,"abstract":"Introduction: Consider a set S of points in the plane, called sites, and a signal that is sent out from each site. Now assume that each signal starts at the same time, say time t := 0, and propagates with unit speed uniformly in all directions. The locations at time t ≥ 0 that are reached by a signal sent out from a site s ∈ S is given by a circle ( o set circle ) of radius t centered at s, and the area that has been covered by that signal by time t is the corresponding circular disc. For t su ciently small, no pair of these discs will intersect. However, as t increases, intersections will occur. Apparently, intersections of two such circles correspond to points of the plane that are reached by two di erent signals at the same time. Assigning each locus of the plane to the site whose signal reached it rst yields a partition of the plane that is well-known as the Voronoi diagram of S; cf. Fig. 1(a). Adjacent regions of this partition are separated by straight-line segments. (We refer to the textbook [11] for more background information on Voronoi diagrams.) The boundary of the area covered by at least one signal by time t is called the wavefront of S at time t. It is easy to see that every wavefront of S consists of circular arcs whose endpoints lie on the Voronoi diagram of S. Voronoi diagrams can be generalized to settings where the signals no longer all travel at the same speed. To each site s a weight σ(s) is assigned that speci es how fast the signal travels: In this modi ed setting, the signal has reached points at distance σ(s) · t (from s) at time t. The corresponding Voronoi diagram is known asmultiplicatively weighted Voronoi diagram [5]. The common boundary of two adjacent regions is no longer a line segment but is a circular arc. Also, the region associated with a speci c site s can now be disconnected or multiply-connected; cf. Fig. 1(b). In a similar way, one can generalize the Voronoi diagram by allowing the sites to start emitting their signals at di erent points in time. This leads to the concept of additively weighted Voronoi diagrams. Voronoi diagrams have become an important geometric tool for modeling and analyzing coverage areas of sensors and transmitters. We refer to [4, 10, 12] for sample publications on this application. Common to these publications is the fact that the signal propagation is assumed to be uniform both over all sites and over all directions for each site.","PeriodicalId":166025,"journal":{"name":"CAD'21 Proceedings","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114783131","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}
Pub Date : 2021-05-12DOI: 10.14733/CADCONFP.2021.267-271
Li Xiangdong, He Youlong, Q. Peng, Liu Wei
{"title":"A computer-aided Approach for Acquisition and Importance Ranking of Customer Requirements from the Online Comment Mining","authors":"Li Xiangdong, He Youlong, Q. Peng, Liu Wei","doi":"10.14733/CADCONFP.2021.267-271","DOIUrl":"https://doi.org/10.14733/CADCONFP.2021.267-271","url":null,"abstract":"","PeriodicalId":166025,"journal":{"name":"CAD'21 Proceedings","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121323098","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}
Pub Date : 2021-05-12DOI: 10.14733/CADCONFP.2021.144-148
Yuka Watanabe, J. Mitani
{"title":"Fitting Single Crease Curved-Fold Model to the User Specified Points","authors":"Yuka Watanabe, J. Mitani","doi":"10.14733/CADCONFP.2021.144-148","DOIUrl":"https://doi.org/10.14733/CADCONFP.2021.144-148","url":null,"abstract":"","PeriodicalId":166025,"journal":{"name":"CAD'21 Proceedings","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123121569","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}
Pub Date : 2021-05-12DOI: 10.14733/CADCONFP.2021.314-318
Haoxiang Li, J. Zheng
{"title":"L0-Regularization based Material Design for Hexahedral Mesh Models","authors":"Haoxiang Li, J. Zheng","doi":"10.14733/CADCONFP.2021.314-318","DOIUrl":"https://doi.org/10.14733/CADCONFP.2021.314-318","url":null,"abstract":"","PeriodicalId":166025,"journal":{"name":"CAD'21 Proceedings","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124998906","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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