Pub Date : 2022-01-01DOI: 10.5220/0011299300003271
P. D. Giamberardino, D. Iacoviello, F. Papa
The COVID-19 pandemic highlighted the fragility of the world in addressing a global health threat. The available resources of the pre-pandemic national health systems were inadequate to cope with the huge number of infected subjects needing health care and with the rapidity of the infection spread characterizing the COVID-19 outbreak. Indeed, an adequate allocation of the resources could produce in principle a strong reduction of the infection spread and of the hospital burden, preventing the collapse of the health system. In this work, taking inspiration from the COVID-19 and the difficulties in facing the emergency, an optimal problem of resource allocation is formulated on the basis of an ODE multi-group model composed by a network of SEIR-like submodels. The multi-group structure allows to differentiate the epidemic response of different populations or of various subgroups in the same population. In fact, an epidemic does not affect all populations in the same way, and even within the same population there can be epidemiological differences, like the susceptibility to the virus, the level of infectivity of the infectious subjects and the recovery from the disease. The subgroups are selected within the total population based on some peculiar characteristics, like for instance age, work, social condition, geographical position, etc., and they are connected by a network of contacts that allows the virus circulation within and among the groups. The proposed optimal control problem aims at defining a suitable monitoring campaign that is able to optimally allocate the number of swab tests between the subgroups of the population in order to reduce the number of infected patients (especially the most fragile ones) so reducing the epidemic impact on the health system. The proposed monitoring strategy can be applied both during the most critical phases of the emergency and in endemic conditions, when an active surveillance could be crucial for preventing the contagion rise.
{"title":"Optimal Resource Allocation for Fast Epidemic Monitoring in Networked Populations","authors":"P. D. Giamberardino, D. Iacoviello, F. Papa","doi":"10.5220/0011299300003271","DOIUrl":"https://doi.org/10.5220/0011299300003271","url":null,"abstract":"The COVID-19 pandemic highlighted the fragility of the world in addressing a global health threat. The available resources of the pre-pandemic national health systems were inadequate to cope with the huge number of infected subjects needing health care and with the rapidity of the infection spread characterizing the COVID-19 outbreak. Indeed, an adequate allocation of the resources could produce in principle a strong reduction of the infection spread and of the hospital burden, preventing the collapse of the health system. In this work, taking inspiration from the COVID-19 and the difficulties in facing the emergency, an optimal problem of resource allocation is formulated on the basis of an ODE multi-group model composed by a network of SEIR-like submodels. The multi-group structure allows to differentiate the epidemic response of different populations or of various subgroups in the same population. In fact, an epidemic does not affect all populations in the same way, and even within the same population there can be epidemiological differences, like the susceptibility to the virus, the level of infectivity of the infectious subjects and the recovery from the disease. The subgroups are selected within the total population based on some peculiar characteristics, like for instance age, work, social condition, geographical position, etc., and they are connected by a network of contacts that allows the virus circulation within and among the groups. The proposed optimal control problem aims at defining a suitable monitoring campaign that is able to optimally allocate the number of swab tests between the subgroups of the population in order to reduce the number of infected patients (especially the most fragile ones) so reducing the epidemic impact on the health system. The proposed monitoring strategy can be applied both during the most critical phases of the emergency and in endemic conditions, when an active surveillance could be crucial for preventing the contagion rise.","PeriodicalId":6436,"journal":{"name":"2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010)","volume":"16 1","pages":"616-625"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85018465","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 : 2022-01-01DOI: 10.5220/0011275900003271
María Flores, D. Valiente, J. Cabrera, Ó. Reinoso, L. Payá
: 360-degree views are beneficial in robotic tasks because they provide a compact view of the whole scenario. Among the different vision systems to generate this image, we use a back-to-back pair of fisheye lens cameras by Garmin (VIRB 360). The objectives of this work are twofold: generating a high-quality 360-degree view using different algorithms and performing an analytic evaluation. To provide a consistent evaluation and comparison of algorithms, we propose an automatic method that determines the similarity of the overlapping area of the generated views as regards a reference image, in terms of a global descriptor. These descriptors are obtained from one of the Convolutional Neural Network layers. As a result, the study reveals that an accurate stitching process can be achieved when a high number of feature points are detected and uniformly distributed in the overlapping area. In this case, the 360-degree view generated by the algorithm which employs the camera model provides more efficient stitching than the algorithm which considers the angular fisheye projection. This outcome demonstrates the wrong effects of the fisheye projection, which presents high distortion in the top and bottom parts. Likewise, both algorithms have been also compared with the view generated by the camera.
{"title":"Generation and Quality Evaluation of a 360-degree View from Dual Fisheye Images","authors":"María Flores, D. Valiente, J. Cabrera, Ó. Reinoso, L. Payá","doi":"10.5220/0011275900003271","DOIUrl":"https://doi.org/10.5220/0011275900003271","url":null,"abstract":": 360-degree views are beneficial in robotic tasks because they provide a compact view of the whole scenario. Among the different vision systems to generate this image, we use a back-to-back pair of fisheye lens cameras by Garmin (VIRB 360). The objectives of this work are twofold: generating a high-quality 360-degree view using different algorithms and performing an analytic evaluation. To provide a consistent evaluation and comparison of algorithms, we propose an automatic method that determines the similarity of the overlapping area of the generated views as regards a reference image, in terms of a global descriptor. These descriptors are obtained from one of the Convolutional Neural Network layers. As a result, the study reveals that an accurate stitching process can be achieved when a high number of feature points are detected and uniformly distributed in the overlapping area. In this case, the 360-degree view generated by the algorithm which employs the camera model provides more efficient stitching than the algorithm which considers the angular fisheye projection. This outcome demonstrates the wrong effects of the fisheye projection, which presents high distortion in the top and bottom parts. Likewise, both algorithms have been also compared with the view generated by the camera.","PeriodicalId":6436,"journal":{"name":"2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010)","volume":"43 1","pages":"434-442"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83542068","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 : 2022-01-01DOI: 10.5220/0011267800003271
Samet Güler, I. Yildirim, H. Alabay
: Air and ground robots with distinct sensing characteristics can be combined in a team to accomplish de-manding tasks robustly. A key challenge in such heterogeneous systems is the design of a local positioning methodology where each robot estimates its location with respect to its neighbors. We propose a filtering-based relative localization algorithm for air-ground teams composed of vertical-take-off-and-landing drones and unmanned aerial vehicles. The team members interact through a sensing/communication mechanism rely-ing on onboard units, which results in a mutual connection between the air and ground components. Exploiting the supplementary features of omnidirectional distance sensors and monocular cameras, the framework can function in all environments without fixed infrastructures. Various simulation and experiment results verify the competency of our approach.
{"title":"Mutual Relative Localization in Heterogeneous Air-ground Robot Teams","authors":"Samet Güler, I. Yildirim, H. Alabay","doi":"10.5220/0011267800003271","DOIUrl":"https://doi.org/10.5220/0011267800003271","url":null,"abstract":": Air and ground robots with distinct sensing characteristics can be combined in a team to accomplish de-manding tasks robustly. A key challenge in such heterogeneous systems is the design of a local positioning methodology where each robot estimates its location with respect to its neighbors. We propose a filtering-based relative localization algorithm for air-ground teams composed of vertical-take-off-and-landing drones and unmanned aerial vehicles. The team members interact through a sensing/communication mechanism rely-ing on onboard units, which results in a mutual connection between the air and ground components. Exploiting the supplementary features of omnidirectional distance sensors and monocular cameras, the framework can function in all environments without fixed infrastructures. Various simulation and experiment results verify the competency of our approach.","PeriodicalId":6436,"journal":{"name":"2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010)","volume":"101 12","pages":"304-312"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91408990","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 : 2022-01-01DOI: 10.5220/0011319300003271
N. Tasmurzayev, B. Amangeldy, Y. Nurakhov, D. Akhmed-Zaki, Z. Baigarayeva
: In this paper, we consider the software and hardware implementation of an intelligent control system for optimal use of solar thermal energy and geothermal energy accumulator for heating and hot water supply of residential areas, multi-storey buildings, greenhouses with the highest possible efficiency. To achieve maximum results, the system complies with the Industry 4.0 concept and uses a multi-level management and monitoring structure such as Web dispatching, local and global system management and monitoring, cloud and local data storage, cloud and local management and monitoring, emergency notification and changes in the system via web technologies.
{"title":"Intelligent Thermal Accumulator Operation Control System based on Renewable Energy Sources","authors":"N. Tasmurzayev, B. Amangeldy, Y. Nurakhov, D. Akhmed-Zaki, Z. Baigarayeva","doi":"10.5220/0011319300003271","DOIUrl":"https://doi.org/10.5220/0011319300003271","url":null,"abstract":": In this paper, we consider the software and hardware implementation of an intelligent control system for optimal use of solar thermal energy and geothermal energy accumulator for heating and hot water supply of residential areas, multi-storey buildings, greenhouses with the highest possible efficiency. To achieve maximum results, the system complies with the Industry 4.0 concept and uses a multi-level management and monitoring structure such as Web dispatching, local and global system management and monitoring, cloud and local data storage, cloud and local management and monitoring, emergency notification and changes in the system via web technologies.","PeriodicalId":6436,"journal":{"name":"2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010)","volume":"25 1","pages":"737-742"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90147224","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 : 2022-01-01DOI: 10.5220/0011279000003271
Arne Hasselbring, U. Frese, T. Röfer
{"title":"Learning Optimal Robot Ball Catching Trajectories Directly from the Model-based Trajectory Loss","authors":"Arne Hasselbring, U. Frese, T. Röfer","doi":"10.5220/0011279000003271","DOIUrl":"https://doi.org/10.5220/0011279000003271","url":null,"abstract":"","PeriodicalId":6436,"journal":{"name":"2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010)","volume":"31 1","pages":"201-208"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89423616","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 : 2022-01-01DOI: 10.5220/0011140800003271
Stephan Pareigis, F. Maaß
: A neural network architecture for end-to-end autonomous driving is presented, which is robust against discrep-ancies in system dynamics during the training process and in application. The proposed network architecture presents a first step to alleviate the simulation to reality gap with respect to differences in system dynamics. A vehicle is trained to drive inside a given lane in the CARLA simulator. The data is used to train NVIDIA’s PilotNet. When an offset is given to the steering angle of the vehicle while the trained network is being applied, PilotNet will not keep the vehicle inside the lane as expected. A new architecture is proposed called PilotNet ∆ , which is robust against steering angle offsets. Experiments in the simulator show that the vehicle will stay in the lane, although the steering properties of the vehicle differ.
{"title":"Robust Neural Network for Sim-to-Real Gap in End-to-End Autonomous Driving","authors":"Stephan Pareigis, F. Maaß","doi":"10.5220/0011140800003271","DOIUrl":"https://doi.org/10.5220/0011140800003271","url":null,"abstract":": A neural network architecture for end-to-end autonomous driving is presented, which is robust against discrep-ancies in system dynamics during the training process and in application. The proposed network architecture presents a first step to alleviate the simulation to reality gap with respect to differences in system dynamics. A vehicle is trained to drive inside a given lane in the CARLA simulator. The data is used to train NVIDIA’s PilotNet. When an offset is given to the steering angle of the vehicle while the trained network is being applied, PilotNet will not keep the vehicle inside the lane as expected. A new architecture is proposed called PilotNet ∆ , which is robust against steering angle offsets. Experiments in the simulator show that the vehicle will stay in the lane, although the steering properties of the vehicle differ.","PeriodicalId":6436,"journal":{"name":"2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010)","volume":"1 1","pages":"113-119"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82235961","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 : 2022-01-01DOI: 10.5220/0011123100003271
F. Arcadio, M. Costanzo, Giulio Luongo, L. Pellegrino, N. Cennamo, C. Natale
: Plastic optical fibers (POFs) biosensors are getting widespread in a number of application fields owing to their low cost, high performance, and for their extreme flexibility in terms of detection ability of a large number of specific substances in different matrices. A specific category of such sensors are those based on the surface plasmon resonance (SPR) phenomenon, which can be made very specific by suitable integration with a biological or chemical molecular recognition element (MRE), specifically designed for binding with the desired substance (the analyte). Despite the flexibility of the SPR-POF sensors, their production is still difficult to automate on a large scale because of the special polishing process of the plastic optical fiber. Such a process is currently performed by a human trained operator who rubs the surface of a short fiber segment against a sandpaper sheet by following an 8-shaped path while exerting a specific force in the direction normal to the contact surface. The present paper proposes the adoption of a collaborative robot programmed to perform the same task based on the data acquired from the human operator. To ensure the safe use of the robotic cell by operators who share the same workspace of the robot, the system is endowed with a workspace monitoring system that ensures the polishing task execution while minimizing the possible occurrence of collisions with human operators by suitable exploiting the kinematic redundancy of the robot.
{"title":"Safe Robotized Polishing of Plastic Optical Fibers for Plasmonic Sensors","authors":"F. Arcadio, M. Costanzo, Giulio Luongo, L. Pellegrino, N. Cennamo, C. Natale","doi":"10.5220/0011123100003271","DOIUrl":"https://doi.org/10.5220/0011123100003271","url":null,"abstract":": Plastic optical fibers (POFs) biosensors are getting widespread in a number of application fields owing to their low cost, high performance, and for their extreme flexibility in terms of detection ability of a large number of specific substances in different matrices. A specific category of such sensors are those based on the surface plasmon resonance (SPR) phenomenon, which can be made very specific by suitable integration with a biological or chemical molecular recognition element (MRE), specifically designed for binding with the desired substance (the analyte). Despite the flexibility of the SPR-POF sensors, their production is still difficult to automate on a large scale because of the special polishing process of the plastic optical fiber. Such a process is currently performed by a human trained operator who rubs the surface of a short fiber segment against a sandpaper sheet by following an 8-shaped path while exerting a specific force in the direction normal to the contact surface. The present paper proposes the adoption of a collaborative robot programmed to perform the same task based on the data acquired from the human operator. To ensure the safe use of the robotic cell by operators who share the same workspace of the robot, the system is endowed with a workspace monitoring system that ensures the polishing task execution while minimizing the possible occurrence of collisions with human operators by suitable exploiting the kinematic redundancy of the robot.","PeriodicalId":6436,"journal":{"name":"2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010)","volume":"33 1","pages":"361-368"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89575458","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 : 2022-01-01DOI: 10.5220/0011142200003271
S. Ibarra, V. Vigneron, J.-Ph. Conge, H. Maaref
: Much of convolutional neural network (CNN)’s success lies in translation invariance. The other part resides in the fact that thanks to a judicious choice of architecture, the network is able to make decisions taking into account the whole image. This work provides an alternative way to extend the pooling function, we named rank-order pooling, capable of extracting texture descriptors from images. The rank-order pooling layers are non parametric, independent of the geometric arrangement or sizes of the image regions, and can therefore better tolerate rotations. Rank-order pooling functions produce images capable of emphasizing low/high frequencies, contours, etc. We shows rank-order pooling leads to CNN models which can optimally exploit information from their receptive field.
{"title":"Importance Order Ranking for Texture Extraction: A More Efficient Pooling Operator Than Max Pooling?","authors":"S. Ibarra, V. Vigneron, J.-Ph. Conge, H. Maaref","doi":"10.5220/0011142200003271","DOIUrl":"https://doi.org/10.5220/0011142200003271","url":null,"abstract":": Much of convolutional neural network (CNN)’s success lies in translation invariance. The other part resides in the fact that thanks to a judicious choice of architecture, the network is able to make decisions taking into account the whole image. This work provides an alternative way to extend the pooling function, we named rank-order pooling, capable of extracting texture descriptors from images. The rank-order pooling layers are non parametric, independent of the geometric arrangement or sizes of the image regions, and can therefore better tolerate rotations. Rank-order pooling functions produce images capable of emphasizing low/high frequencies, contours, etc. We shows rank-order pooling leads to CNN models which can optimally exploit information from their receptive field.","PeriodicalId":6436,"journal":{"name":"2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010)","volume":"13 1","pages":"585-594"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91135055","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 : 2022-01-01DOI: 10.5220/0011319700003271
A. Demian, A. Klimchik
{"title":"External Force Adaptive Compensator for Serial Manipulators","authors":"A. Demian, A. Klimchik","doi":"10.5220/0011319700003271","DOIUrl":"https://doi.org/10.5220/0011319700003271","url":null,"abstract":"","PeriodicalId":6436,"journal":{"name":"2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010)","volume":"28 7 1","pages":"500-507"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80378031","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 : 2022-01-01DOI: 10.5220/0011321300003271
Tim Tiedemann, F. Cordes, Matthis Keppner, Heiner Peters
{"title":"Challenges of Autonomous In-field Fruit Harvesting and Concept of a Robotic Solution","authors":"Tim Tiedemann, F. Cordes, Matthis Keppner, Heiner Peters","doi":"10.5220/0011321300003271","DOIUrl":"https://doi.org/10.5220/0011321300003271","url":null,"abstract":"","PeriodicalId":6436,"journal":{"name":"2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010)","volume":"48 1","pages":"508-515"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78846105","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：