Pub Date : 2023-12-15DOI: 10.1142/s1793351x24410022
Hayden Freedman, Jacob Metzger, Neda Abolhassani, Ana Tudor, Bill Tomlinson, Sanjoy Paul
{"title":"A Bayesian Approach to Constructing Probabilistic Models from Knowledge Graphs","authors":"Hayden Freedman, Jacob Metzger, Neda Abolhassani, Ana Tudor, Bill Tomlinson, Sanjoy Paul","doi":"10.1142/s1793351x24410022","DOIUrl":"https://doi.org/10.1142/s1793351x24410022","url":null,"abstract":"","PeriodicalId":43471,"journal":{"name":"International Journal of Semantic Computing","volume":"11 24","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139000384","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 : 2023-08-28DOI: 10.1142/s1793351x2302004x
Fabio Persia, G. Glesener, Julienne A. Greer
{"title":"Guest Editorial — Special Issue on Transdisciplinary Artificial Intelligence","authors":"Fabio Persia, G. Glesener, Julienne A. Greer","doi":"10.1142/s1793351x2302004x","DOIUrl":"https://doi.org/10.1142/s1793351x2302004x","url":null,"abstract":"","PeriodicalId":43471,"journal":{"name":"International Journal of Semantic Computing","volume":"11 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87252869","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 : 2023-08-09DOI: 10.1142/s1793351x23620039
Hearim Moon, Juyeong Lee, Doyoon Kim, Eunsik Park, Junghyun Moon, Minsun Lee, Eric T. Matson, Minji Lee
Tropical cyclones are the world’s deadliest natural disasters, especially causing tree death by pulling out or breaking the roots of trees, which has a great impact on the forest ecosystem and forest owners. To minimize additional damage, an efficient approach is needed to quickly grasp information on the location and distribution of fallen trees. There are several studies that try to detect fallen trees in the past, but most of the research requires huge costs and is difficult to utilize. This research focuses on resolving those problems. Unmanned aerial vehicle (UAV) is widely used for ground detection for those who need a cost-effective way while pursuing high-resolution images. To take this advantage, this research collects data mainly using a UAV with an auxiliary high-resolution camera. The collected data is used for training the YOLOX model, an object detection algorithm, which can perform an accurate detection within a remarkably short time period. Also, by using YOLOX as a detection model, a wide-range versatility is obtained, which means, the solution driven by this research can be utilized for every scenario where inexpensive, but highly reliable object detection result is needed. This research implements a visualization application that displays detection results, calculated by a trained model, in a client-friendly way. Fallen trees are recognized in images or videos, and the analyzed results are provided as web-based visualizations.
{"title":"Cost-Effective Solution for Fallen Tree Recognition Using YOLOX Object Detection","authors":"Hearim Moon, Juyeong Lee, Doyoon Kim, Eunsik Park, Junghyun Moon, Minsun Lee, Eric T. Matson, Minji Lee","doi":"10.1142/s1793351x23620039","DOIUrl":"https://doi.org/10.1142/s1793351x23620039","url":null,"abstract":"Tropical cyclones are the world’s deadliest natural disasters, especially causing tree death by pulling out or breaking the roots of trees, which has a great impact on the forest ecosystem and forest owners. To minimize additional damage, an efficient approach is needed to quickly grasp information on the location and distribution of fallen trees. There are several studies that try to detect fallen trees in the past, but most of the research requires huge costs and is difficult to utilize. This research focuses on resolving those problems. Unmanned aerial vehicle (UAV) is widely used for ground detection for those who need a cost-effective way while pursuing high-resolution images. To take this advantage, this research collects data mainly using a UAV with an auxiliary high-resolution camera. The collected data is used for training the YOLOX model, an object detection algorithm, which can perform an accurate detection within a remarkably short time period. Also, by using YOLOX as a detection model, a wide-range versatility is obtained, which means, the solution driven by this research can be utilized for every scenario where inexpensive, but highly reliable object detection result is needed. This research implements a visualization application that displays detection results, calculated by a trained model, in a client-friendly way. Fallen trees are recognized in images or videos, and the analyzed results are provided as web-based visualizations.","PeriodicalId":43471,"journal":{"name":"International Journal of Semantic Computing","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135598026","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 : 2023-08-03DOI: 10.1142/s1793351x23620015
Marvin Brenner, Peter Stutz
This work provides the fundament for a gesture-based interaction system between cargo-handling unmanned aerial vehicles (UAVs) and ground personnel. It enables novice operators to visually communicate commands with higher abstractions through a minimum number of necessary gestures. The interaction concept intends to transfer two goal-directed control techniques to a cargo-handling use case: Selecting objects via deictic pointing communicates intention and a single proxy manipulation gesture controls the UAV’s flight. A visual processing pipeline built around an RGB-D sensor is presented and its subordinate components like lightweight object detectors and human pose estimation methods are benchmarked on the UAV-Human dataset. The results provide an overview of suitable methods for 3D gesture-based human drone interaction. A first unoptimized model ensemble runs with 7[Formula: see text]Hz on a Jetson Orin AGX Developer Kit.
该工作为货物搬运无人机与地面人员之间基于手势的交互系统提供了基础。它使新手操作员能够通过最少数量的必要手势，以更高的抽象直观地传达命令。交互概念旨在将两种目标导向控制技术转移到货物处理用例中:通过指示指向选择对象来传达意图，而单个代理操作手势控制无人机的飞行。提出了一种围绕RGB-D传感器构建的视觉处理管道，并在无人机-人类数据集上对其下属组件如轻型目标检测器和人体姿态估计方法进行了基准测试。研究结果为基于3D手势的人类无人机交互提供了合适的方法概述。第一个未优化的模型集合在Jetson Orin AGX Developer Kit上以7 Hz运行。
{"title":"Towards gesture-based cooperation with cargo handling unmanned aerial vehicles","authors":"Marvin Brenner, Peter Stutz","doi":"10.1142/s1793351x23620015","DOIUrl":"https://doi.org/10.1142/s1793351x23620015","url":null,"abstract":"This work provides the fundament for a gesture-based interaction system between cargo-handling unmanned aerial vehicles (UAVs) and ground personnel. It enables novice operators to visually communicate commands with higher abstractions through a minimum number of necessary gestures. The interaction concept intends to transfer two goal-directed control techniques to a cargo-handling use case: Selecting objects via deictic pointing communicates intention and a single proxy manipulation gesture controls the UAV’s flight. A visual processing pipeline built around an RGB-D sensor is presented and its subordinate components like lightweight object detectors and human pose estimation methods are benchmarked on the UAV-Human dataset. The results provide an overview of suitable methods for 3D gesture-based human drone interaction. A first unoptimized model ensemble runs with 7[Formula: see text]Hz on a Jetson Orin AGX Developer Kit.","PeriodicalId":43471,"journal":{"name":"International Journal of Semantic Computing","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136228731","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 : 2023-07-31DOI: 10.1142/s1793351x23630023
Maximilian Kertel, Stefan Harmeling, Markus Pauly, Nadja Klein
Many production processes are characterized by numerous and complex cause-and-effect relationships. Since they are only partially known, they pose a challenge to effective process control. In this work we present how Structural Equation Models can be used for deriving cause-and-effect relationships from the combination of prior knowledge and process data in the manufacturing domain. Compared to earlier applications, we do not assume linear relationships leading to more informative results. Furthermore, our results indicate that including expert knowledge seems to be able to reduce the difference between the learned cause-effect relationships and the expert assessment, thus opening a promising direction for future research on manufacturing processes.
{"title":"Learning Causal Graphs in Manufacturing Domains using Structural Equation Models","authors":"Maximilian Kertel, Stefan Harmeling, Markus Pauly, Nadja Klein","doi":"10.1142/s1793351x23630023","DOIUrl":"https://doi.org/10.1142/s1793351x23630023","url":null,"abstract":"Many production processes are characterized by numerous and complex cause-and-effect relationships. Since they are only partially known, they pose a challenge to effective process control. In this work we present how Structural Equation Models can be used for deriving cause-and-effect relationships from the combination of prior knowledge and process data in the manufacturing domain. Compared to earlier applications, we do not assume linear relationships leading to more informative results. Furthermore, our results indicate that including expert knowledge seems to be able to reduce the difference between the learned cause-effect relationships and the expert assessment, thus opening a promising direction for future research on manufacturing processes.","PeriodicalId":43471,"journal":{"name":"International Journal of Semantic Computing","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135100240","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 : 2023-07-29DOI: 10.1142/s1793351x23020038
D. D’Auria
{"title":"Guest Editorial: Special Issue on Robotic Computing","authors":"D. D’Auria","doi":"10.1142/s1793351x23020038","DOIUrl":"https://doi.org/10.1142/s1793351x23020038","url":null,"abstract":"","PeriodicalId":43471,"journal":{"name":"International Journal of Semantic Computing","volume":"18 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90445934","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 : 2023-06-09DOI: 10.1142/s1793351x23300029
Madjid Maidi, S. Otmane, Yassine Lehiani, Marius Preda
{"title":"Object Detection enhanced by Hybrid Overlay for Augmented Reality","authors":"Madjid Maidi, S. Otmane, Yassine Lehiani, Marius Preda","doi":"10.1142/s1793351x23300029","DOIUrl":"https://doi.org/10.1142/s1793351x23300029","url":null,"abstract":"","PeriodicalId":43471,"journal":{"name":"International Journal of Semantic Computing","volume":"7 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82128990","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 : 2023-06-09DOI: 10.1142/s1793351x23620040
Petrit Rama, N. Bajçinca
{"title":"Maneuver Prediction using Traffic Scene Graphs via Graph Neural Networks and Recurrent Neural Networks","authors":"Petrit Rama, N. Bajçinca","doi":"10.1142/s1793351x23620040","DOIUrl":"https://doi.org/10.1142/s1793351x23620040","url":null,"abstract":"","PeriodicalId":43471,"journal":{"name":"International Journal of Semantic Computing","volume":"222 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80473622","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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