AI Communications最新文献_第2页

GWS: Rotation object detection in aerial remote sensing images based on Gauss–Wasserstein scattering GWS：基于高斯-瓦瑟斯坦散射的航空遥感图像旋转物体检测

IF 0.8 4区计算机科学

AI Communications Pub Date : 2023-12-11 DOI: 10.3233/aic-230135

Ling Gan, Xiaodong Tan, Liuhui Hu

{"title":"GWS: Rotation object detection in aerial remote sensing images based on Gauss–Wasserstein scattering","authors":"Ling Gan, Xiaodong Tan, Liuhui Hu","doi":"10.3233/aic-230135","DOIUrl":"https://doi.org/10.3233/aic-230135","url":null,"abstract":"The majority of existing rotating target detectors inherit the horizontal detection paradigm and design the rotational regression loss based on the inductive paradigm. But the loss design limitation of the inductive paradigm makes these detectors hardly detect effectively tiny targets with high accuracy, particularly for large-aspect-ratio objects. Therefore, in view of the fact that horizontal detection is a special scenario of rotating target detection and based on the relationship between rotational and horizontal detection, we shift from an inductive to a deductive paradigm of design to develop a new regression loss function named Gauss–Wasserstein scattering (GWS). First, the rotating bounding box is transformed into a two-dimensional Gaussian distribution, and then the regression losses between Gaussian distributions are calculated as the Wasserstein scatter; By analyzing the gradient of centroid regression, centroid regression is shown to be able to adjust gradients dynamically based on object characteristics, and small targets requiring high accuracy detection rely on this mechanism, and more importantly, it is further demonstrated that GWS is scale-invariant while possessing an explicit regression logic. The method is performed on a large public remote sensing dataset DOTA and two popular detectors and achieves a large accuracy improvement in both large aspect ratio targets and small targets detection compared to similar methods.","PeriodicalId":50835,"journal":{"name":"AI Communications","volume":"14 2","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138981133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A logical modeling of the Yōkai board game 阳海棋盘游戏的逻辑建模

IF 0.8 4区计算机科学

AI Communications Pub Date : 2023-11-28 DOI: 10.3233/aic-230050

Jorge Fernandez, Dominique Longin, Emiliano Lorini, Frédéric Maris

引用次数: 0

On the links between belief merging, the Borda voting method, and the cancellation property 1 论信念合并、Borda投票法与消去性之间的联系

IF 0.8 4区计算机科学

AI Communications Pub Date : 2023-11-17 DOI: 10.3233/aic-220306

Patricia Everaere, Chouaib Fellah, Sébastien Konieczny, Ramón Pino Pérez

引用次数: 0

Agents in Traffic and Transportation (ATT 2022): Revised and Extended Papers 交通运输代理(ATT 2022):修订和扩展文件

4区计算机科学

AI Communications Pub Date : 2023-11-14 DOI: 10.3233/aic-239001

Ana L.C. Bazzan, Ivana Dusparic, Marin Lujak, Giuseppe Vizzari

引用次数: 0

An adaptive threshold based gait authentication by incorporating quality measures 结合质量度量的自适应阈值步态认证

4区计算机科学

AI Communications Pub Date : 2023-10-25 DOI: 10.3233/aic-230121

Sonia Das, Sukadev Meher, Upendra Kumar Sahoo

引用次数: 0

Effective training to improve DeepPilot 有效的培训，以提高DeepPilot

4区计算机科学

AI Communications Pub Date : 2023-10-24 DOI: 10.3233/aic-230065

L. Oyuki Rojas-Perez, Jose Martinez-Carranza

{"title":"Effective training to improve DeepPilot","authors":"L. Oyuki Rojas-Perez, Jose Martinez-Carranza","doi":"10.3233/aic-230065","DOIUrl":"https://doi.org/10.3233/aic-230065","url":null,"abstract":"We present an approach to autonomous drone racing inspired by how a human pilot learns a race track. Human pilots drive around the track multiple times to familiarise themselves with the track and find key points that allow them to complete the track without the risk of collision. This paper proposes a three-stage approach: exploration, navigation, and refinement. Our approach does not require prior knowledge about the race track, such as the number of gates, their positions, and their orientations. Instead, we use a trained neural pilot called DeepPilot to return basic flight commands from camera images where a gate is visible to navigate an unknown race track and a Single Shot Detector to visually detect the gates during the exploration stage to identify points of interest. These points are then used in the navigation stage as waypoints in a flight controller to enable faster flight and navigate the entire race track. Finally, in the refinement stage, we use the methodology developed in stages 1 and 2, to generate novel data to re-train DeepPilot, which produces more realistic manoeuvres for when the drone has to cross a gate. In this sense, similar to the original work, rather than generating examples by flying in a full track, we use small tracks of three gates to discover effective waypoints to be followed by the waypoint controller. This produces novel training data for DeepPilot without human intervention. By training with this new data, DeepPilot significantly improves its performance by increasing its flight speed twice w.r.t. its original version. Also, for this stage 3, we required 66 % less training data than in the original DeepPilot without compromising the effectiveness of DeepPilot to enable a drone to autonomously fly in a racetrack.","PeriodicalId":50835,"journal":{"name":"AI Communications","volume":"33 1-2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135266791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Lifetime policy reuse and the importance of task capacity 生命周期策略重用和任务容量的重要性

4区计算机科学

AI Communications Pub Date : 2023-10-24 DOI: 10.3233/aic-230040

David M. Bossens, Adam J. Sobey

引用次数: 0

Dual cross-domain session-based recommendation with multi-channel integration 双跨域会话推荐，多通道集成

4区计算机科学

AI Communications Pub Date : 2023-10-13 DOI: 10.3233/aic-230084

Jinjin Zhang, Xiang Hua, Peng Zhao, Kai Kang

{"title":"Dual cross-domain session-based recommendation with multi-channel integration","authors":"Jinjin Zhang, Xiang Hua, Peng Zhao, Kai Kang","doi":"10.3233/aic-230084","DOIUrl":"https://doi.org/10.3233/aic-230084","url":null,"abstract":"Session-based recommendation aims at predicting the next behavior when the current interaction sequence is given. Recent advances evaluate the effectiveness of dual cross-domain information for the session-based recommendation. However, we discover that accurately modeling the session representations is still a challenging problem due to the complexity of preference interactions in the cross-domain, and various methods are proposed to only model the common features of cross-domain, while ignoring the specific features and enhanced features for the dual cross-domain. Without modeling the complete features, the existing methods suffer from poor recommendation accuracy. Therefore, we propose an end-to-end dual cross-domain with multi-channel interaction model (DCMI), which utilizes dual cross-domain session information and multiple preference interaction encoders, for session-based recommendation. In DCMI, we apply a graph neural network to generate the session global preference and local preference. Then, we design a cross-preference interaction module to capture the common, specific, and enhanced features for cross-domain sessions with local preferences and global preferences. Finally, we combine multiple preferences with a bilinear fusion mechanism to characterize and make recommendations. Experimental results on the Amazon dataset demonstrate the superiority of the DCMI model over the state-of-the-art methods.","PeriodicalId":50835,"journal":{"name":"AI Communications","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135805221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Conflagration-YOLO: a lightweight object detection architecture for conflagration fire - yolo:用于fire的轻量级对象检测体系结构

4区计算机科学

AI Communications Pub Date : 2023-10-13 DOI: 10.3233/aic-230094

Ning Sun, Pengfei Shen, Xiaoling Ye, Yifei Chen, Xiping Cheng, Pingping Wang, Jie Min

{"title":"Conflagration-YOLO: a lightweight object detection architecture for conflagration","authors":"Ning Sun, Pengfei Shen, Xiaoling Ye, Yifei Chen, Xiping Cheng, Pingping Wang, Jie Min","doi":"10.3233/aic-230094","DOIUrl":"https://doi.org/10.3233/aic-230094","url":null,"abstract":"Fire monitoring of fire-prone areas is essential, and in order to meet the requirements of edge deployment and the balance of fire recognition accuracy and speed, we design a lightweight fire recognition network, Conflagration-YOLO. Conflagration-YOLO is constructed by depthwise separable convolution and more attention to fire feature information extraction from a three-dimensional(3D) perspective, which improves the network feature extraction capability, achieves a balance of accuracy and speed, and reduces model parameters. In addition, a new activation function is used to improve the accuracy of fire recognition while minimizing the inference time of the network. All models are trained and validated on a custom fire dataset and fire inference is performed on the CPU. The mean Average Precision(mAP) of the proposed model reaches 80.92%, which has a great advantage compared with Faster R-CNN. Compared with YOLOv3-Tiny, the proposed model decreases the number of parameters by 5.71 M and improves the mAP by 6.67%. Compared with YOLOv4-Tiny, the number of parameters decreases by 3.54 M, mAP increases by 8.47%, and inference time decreases by 62.59 ms. Compared with YOLOv5s, the difference in the number of parameters is nearly twice reduced by 4.45 M and the inference time is reduced by 41.87 ms. Compared with YOLOX-Tiny, the number of parameters decreases by 2.5 M, mAP increases by 0.7%, and inference time decreases by 102.49 ms. Compared with YOLOv7, the number of parameters decreases significantly and the balance of accuracy and speed is achieved. Compared with YOLOv7-Tiny, the number of parameters decreases by 3.64 M, mAP increases by 0.5%, and inference time decreases by 15.65 ms. The experiment verifies the superiority and effectiveness of Conflagration-YOLO compared to the state-of-the-art (SOTA) network model. Furthermore, our proposed model and its dimensional variants can be applied to computer vision downstream target detection tasks in other scenarios as required.","PeriodicalId":50835,"journal":{"name":"AI Communications","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135805222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Transferring experiences in k-nearest neighbors based multiagent reinforcement learning: an application to traffic signal control 基于k近邻的多智能体强化学习的经验传递:在交通信号控制中的应用

4区计算机科学

AI Communications Pub Date : 2023-09-27 DOI: 10.3233/aic-220305

Ana Lucia C. Bazzan, Vicente N. de Almeida, Monireh Abdoos

{"title":"Transferring experiences in k-nearest neighbors based multiagent reinforcement learning: an application to traffic signal control","authors":"Ana Lucia C. Bazzan, Vicente N. de Almeida, Monireh Abdoos","doi":"10.3233/aic-220305","DOIUrl":"https://doi.org/10.3233/aic-220305","url":null,"abstract":"The increasing demand for mobility in our society poses various challenges to traffic engineering, computer science in general, and artificial intelligence in particular. Increasing the capacity of road networks is not always possible, thus a more efficient use of the available transportation infrastructure is required. Another issue is that many problems in traffic management and control are inherently decentralized and/or require adaptation to the traffic situation. Hence, there is a close relationship to multiagent reinforcement learning. However, using reinforcement learning poses the challenge that the state space is normally large and continuous, thus it is necessary to find appropriate schemes to deal with discretization of the state space. To address these issues, a multiagent system with agents learning independently via a learning algorithm was proposed, which is based on estimating Q-values from k-nearest neighbors. In the present paper, we extend this approach and include transfer of experiences among the agents, especially when an agent does not have a good set of k experiences. We deal with traffic signal control, running experiments on a traffic network in which we vary the traffic situation along time, and compare our approach to two baselines (one involving reinforcement learning and one based on fixed times). Our results show that the extended method pays off when an agent returns to an already experienced traffic situation.","PeriodicalId":50835,"journal":{"name":"AI Communications","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135586495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0