標(biāo)題: Titlebook: Artificial Intelligence and Robotics; Huimin Lu Book 2021 Springer Nature Switzerland AG 2021 Computational Intelligence.Intelligent Syste [打印本頁(yè)] 作者: BRISK 時(shí)間: 2025-3-21 16:14
書(shū)目名稱Artificial Intelligence and Robotics影響因子(影響力)
書(shū)目名稱Artificial Intelligence and Robotics影響因子(影響力)學(xué)科排名
書(shū)目名稱Artificial Intelligence and Robotics網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱Artificial Intelligence and Robotics網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱Artificial Intelligence and Robotics被引頻次
書(shū)目名稱Artificial Intelligence and Robotics被引頻次學(xué)科排名
書(shū)目名稱Artificial Intelligence and Robotics年度引用
書(shū)目名稱Artificial Intelligence and Robotics年度引用學(xué)科排名
書(shū)目名稱Artificial Intelligence and Robotics讀者反饋
書(shū)目名稱Artificial Intelligence and Robotics讀者反饋學(xué)科排名
作者: Gesture 時(shí)間: 2025-3-21 22:12 作者: Lucubrate 時(shí)間: 2025-3-22 04:12
https://doi.org/10.1007/978-3-658-09387-7ant to control the crowd number. In this paper, we address the problem of crowd counting in the crowded scene. Our model accurately estimated the count of people in the crowded scene. Firstly, we proposed a novel and simple convolutional neural network, called Global Counting CNN (GCCNN). The GCCNN 作者: indices 時(shí)間: 2025-3-22 07:09
Beispiele zu den Grundbeanspruchungsarten,nuclei segmentation is to manually label a huge amount of cell images, which is a labor-intensive and time-consuming process. This paper develops a semi-supervised learning approach to reduce the dependence on the amount of labeled images, and it consists of three main steps. First, cell regions are作者: Kernel 時(shí)間: 2025-3-22 10:10 作者: Meager 時(shí)間: 2025-3-22 13:00
https://doi.org/10.1007/978-3-322-88356-8xisting approaches reported in the literature, our work is characterized with a number of novel features: (i) the high level video event modeling and recognition based on Petri net are fully automatic, which are not only capable of covering single video events but also multiple ones without limit; (作者: GLOSS 時(shí)間: 2025-3-22 19:25
Beschreibung des Programmsystems FEMPA,n-like dialogues. The responses proposed by the chat-bot are only a passive answer or assentation, which does not arouse the desire of people to continue communicating. To address this challenge, in this paper, we propose a question generalization method with three types of question proposing scheme作者: 健談 時(shí)間: 2025-3-22 23:21 作者: 毀壞 時(shí)間: 2025-3-23 05:03
https://doi.org/10.1007/978-3-642-25123-8application scenarios, there is a fundamental challenge that how to guarantee the discriminative ability of feature from vary inputs for face verification task. Aiming at this problem, we proposed a context-aware based discriminative siamese neural network for face verification. In fact, the structu作者: 糾纏 時(shí)間: 2025-3-23 08:26
In Vivo Research and Development: 1976–1986ferent multi-source images, we propose a new method of object-level matching for multi-source image based on improved dictionary learning. Two main steps, unified representation and similarity measure, are contained. Firstly, we complete the unified representation of multi-source images by improved 作者: ACTIN 時(shí)間: 2025-3-23 10:07 作者: PARA 時(shí)間: 2025-3-23 15:43 作者: START 時(shí)間: 2025-3-23 18:42
FET Modeling for Circuit Simulationfinition of rice growth quantity, which takes into account the periodic growth and the key growth indexes of each stage, so as to characterize the growth of rice in each growing period. Elman neural network was used to determine the relationship between environmental factors and growth in each growt作者: DOLT 時(shí)間: 2025-3-23 23:45
Sitzung 3: Alltagsstrukturierungcy details, which is insufficient to reconstruct high-resolution image. To address this problem, we propose a multi-scale progressive image super-resolution reconstruction network (MSPN) based on the asymmetric Laplacian pyramid structure. Our proposed network allows us to separate the difficult pro作者: 緩和 時(shí)間: 2025-3-24 04:51 作者: Inoperable 時(shí)間: 2025-3-24 07:19 作者: Parabola 時(shí)間: 2025-3-24 13:43
Sitzung 3: Alltagsstrukturierungce rules of compound formulas in the world hinders its application in practical systems.This paper presents an automatic evaluation algorithm of modal logic compound formula based on Haskell functional programming to solve the problem of evaluating modal logic compound formula in the possible world.作者: 影響 時(shí)間: 2025-3-24 18:55
Klaus Feyrer,Karl-Heinz Wehkingnel reconstruction based on the sparsity. Based on the subspace tracking sparse reconstruction algorithm, an adaptive sparsity subspace tracking (ASSP) algorithm is proposed. The algorithm obtains sparsity according to the principle of minimum residua, accurately estimates the ultra-wideband channel作者: 健忘癥 時(shí)間: 2025-3-24 22:35 作者: FISC 時(shí)間: 2025-3-24 23:25 作者: savage 時(shí)間: 2025-3-25 03:56
Studies in Computational Intelligencehttp://image.papertrans.cn/b/image/162271.jpg作者: Externalize 時(shí)間: 2025-3-25 10:07
Artificial Intelligence and Robotics978-3-030-56178-9Series ISSN 1860-949X Series E-ISSN 1860-9503 作者: 掃興 時(shí)間: 2025-3-25 13:04
https://doi.org/10.1007/978-3-030-56178-9Computational Intelligence; Intelligent Systems; Artificial Intelligence; Robotics; ISAIR; ISAIR 2019作者: 斜 時(shí)間: 2025-3-25 16:53 作者: 沒(méi)花的是打擾 時(shí)間: 2025-3-25 22:28 作者: linear 時(shí)間: 2025-3-26 03:58 作者: 極肥胖 時(shí)間: 2025-3-26 05:36 作者: Frisky 時(shí)間: 2025-3-26 11:32
,A Semi-supervised Learning Method for?Automatic Nuclei Segmentation Using?Generative Adversarial Nenuclei segmentation is to manually label a huge amount of cell images, which is a labor-intensive and time-consuming process. This paper develops a semi-supervised learning approach to reduce the dependence on the amount of labeled images, and it consists of three main steps. First, cell regions are作者: Notify 時(shí)間: 2025-3-26 12:50 作者: patriarch 時(shí)間: 2025-3-26 19:54 作者: originality 時(shí)間: 2025-3-26 21:11 作者: Spinal-Tap 時(shí)間: 2025-3-27 01:37
Optimal Scheduling of IoT Tasks in Cloud-Fog Computing Networks, due to their real-time requirements. Fog computing aims at forming the idle edge devices that are in the vicinity of IoT end devices as instantaneous small-scale Fog networks (Fogs), so as to provide one-hop services to satisfy the real-time requirement. Since Fogs may consist of only wireless node作者: gospel 時(shí)間: 2025-3-27 07:08 作者: 意見(jiàn)一致 時(shí)間: 2025-3-27 11:43
Object-Level Matching for Multi-source Image Using Improved Dictionary Learning Algorithm,ferent multi-source images, we propose a new method of object-level matching for multi-source image based on improved dictionary learning. Two main steps, unified representation and similarity measure, are contained. Firstly, we complete the unified representation of multi-source images by improved 作者: 讓步 時(shí)間: 2025-3-27 14:13
Classification of Hyperspectral Image Based on Shadow Enhancement by Dynamic Stochastic Resonance,nce (DSR) theory has proved that the noise contained in the signal can enhance the strength of the original signal and improve the signal-to-noise ratio (SNR). And it has been applied in signal and image processing,communication and other fields. In this paper, DSR theory is introduced to the shadow作者: Provenance 時(shí)間: 2025-3-27 19:27
Generative Image Inpainting,ach for image inpainting in this paper. The completion model contains one generator and double discriminators. The generator is the architecture of AutoEncoders with skip connection and the discriminators are simple convolutional neural networks architecture. Wasserstein GAN loss is used to ensure o作者: 戲服 時(shí)間: 2025-3-27 22:58
Rice Growth Prediction Based on Periodic Growth,finition of rice growth quantity, which takes into account the periodic growth and the key growth indexes of each stage, so as to characterize the growth of rice in each growing period. Elman neural network was used to determine the relationship between environmental factors and growth in each growt作者: dainty 時(shí)間: 2025-3-28 02:24
A Multi-scale Progressive Method of Image Super-Resolution,cy details, which is insufficient to reconstruct high-resolution image. To address this problem, we propose a multi-scale progressive image super-resolution reconstruction network (MSPN) based on the asymmetric Laplacian pyramid structure. Our proposed network allows us to separate the difficult pro作者: BROTH 時(shí)間: 2025-3-28 09:05
Track Related Bursty Topics in Weibo,n Weibo, but cannot track the related bursty topics. Based on the time series of Weibo, the binary word pair is used for topic modeling and the bursty topics in the Weibo are extracted to form a new topic set. And then, the similarity of the topics in adjacent time periods can be calculated by KL me作者: Ardent 時(shí)間: 2025-3-28 12:12 作者: –吃 時(shí)間: 2025-3-28 14:35
An Automatic Evaluation Method for Modal Logic Combination Formula,ce rules of compound formulas in the world hinders its application in practical systems.This paper presents an automatic evaluation algorithm of modal logic compound formula based on Haskell functional programming to solve the problem of evaluating modal logic compound formula in the possible world.作者: JUST 時(shí)間: 2025-3-28 19:59
Research on CS-Based Channel Estimation Algorithm for UWB Communications,nel reconstruction based on the sparsity. Based on the subspace tracking sparse reconstruction algorithm, an adaptive sparsity subspace tracking (ASSP) algorithm is proposed. The algorithm obtains sparsity according to the principle of minimum residua, accurately estimates the ultra-wideband channel作者: DEMN 時(shí)間: 2025-3-29 00:52
A New Unambiguous Acquisition Algorithm for BOC(n, n) Signals,s paper proposes a new fuzzy-free capture algorithm for BOC(n, n) signals. Divide the local BOC signal into two branch signals. Then, correlating the tributary signal with the received BOC signal, and the correlation function is then shifted by a quarter and three quarters of the chip, respectively,作者: 卜聞 時(shí)間: 2025-3-29 05:10 作者: 取之不竭 時(shí)間: 2025-3-29 10:32 作者: Atrium 時(shí)間: 2025-3-29 12:22 作者: 越自我 時(shí)間: 2025-3-29 15:53 作者: happiness 時(shí)間: 2025-3-29 20:25 作者: 熱情的我 時(shí)間: 2025-3-30 02:22
Track Related Bursty Topics in Weibo,tric and the related bursty topics can be tracked. The experimental results show that the method can effectively segment the time series of Weibo topics, and realize the discovery and tracking of related topics in Weibo.作者: asthma 時(shí)間: 2025-3-30 07:29
Terrain Classification Algorithm for Lunar Rover Based on Visual Convolutional Neural Network,better path, avoiding the unnecessary trouble caused by the delay of the land-month communication. The overall accuracy of our classification is 80%, and some of them have higher precision. It is expected that the classification results will help the decision of path planning.作者: ostensible 時(shí)間: 2025-3-30 11:08 作者: ALLAY 時(shí)間: 2025-3-30 14:34 作者: 值得 時(shí)間: 2025-3-30 16:37
Book 2021o brought new challenges for the artificial intelligence community. Sharing recent advances in the field, the book features papers by young researchers presented at the 4th International Symposium on Artificial Intelligence and Robotics 2019 (ISAIR2019), held in Daegu, Korea, on August 20–24, 2019...?.作者: 尊嚴(yán) 時(shí)間: 2025-3-30 23:31 作者: cogitate 時(shí)間: 2025-3-31 01:40 作者: 嘲弄 時(shí)間: 2025-3-31 06:25
https://doi.org/10.1007/978-3-658-09387-7erence values in the perspective map. In general, it makes the final density map more accurate. The dataset we used is a set of public dataset, which are WorldExpo’10 dataset, Shanghaitech dataset, the UCF_CC_50 dataset and the UCSD dataset. The experiments have proved our method achieves the state-of-the-art result over other algorithms.作者: 畸形 時(shí)間: 2025-3-31 10:44 作者: figurine 時(shí)間: 2025-3-31 14:00 作者: osculate 時(shí)間: 2025-3-31 18:49
CBCNet: A Deep Learning Approach to Urban Images Classification in Urban Computing,ban images containing eight categories, contrast experiments prove that the dataset is rational and feasibility. Experiment results obtained on two benchmark datasets demonstrate that classification accuracy and computation of CBCNet outperform those by the previous state-of-the-art items of AlexNet, VGGNet16 and ResNet50.作者: 指耕作 時(shí)間: 2025-4-1 00:12 作者: 縱欲 時(shí)間: 2025-4-1 02:19 作者: 恫嚇 時(shí)間: 2025-4-1 09:26 作者: preeclampsia 時(shí)間: 2025-4-1 12:11
