Priori Knowledge Research Articles

An Imitation Learning Based Algorithm Enabling Priori Knowledge Transfer in Modern Electricity Markets for Bayesian Nash Equilibrium Estimation

An Imitation Learning Based Algorithm Enabling Priori Knowledge Transfer in Modern Electricity Markets for Bayesian Nash Equilibrium Estimation

EPK-CLIP: External and Priori Knowledge CLIP for action recognition

Contrastive Language-Image Pretraining (CLIP) models have achieved significant success and have markedly improved the performance of various downstream tasks, including action recognition. However, how to effectively introduce knowledge into the field of action recognition remains an open question. In this work, an External and Priori Knowledge CLIP (EPK-CLIP) is proposed to introduce external knowledge into the model. To capture external knowledge, an external knowledge embedding module is proposed, which can generate and utilize human-object interaction relations as external knowledge, enabling the model to learn better features. Furthermore, the sparse regularization is introduced in the loss function, endowing the model with the ability to exploit the sparse priori knowledge inherent in the classification task. Finally, multiple inference module is proposed to obtain classification results from both direct and indirect perspectives. Specifically, the final classification result is obtained by fusing the outputs of different reasoning modules. Moreover, four external knowledge datasets: Kinetics-400-VC, Jester-VC, HMDB-51-VC, and UCF-101-VC are built and released for public usage, which is a multimodal extension of corresponding action datasets respectively. Under fully-supervised settings, our model achieves the top-1 accuracy of 84.3%, 97.1%, 82.9%, and 98.2% on Kinetics-400, Jester, HMDB-51, and UCF-101, respectively. In zero-shot experiments, our model also achieves state-of-the-art results, with top-1 accuracy of 51.6% and 77.7% on HMDB-51 and UCF-101, respectively. All related datasets and code can be found at https://github.com/geek12138/EPK-CLIP.

Flying Projectile Attitude Determination from Ground-Based Monocular Imagery with a Priori Knowledge

This paper discusses using ground-based imagery to determine the attitude of a flying projectile assuming prior knowledge of its external geometry. It presents a segmentation-based approach to follow the object and evaluates it quantitatively with simulated data and qualitatively with both simulated and real data. Two experimental cases are considered: One assumes reliable target distance measurement from an auxiliary range sensor, while the other assumes no range information. The results show that in the case of an unknown projectile–camera distance, with projectile dimensions of 1.378 m and 0.08 m in length and diameter, the estimated distance, in-plane location, and pitch angle accuracies are about 50 m, 0.15 m, and 6 degrees, respectively. Yaw angle estimation is ambiguous. In the second case, assuming that the projectile–camera distance is known resolves the ambiguity of yaw estimation, resulting in accuracies of about 0.15 m, 3 degrees, and 20 degrees for in-plane location, pitch, and yaw angles, respectively. These accuracies were normalized to a 1-km projectile–camera distance.

Perspective: Microwave Medical Imaging Using Space-Time-Frequency A Priori Knowledge for Health Monitoring

Perspective: Microwave Medical Imaging Using Space-Time-Frequency A Priori Knowledge for Health Monitoring

Finite Set Sensorless Control With Minimum a Priori Knowledge and Tuning Effort for Interior Permanent-Magnet Synchronous Motors

Finite Set Sensorless Control With Minimum a Priori Knowledge and Tuning Effort for Interior Permanent-Magnet Synchronous Motors

Habits of Phantasy and the Possibility of A Priori Knowledge

Habits of Phantasy and the Possibility of A Priori Knowledge

Retracted: An Enhanced Priori Knowledge GAN for CT Images Generation of Early Lung Nodules with Small-Size Labelled Samples.

[This retracts the article DOI: 10.1155/2022/2129303.].

FLPK-BiSeNet: Federated Learning Based on Priori Knowledge and Bilateral Segmentation Network for Image Edge Extraction

Federated learning can effectively ensure data security and improve the problem of data islanding. However, the performance of federated learning-based schemes could be better due to the imbalance of image data. Therefore, this paper proposes a federated learning approach based on priori knowledge and a bilateral segmentation network for image edge extraction. First, federated learning can distribute training images for some special complex images due to the small sample and unshared data. Then, the image with similar edge information to the original image is learned to obtain prior knowledge, and the local uniform sparsity method is used to strengthen the detail features and weaken the background features. Based on the bilateral segmentation network, we introduce a dilated pyramid pooling layer and multi-scale feature fusion module to fuse the shallow detailed features in the context path with the deep abstract features obtained through the dilated pyramid pooling. The final result is obtained by fusing the result with prior knowledge and the result with the context path. Finally, we conduct experiments on some public datasets, and the results show that the proposed method greatly improves extraction accuracy compared with the traditional and the most advanced methods.

A Priori Knowledge Based Ground Moving Target Indication Technique Applied to Distributed Spaceborne SAR System

Through formation flying, the distributed spaceborne SAR(synthetic aperture radar) system can increase the number of spatial degree of freedoms (DOFs) and provide flexible multi-baselines for SAR-GMTI (ground moving target indication), which improves the system performance. This paper proposes an a priori knowledge-based adaptive clutter cancellation and moving target detection technique applied to the distributed spaceborne SAR-GMTI systems. Firstly, the adaptive clutter cancellation technique is exploited to suppress the ground clutter. A priori knowledge, such as road network information, is integrated to the adaptive clutter cancellation processor to reduce any moving target steering vector mismatch. Secondly, adaptive matched filter (AMF) and adaptive beamformer orthogonal rejection test (ABORT) are exploited as adaptive detection techniques for moving target detection. Due to the dense road network, the moving target steering vector estimation may be ambiguous for the different position and orientation of the roads. The multiple hypothesis testing (MHT) technique is proposed to detect the moving targets and resolve the potential ambiguities. A scheme is exploited to detect, classify, and relocate the moving targets. Finally, simulation experiments and performance analysis have demonstrated the effectiveness and robustness of the proposed technique.

An Abstractive Summarization Model Based on Joint-Attention Mechanism and a Priori Knowledge

An abstractive summarization model based on the joint-attention mechanism and a priori knowledge is proposed to address the problems of the inadequate semantic understanding of text and summaries that do not conform to human language habits in abstractive summary models. Word vectors that are most relevant to the original text should be selected first. Second, the original text is represented in two dimensions—word-level and sentence-level, as word vectors and sentence vectors, respectively. After this processing, there will be not only a relationship between word-level vectors but also a relationship between sentence-level vectors, and the decoder discriminates between word-level and sentence-level vectors based on their relationship with the hidden state of the decoder. Then, the pointer generation network is improved using a priori knowledge. Finally, reinforcement learning is used to improve the quality of the generated summaries. Experiments on two classical datasets, CNN/DailyMail and DUC 2004, show that the model has good performance and effectively improves the quality of generated summaries.

Fusion of Multisource Satellite AOD Products via Bayesian Maximum Entropy With Explicit A Priori Knowledge

Fusion of Multisource Satellite AOD Products via Bayesian Maximum Entropy With Explicit A Priori Knowledge

Spherical Centralized Quantization for Fast Image Retrieval.

Existing supervised quantization methods usually learn the quantizers from pair-wise, triplet, or anchor-based losses, which only capture their relationship locally without aligning them globally. This may cause an inadequate use of the entire space and a severe intersection among different semantics, leading to inferior retrieval performance. Furthermore, to enable quantizers to learn in an end-to-end way, current practices usually relax the non-differentiable quantization operation by substituting it with softmax, which unfortunately is biased, leading to an unsatisfying suboptimal solution. To address the above issues, we present Spherical Centralized Quantization (SCQ), which contains a Priori Knowledge based Feature (PKFA) module for the global alignment of feature vectors, and an Annealing Regulation Semantic Quantization (ARSQ) module for low-biased optimization. Specifically, the PKFA module first applies Semantic Center Allocation (SCA) to obtain semantic centers based on prior knowledge, and then adopts Centralized Feature Alignment (CFA) to gather feature vectors based on corresponding semantic centers. The SCA and CFA globally optimize the inter-class separability and intra-class compactness, respectively. After that, the ARSQ module performs a partial-soft relaxation to tackle biases, and an Annealing Regulation Quantization loss for further addressing the local optimal solution. Experimental results show that our SCQ outperforms state-of-the-art algorithms by a large margin (2.1%, 3.6%, 5.5% mAP respectively) on CIFAR-10, NUS-WIDE, and ImageNet with a code length of 8 bits. Codes are publicly available:https://github.com/zzb111/Spherical-Centralized-Quantization.

Autonomous Generation of Service Strategy for Household Tasks: A Progressive Learning Method With A Priori Knowledge and Reinforcement Learning

Human beings tend to learn unknown knowledge in a gradual process, from the basic to the complex. Based on this point, we propose a progressive learning method for producing service strategies according to requests, with a hierarchical priori knowledge and reinforcement learning. Service strategy aims to guide how to perform home services and takes into consideration the relationship between actions and objects in home environment. In this paper, strategy generation is regarded as a text generation problem in question answering (QA). Firstly, a hierarchical priori knowledge with service-object correlation at the bottom and action-object correlation at the top is constructed to assist the understanding on the relationship of objects and actions in service strategies. Service-object correlation guides how to select proper objects with the correct order, while action-object correlation associates actions in strategies according to selected objects. Based on the hierarchical priori knowledge, a progressive learning method is proposed to make the model produce effective strategies with a sequential cognition, from service-object correlation (objects) to action-object correlation (actions). After that, reinforcement learning is employed to enhance the progressive guidance, by designing rewards in terms of the hierarchical priori knowledge. Finally, the proposed method is tested with both comparative experiments and ablation studies, and the experimental results demonstrate the superiority in producing comprehensive and logical strategies, indicating that the progressive learning method in our paper can further improve the QA performance.

An Enhanced Priori Knowledge GAN for CT Images Generation of Early Lung Nodules with Small-Size Labelled Samples.

The small size of labelled samples is one of the challenging problems in identifying early lung nodules from CT images using deep learning methods. Recent literature on the topic shows that deep convolutional generative adversarial network (DCGAN) has been used in medical data synthesis and gained some success, but does not demonstrate satisfactory results in synthesizing CT images. It primarily suffers from the problem of model convergence and is prone to mode collapse. In this paper, we propose a generative adversarial network (GAN) model with prior knowledge to generate CT images of early lung nodules from a small-size of labelled samples, i.e., SLS-PriGAN. Particularly, a knowledge acquisition network and a sharpening network are designed for priori knowledge learning and acquisition, and then, a GAN model is developed to produce CT images of early lung nodules. To validate our method, a general fast R-CNN network is trained using the CT images generated by SLS-PriGAN. The experiment result shows that it achieved a recognizing accuracy of 91%, a recall rate of 81%, and F1 score of 0.85 in identifying clinic CT images of early lung nodules. This provides a promising way of identifying early lung nodules from CT images using deep learning with small-size labelled samples.

Deep Learning-Based Near-Field Source Localization Without a Priori Knowledge of the Number of Sources

In this paper, we propose a high resolution grid-based deep learning source localization that precisely estimates the locations of near-field sources without a priori knowledge of the number of sources. The proposed method consists of a principal component analysis network (PCAnet) and a spatial spectrum network (Sp2net). The proposed PCAnet calculates the noise spaces of the received signals by convolutional layers without a priori knowledge or the estimation of the number of sources and has the lower complexity than eigenvalue decomposition (EVD). The proposed Sp2net calculates the spatial spectrum with a linear layer from the output of the PCAnet and classifies dense location grids with a convolutional neural network (CNN). From the spatial spectrum, this paper also proposes an activation function to enlarge the values at the grid points where the near-field sources exist, which are differentiable for all input values. Then, the direction of arrivals (DOAs) and the ranges of the near-field sources are estimated with high resolution. Computer simulations demonstrated that the proposed method had better DOA and range estimation performances than those of the conventional methods.

Using Priori Knowledge to Develop and Apply an Intelligence Microwave Ablation Planning System for Hepatocellular Carcinoma: A Multicenter Prospective Study

Using Priori Knowledge to Develop and Apply an Intelligence Microwave Ablation Planning System for Hepatocellular Carcinoma: A Multicenter Prospective Study

Clinical Use of A Priori Knowledge of Organ-At-Risk Sparing During Radiation Therapy Treatment for Oropharyngeal Cancer: Dosimetric and Patient Reported Outcome Improvements

PurposeThis study aimed to prospectively assess dosimetric and clinical effects of treatment planners having a priori knowledge of the maximum achievable dose sparing for organs at risk (OARs) for patients with oropharynx cancer receiving intensity modulated radiation therapy (RT). Methods and MaterialsWe examined patients with oropharynx cancer who were treated in prospective clinical trials between February 2012 and April 2019 at our institution. A tool generating estimates of maximum achievable dose sparing for OARs (feasibility dose-volume histogram [FDVH]) was used clinically starting July 2016. Patients were divided into 2 cohorts: Before (ie, baseline) and after (ie, FDVH-guided) FDVH. Doses received by various OARs were compared with those estimated to be achievable per FDVH, and that difference was defined as the excess of feasible dose (EFD). Patient-reported outcome (PRO) questionnaires were completed at 3, 6, and 12 months after treatment. The baseline and FDVH-guided cohorts were compared in terms of EFD, plan quality metrics, and post-RT PRO assessments. ResultsA total of 139 patients were included in the analysis (60 in the baseline cohort, 79 in the FDVH-guided cohort). The FDVH-guided cohort had lower EFD to the contralateral parotid by 4.1 Gy, the ipsilateral parotid by 10.6 Gy, the larynx by 4.3 Gy, the oral cavity by 1.5 Gy, and the contralateral submandibular gland by 0.4 Gy. Plan quality metrics were similar between the cohorts. Less variation of EFD was seen in the FDVH-guided cohort for the parotid glands and contralateral submandibular gland (P < .05). The average post-RT PROs were better in the FVHD cohort versus baseline (particularly at the 6-month timepoint for dry mouth frequency, sticky saliva, meal enjoyment, severity of pain, and Eating Assessment Tool 10 composite [swallowing]; P < .05). ConclusionsUse of FDVH was associated with improved and less variable OAR sparing for clinically delivered plans. FDVH-guided patients had improved PROs compared with baseline with a variety of outcomes significantly improved at 6 months after treatment.

A Priori Knowledge of Maximum Achievable Organ at Risk Sparing: Impact on Organ Doses and Patient Reported Outcomes

IMRT planning is challenging, at least in part, due to the essentially-infinite number of possibilities, and since the planner is never sure that they have gotten "the best", or at least "nearly the best" achievable plan. We prospectively assessed the impact of providing IMRT planners with a priori knowledge of the maximum achievable dose sparing for organs at risk (OARs) for patients on both dosimetric and clinical endpoints. We hypothesized that a priori knowledge of maximum achievable sparing would improve dosimetric and clinical (i.e., patient reported outcome) measures.We examined patients receiving IMRT for oropharynx cancer on prospective clinical trials from 2012-2019 at our institution. A tool that generates estimates of maximum achievable dose sparing for organs at risk was used starting July 2016. Patients treated pre-implementation (i.e., baseline) and post-implementation (i.e., experimental) were compared for the endpoints: (1) Doses received by various OARs MINUS doses achievable (per the tool), termed the 'excess of feasible dose'; (2) Plan quality metrics (e.g., target homogeneity and conformity), and (3) Patient reported outcomes (PRO) per questionnaires completed at 3-, 6- and 12-months post-treatment.139 patients were analyzed (60-baseline cohort, 79-experimental). (1) The excess of feasible dose was lower in the experimental (vs baseline) cohort, for the contralateral parotid (9.6 vs 5.5 Gy; P < 0.001), the ipsilateral parotid (17.6 vs 6.8 Gy; P < 0.001), larynx (12.5 vs 8.2 Gy; P = < 0.001), oral cavity by (15.8 vs 14.3 Gy; P = 0.002), and contralateral submandibular gland (17.4 vs 17 Gy; P = 0.40). Lesser variation of excess of feasible dose (i.e., more consistent sparing) was seen in the experimental (vs. baseline) cohort for the parotid and contralateral submandibular glands (P < 0.05). (2) Some small differences in target homogeneity and conformity were observed but not felt to be clinically significant. (3) The average post-RT PROs were significantly better in the experimental cohort vs baseline at 6 months post-RT. Dry mouth severity, sticky saliva, meal enjoyment, severity of pain, and EAT10 composite (swallowing) were all significantly improved; P < 0.05).Clinical plans aided by an a priori estimate of maximum achievable dose sparing for OARs was associated with improved (and more consistent) OAR sparing and better clinical outcomes (i.e., better post-RT PROs). Clinical implementation of this tool can yield quantifiable dosimetric and patient benefits.

‘세계에 대한 선험적 지식의 가능성’과 외재주의에 대한 재고: 외재주의와 특권적 접근의 양립 가능성에 대한 소이어(1998)의 논의를 중심으로

‘세계에 대한 선험적 지식의 가능성’과 외재주의에 대한 재고: 외재주의와 특권적 접근의 양립 가능성에 대한 소이어(1998)의 논의를 중심으로

Архетипы в контексте априорного знания и эпистемы

Архетипы в контексте априорного знания и эпистемы