An improved Criminisi algorithm based on a new priority function and updating confidence

2014 ◽  
Author(s):  
Ajian Nan ◽  
Xiaoqiang Xi
Keyword(s):  
Priority Function

Efficient Drill Sequence Optimization Using a Heuristic Priority Function

2021 ◽  
Author(s):  
Zhenzhen Wang ◽  
Jincong He ◽  
Shusei Tanaka ◽  
Xian-Huan Wen
Keyword(s):  
Optimization Problems ◽  
Computational Cost ◽  
Oil And Gas Industry ◽  
Automatic Process ◽  
Displacement Efficiency ◽  
General Applicability ◽  
Sequence Optimization ◽  
Starting Point ◽  
Priority Function ◽  
Well Index

Abstract Drill sequence optimization is a common challenge faced in the oil and gas industry and yet it cannot be solved efficiently by existing optimization methods due to its unique features and constraints. For many fields, the drill queue is currently designed manually based on engineering heuristics. In this paper, a heuristic priority function is combined with traditional optimizers to boost the optimization efficiency at a lower computational cost to speed up the decision-making process. The heuristic priority function is constructed to map the individual well properties such as well index and inter-well distance to the well priority values. As the name indicates, wells with higher priority values will be drilled earlier in the queue. The heuristic priority function is a comprehensive metric of inter-well communication & displacement efficiency. For example, injectors with fast support to producers or producers with a better chance to drain the unswept region tend to have high scores. It contains components that weigh the different properties of a well. These components are then optimized during the optimization process to generate the beneficial drill sequences. Embedded with reservoir engineering heuristics, the priority function helps the optimizer focus on exploring scenarios with promising outcomes. The proposed heuristic priority function, combined with the Genetic Algorithm (GA), has been tested through drill sequence optimization problems for the Brugge field and Olympus field. Optimizations that are directly performed on the drill sequence are employed as reference cases. Different continu- ous/categorical parameterization schemes and various forms of heuristic priority functions are also investigated. Our exploration reveals that the heuristic priority function including well type, constraints, well index, distance to existing wells, and adjacent oil in place yields the best outcome. The proposed approach was able to achieve a better optimization starting point (∼5-18% improvement due to more reasonable drill sequence rather than random guess), a faster convergence rate (results stabilized at 12 vs. 30 iterations), and a lower computational cost (150-250 vs. 1,300 runs to achieve the same NPV) over the reference methods. Similar performance improvement was also observed in another application to a North Sea type reservoir. This demonstrated the general applicability of the proposed method. The employment of the heuristic priority function improves the efficiency and reliability of drill sequence optimization compared to the traditional methods that directly optimize the sequence. It can be easily embedded in either commercial or research simulators as an independent module. In addition, it is also an automatic process that fits well with iterative optimization algorithms.

MAX-MIN FAIR FLOW CONTROL SENSITIVE TO PRIORITIES

2005 ◽  
Vol 06 (02) ◽  
pp. 85-114 ◽  
Author(s):  
PANAGIOTA FATOUROU ◽  
MARIOS MAVRONICOLAS ◽  
PAUL SPIRAKIS
Keyword(s):  
Flow Control ◽  
Communication Networks ◽  
Transmission Rate ◽  
Distributed Applications ◽  
Network Resources ◽  
Combinatorial Properties ◽  
Individual Session ◽  
A Priority ◽  
Priority Function ◽  
Fairness Condition

Flow control is the dominant technique currently used in communication networks for preventing excess traffic from flooding the network, and for handling congestion. In rate-based flow control, transmission rates of sessions are adjusted in an end-to-end manner through a sequence of operations. In this work, we present a theory of max-min fair, rate-based flow control sensitive to priorities of different sessions, as a significant extension of the classical theory of max-min fair, rate-based flow control to networks supporting applications with diverse requirements on network resources. Each individual session bears a priority function, which maps the session's priority to a transmission rate; the priority is a working abstraction of the session's priority to bandwidth access. Priority functions enable the specification of requirements on bandwidth access by distributed applications, and the formal handling of such requirements. We present priority max-min fairness, as a novel and well motivated fairness condition which requires that assigned rates correspond, through the priority functions, to priorities comprising a max-min vector. We also introduce priority bottleneck algorithms gradually update a session's rate until when its priority is restricted on a priority bottleneck edge of the network. We establish a collection of interesting combinatorial properties of priority bottleneck algorithms. Most significantly, we show that they can only converge to priority max-min fairness. As an application of our general theory, we embed priority bottleneck algorithms in the more realistic optimistic framework for rate-based flow control. The optimistic framework allows for both decreases and increases of session rates. We exploit these additionally provided semantics to prove further combinatorial properties for the termination of priority bottleneck algorithms in the optimistic framework. We use these properties to conclude the first optimistic algorithms for efficient, max-min fair, rate-based flow control sensitive to priorities.

scholarly journals Formation of spiritual culture and environmentally responsible human behavior

2021 ◽  
Vol 258 ◽  
pp. 07014
Author(s):  
Asanbek Akmataliev ◽  
Mirlanbek Manashov ◽  
Turdukan Abdykaarova ◽  
Rustambek Salimov ◽  
Samara Karabaeva ◽  
...  
Keyword(s):  
Historical Development ◽  
Social Reality ◽  
Research Topic ◽  
Moral Life ◽  
Environmentally Responsible ◽  
Public Consciousness ◽  
New Type ◽  
Priority Function ◽  
Market Relations

The spiritual sphere of the society is a system of relations between people, reflecting the spiritual and moral life of a society, represented by such subsystems as culture, science, religion, morality, ideology, art. The significance of the spiritual sphere is determined by its most important, priority function of determining the value-normative system of a society, which, in its turn, reflects the level of development of public consciousness and the intellectual and moral potential of a society as a whole. The relevance of the research topic is determined by the sociocultural changes taking place in a society associated with the formation of a new type of sociality in Kyrgyzstan. In a society that has moved to a different stage of socio-historical development, the role of the spiritual sphere of society changes significantly. In the conditions of the existence of worldview pluralism and the complication of social reality, the need to reflect on society as a “field” for the implementation of various intentions of spiritual life is actualized. The formation of a new social reality of a society in the process of transition to market relations is associated with negative processes of spiritual impoverishment of citizens, gradually forgetting about their historical roots.

scholarly journals Quality Improvement for Exemplar-based Image Inpainting using a Modified Searching Mechanism

2020 ◽  
Vol 4 (1) ◽  
pp. 1
Author(s):  
Mariwan Wahid Ahmed ◽  
Alan Anwer Abdulla
Keyword(s):  
Image Processing ◽  
Image Compression ◽  
Euclidean Distance ◽  
Image Inpainting ◽  
Input Image ◽  
Second Step ◽  
Research Areas ◽  
Potential Applications ◽  
Priority Function ◽  
Popular Images

Digital image processing has a significant impact in different research areas including medical image processing, biometrics, image inpainting, object detection, information hiding, and image compression. Image inpainting is a science of reconstructing damaged parts of digital images and filling-in regions in which information are missing which has many potential applications such as repairing scratched images, removing unwanted objects, filling missing area, and repairing old images. In this paper, an image inpainting algorithm is developed based on exemplar, which is one of the most important and popular images inpainting technique, to fill-in missing area that caused either by removing unwanted objects, by image compression, by scratching image, or by image transformation through internet. In general, image inpainting consists of two main steps: The first one is the priority function. In this step, the algorithm decides to select which patch has the highest priority to be filled at the first. The second step is the searching mechanism to find the most similar patch to the selected highest priority patch to be inpainted. This paper concerns the second step and an improved searching mechanism is proposed to select the most similar patch. The proposed approach entails three steps: (1) Euclidean distance is used to find the similarity between the highest priority patches which need to be inpainted with each patch of the input image, (2) the position/location distance between those two patches is calculated, and (3) the resulted value from the first step is summed with the resulted value obtained from the second step. These steps are repeated until the last patch from the input image is checked. Finally, the smallest distance value obtained in step 3 is selected as the most similar patch. Experimental results demonstrated that the proposed approach gained a higher quality in terms of both objectives and subjective compared to other existing algorithms.

scholarly journals Feature selection in evolving job shop dispatching rules with genetic programming

2021 ◽  
Author(s):  
Yi Mei ◽  
Mengjie Zhang ◽  
Su Nyugen
Keyword(s):  
Genetic Programming ◽  
Domain Knowledge ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Search Space ◽  
Dispatching Rules ◽  
Terminal Set ◽  
Selection Approach ◽  
Personal Use ◽  
Priority Function

Genetic Programming (GP) has been successfully used to automatically design dispatching rules in job shop scheduling. The goal of GP is to evolve a priority function that will be used to order the waiting jobs at each decision point, and decide the next job to be processed. To this end, the proper terminals (i.e. job shop features) have to be decided. When evolving the priority function, various job shop features can be included in the terminal set. However, not all the features are helpful, and some features are irrelevant to the rule. Including irrelevant features into the terminal set enlarges the search space, and makes it harder to achieve promising areas. Thus, it is important to identify the important features and remove the irrelevant ones to improve the GP-evolved rules. This paper proposes a domain-knowledge-free feature ranking and selection approach. As a result, the terminal set is significantly reduced and only the most important features are selected. The experimental results show that using only the selected features can lead to significantly better GP-evolved rules on both training and unseen test instances. © Mei 2016. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in 'GECCO '16: Proceedings of the Genetic and Evolutionary Computation Conference', https://doi.org/10.1145/2908812.2908822.

scholarly journals Supervisors’ Visual Attention Allocation Modeling Using Hybrid Entropy

Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 393 ◽  
Author(s):  
Haifeng Bao ◽  
Weining Fang ◽  
Beiyuan Guo ◽  
Peng Wang
Keyword(s):  
Visual Attention ◽  
Human Machine Interface ◽  
Attention Allocation ◽  
Allocation Mechanism ◽  
Allocation Model ◽  
Proposed Model ◽  
Machine Interface ◽  
Priority Function ◽  
Definition Of ◽  
Automation Technology

With the improvement in automation technology, humans have now become supervisors of the complicated control systems that monitor the informative human–machine interface. Analyzing the visual attention allocation behaviors of supervisors is essential for the design and evaluation of the interface. Supervisors tend to pay attention to visual sections with information with more fuzziness, which makes themselves have a higher mental entropy. Supervisors tend to focus on the important information in the interface. In this paper, the fuzziness tendency is described by the probability of correct evaluation of the visual sections using hybrid entropy. The importance tendency is defined by the proposed value priority function. The function is based on the definition of the amount of information using the membership degrees of the importance. By combining these two cognitive tendencies, the informative top-down visual attention allocation mechanism was revealed, and the supervisors’ visual attention allocation model was built. The Building Automatic System (BAS) was used to monitor the environmental equipment in a subway, which is a typical informative human–machine interface. An experiment using the BAS simulator was conducted to verify the model. The results showed that the supervisor’s attention behavior was in good agreement with the proposed model. The effectiveness and comparison with the current models were also discussed. The proposed attention allocation model is effective and reasonable, which is promising for use in behavior analysis, cognitive optimization, and industrial design.

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