Decision makers can choose the combination of uncertainty sets and uncertain levels according to their risk preferences to minimize the total cost. The example results show that the Interval-Polyhedron set's robust models have the smallest total costs and strongest robustness. The robust optimization models are then compared to the minimum cost models for consensus. ![]() Sensitivity analysis is carried out under different parameter conditions to determine the robustness of the solutions obtained from robust optimization models. For three types of minimum cost consensus models with direction restrictions, including MCCM-DC,-MCCM-DC and threshold-based (TB)-MCCM-DC, the robust cost consensus models corresponding to four types of uncertainty sets (Box set, Ellipsoid set, Polyhedron set and Interval-Polyhedron set) are established. ![]() At the same time, four uncertain level parameters are introduced. Based on the asymmetric cost consensus model, this paper considers the uncertainties of the experts' unit adjustment costs under the background of group decision making. The robust optimization method has progressively become a research hot spot as a valuable means for dealing with parameter uncertainty in optimization problems. Ultimately, the experimental results show the delivery ratio about messages is improved significantly and verify the effectiveness of EEIS. Meanwhile, both parties’ resource status, wealth status and the price of messages must be open and transparent to prevent the nodes from making false pricing during the transaction. ![]() Otherwise, the next message will continue to be traded. The buyer and seller respectively make a price about the message according to its own resource state and negotiate twice the pricing both side until they agree, then the buyer will send the message and pay a certain virtual currency to the seller. It is main that messages forwarding will be abstracted into a transaction in EEIS. Thus, an equivalent-exchange-based data forwarding incentive scheme (EEIS) will be proposed in this paper. Lastly, we outline the current achievements and limitations of the existing methods, along with the current research challenges, to assist the research community on defect detection in setting a further agenda for future studies.Īs nodes have limited resources in the socially aware networks, they will have strong selfish behaviors, such as not forwarding messages and losing packets, which will lead to poor network performance. The core ideas and codes of studies related to high precision, high positioning, rapid detection, small object, complex background, occluded object detection and object association, are summarized. To further understand the difficulties in the field of defect detection, we investigate the functions and characteristics of existing equipment used for defect detection. Third, we summarize and analyze the application of ultrasonic testing, filtering, deep learning, machine vision, and other technologies used for defect detection, by focusing on three aspects, namely method and experimental results. Second, recent mainstream techniques and deep-learning methods for defects are reviewed with their characteristics, strengths, and shortcomings described. First, we classify the defects of products, such as electronic components, pipes, welded parts, and textile materials, into categories. This study surveys stateoftheart deep-learning methods in defect detection. The detection of product defects is essential in quality control in manufacturing. ![]() In the final stage of the current paper, some ideas are recommended for future works in the relevant fields. Moreover, the items influencing the performance of these methods are investigated. In this work, different applications of intelligent methods in performance modeling heat exchangers are reviewed, and the key outcomes of the reviewed works are represented. Owing to the aforementioned facts, it would be crucial to consider the influential factors in the proposed mode to produce models with the greatest accuracy. The accuracy and applicability of machine learning methods, mainly based on intelligent techniques, in modeling and forecasting the performance of heat exchangers are dependent on some factors including architecture of algorithm, inputs of the model, and complexity of the system. In addition to experimental and time-consuming computational approaches, intelligent methods can be used for the investigation of heat exchanger performance due to their abilities in accurate prediction and relatively fast performance. Heat exchangers are applicable in different industries and technologies, and their performance is influenced by different parameters.
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