We present an evolutionary adaptive eye-tracking framework aiming for low-cost human computer interaction. The main focus is to guarantee eye-tracking performance without using high-cost devices and strongly controlled situations. The performance optimization of eye tracking is formulated into the dynamic control problem of deciding on an eye tracking algorithm structure and associated thresholds/parameters, where the dynamic control space is denoted by genotype and phenotype spaces. The evolutionary algorithm is responsible for exploring the genotype control space, and the reinforcement learning algorithm organizes the evolved genotype into a reactive phenotype. The evolutionary algorithm encodes an eye-tracking scheme as a genetic code based on image variation analysis. Then, the reinforcement learning algorithm defines internal states in a phenotype control space limited by the perceived genetic code and carries out interactive adaptations. The proposed method can achieve optimal performance by compromising the difficulty in the real-time performance of the evolutionary algorithm and the drawback of the huge search space of the reinforcement learning algorithm. Extensive experiments were carried out using webcam image sequences and yielded very encouraging results. The framework can be readily applied to other low-cost vision-based human computer interactions in solving their intrinsic brittleness in unstable operational environments.
Green capacity investment projects have rapidly emerged involving suppliers, customers, and manufacturing organizations in supply chain systems with environmental challenges. This paper focuses on and identifies both primary strategic and operational elements that will aid managers in evaluating and making risky multi-criteria decisions on green capacity investment projects. We propose a cloud prospect value consensus process consisting of feedback and adjustment mechanisms that provide modification instructions to the corresponding decision makers for a decision matrix based on the cloud model and prospect theory, which considers psychological behavior, disagreements between decision makers, and the ambiguity of linguistic variable assessment across multi-criteria risks. The new model increases the efficiency and accuracy of decision making. To verify the feasibility and validity of the Cloud Prospect Value Consensus Degree based on the Feedback adjustment mechanism, its performance is compared with three state-of-the-art multi-criteria group decision-making methods.
The assessment of risky linguistic variables has significant applications in multiattribute group decision problems. This paper focuses on risky multicriteria group decision making using linguistic variable assessment and proposes a new model which considers various and differential psychological behavior and the ambiguity of linguistic variable assessment across multicriteria risks. Based on the cloud prospect value assessment, this paper proposes a cloud prospect value aggregation method and consensus degree measurement. An improved feedback adjustment mechanism based on regret theory is employed as the consistency model, which complements prospect theory. The three theoretical methods together constitute the core elements of the proposed CPD (cloud prospect value consensus degree decision) model. The feasibility and validity of the new decision making model are demonstrated with a numerical example, and feedback performance was compared with conventional direct feedback. The proposed CPD approach satisfies given consistency threshold of 0.95 and 0.98 after three and four feedback loops, respectively. Compared to the proposed CPD method, direct feedback approach needs seven and ten feedback loops under the same threshold, respectively, which shows that the proposed model increases efficiency and accuracy of group decision making and significantly reduces time cost.
The artificial potential field method has been extensively used in mobile robot path planning for its characteristics of simpleness, high efficiency, and smooth path. In this paper, to solve the problem of local minima in traditional artificial potential field method, A modified form of repulsion function is proposed. A detour force is added to the repulsion function, the problem of local minima can be solved effectively. In the end, with the help of Matlab software simulating, the result shows that this method is simple and effective.
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