Publications

The graph coloring problem (GCP) is a classic combinatorial optimization problem that aims to find the minimum number of colors assigned to vertices of a graph such that no two adjacent vertices receive the same color. GCP has been extensively studied by researchers from various fields, including mathematics, computer science, and biological science. Due to the NP-hard nature, many heuristic algorithms have been proposed to solve GCP. However, existing GCP algorithms focus on either small hard graphs or large-scale sparse graphs (with up to 10^7 vertices). This paper presents an efficient hybrid heuristic algorithm for GCP, named HyColor, which excels in handling large-scale sparse graphs while achieving impressive results on small dense graphs. The efficiency of HyColor comes from the following three aspects: a local decision strategy to improve the lower bound on the chromatic number; a graph-reduction strategy to reduce the working graph; and a k-core and mixed degree-based greedy heuristic for efficiently coloring graphs. HyColor is evaluated against three state-of-the-art GCP algorithms across four benchmarks, comprising three large-scale sparse graph benchmarks and one small dense graph benchmark, totaling 209 instances. The results demonstrate that HyColor consistently outperforms existing heuristic algorithms in both solution accuracy and computational efficiency for the majority of instances. Notably, HyColor achieved the best solutions in 194 instances (over 93%), with 34 of these solutions significantly surpassing those of other algorithms. Furthermore, HyColor successfully determined the chromatic number and achieved optimal coloring in 128 instances.

No abstract is provided for this article.

AdaBoost-KNN using adaptive feature selection with direct optimization is proposed for dynamic emotion recognition in human-robot interaction, where the real-time dynamic emotion is recognized based on facial expression. It can make robots capable of understanding human dynamic emotions, in such a way that human-robot interaction is realized in a smooth manner. Based on the facial key points extracted by Candide-3 model, adaptive feature selection is adopted, namely Plus-L Minus-R Selection is completed. It can determine the features that contribute the most to emotion recognition, thereby forming the basis of emotion classification. Emotion classification is based on AdaBoost-KNN, which builds a series of KNN classifiers. AdaBoost-KNN adjusts the weights of the data in an iterative manner. Moreover, global optimal parameters are approximated with direct optimization until the recognition rate reaches its maximal value. The experimental performance of the proposal is verified by a k-fold cross-validation. Results show that the recognition rate of the proposed approach is higher than that of the AdaBoost-KNN, adaptive feature selection-based AdaBoost-KNN, and AdaBoost-KNN with direct optimization. It is also higher than the rate achieved by other traditional recognition methods, such as AdaBoost, KNN, and SVM. In addition, preliminary application experiments are developed in our emotional social robot system, composed of two mobile robots. The experiments demonstrate the dynamic emotion understanding ability of robots in human-robot interaction.

The prediction of unloading times is crucial for reducing demurrage costs and ensuring the smooth scheduling of downstream processes in a steel plant. The duration of unloading a cargo ship is primarily determined by the unloading schedule established at the raw materials terminal and the storage operation schedule implemented in the stockyard. This study aims to provide an accurate forecast of unloading times for incoming ships at the raw materials terminal of a steel plant. We propose three neural network-based methods: the Backpropagation Neural Network (BP), the Random Vector Functional Link (RVFL), and the Stochastic Configurations Network (SCN) for this prediction. This issue has not been previously researched using similar methods, particularly in the context of large-scale steel plants. The performance of these three methods is evaluated based on several indices: the Root Mean Square Error (RMSE), the quality of the best solution, convergence, and stability, which are employed for predicting unloading times. The prediction accuracies achieved by the BP, RVFL, and SCN were 76%, 85%, and 87%, respectively. These results demonstrate the effectiveness and potential applications of the proposed methods.

Perception techniques for autonomous driving should be adaptive to various environments. In the case of traffic line detection, an essential perception module, many condition should be considered, such as number of traffic lines and computing power of the target system. To address these problems, in this paper, we propose a traffic line detection method called Point Instance Network (PINet); the method is based on the key points estimation and instance segmentation approach. The PINet includes several stacked hourglass networks that are trained simultaneously. Therefore the size of the trained models can be chosen according to the computing power of the target environment. We cast a clustering problem of the predicted key points as an instance segmentation problem; the PINet can be trained regardless of the number of the traffic lines. The PINet achieves competitive accuracy and false positive on the TuSimple and Culane datasets, popular public datasets for lane detection. Our code is available at https://github.com/koyeongmin/PINet_new