Handwriting difficulties (HWDs) in children have adverse effects on their confidence and academic progress. Detecting HWDs is the first crucial step toward clinical or teaching intervention for children with HWDs. To date, how to automatically detect HWDs is still a challenge, although digitizing tablets have provided an opportunity to automatically collect handwriting process information. Especially, to our best knowledge, there is no exploration into the potential of combining machine learning algorithms and the handwriting process information to automatically detect Chinese HWDs in children. To bridge the gap, we first conducted an experiment to collect sample data and then compared the performance of five commonly used classification algorithms (Decision tree, Support Vector Machine (SVM), Artificial Neural Network, Naïve Bayesian and k-Nearest Neighbor) in detecting HWDs. The results showed that: (1) only a small proportion (13%) of children had Chinese HWDs and each classification model on the imbalanced dataset (39 children at risk of HWDs versus 261 typical children) produced the results that were better than random guesses, indicating the possibility of using classification algorithms to detect Chinese HWDs; (2) the SVM model had the best performance in detecting Chinese HWDs among the five classification models; and (3) the performance of the SVM model, especially its sensitivity, could be significantly improved by employing the Synthetic Minority Oversampling Technique to handle the class-imbalanced data. This study gains new insights into which handwriting features are predictive of Chinese HWDs in children and proposes a method that can help the clinical and educational professionals to automatically detect children at risk of Chinese HWDs.

This paper addresses the scheduling problem of a class of automated manufacturing systems with multiple resource requests. In the automated manufacturing system model, a set of jobs is to be processed and each job requires a sequence of operations. Each operation may need more than one resource type and multiple identical units with the same resource type. Upon the completion of an operation, resources needed in the next operation of the same job cannot be released and the remaining resources cannot be released until the start of the next operation. The scheduling problem is formulated by Timed Petri nets model under which the scheduling goal consists in sequencing the transition firing sequence in order to avoid the deadlock situation and to minimize the makespan. In the proposed genetic algorithm with deadlock-free constraint, Petri net transition sequence is coded and a deadlock detection method based on D-siphon technology is proposed to reschedule the sequence of transitions. The enabled transitions should be fired as early as possible and thus the quality of solutions can be improved. In the fitness computation procedure, a penalty item for the infeasible solution is involved to prevent the search process from converging to the infeasible solution. The method proposed in this paper can get a feasible scheduling strategy as well as enable the system to achieve good performance. Numerical results presented in the paper show the efficiency of the proposed algorithm.

Researchers have already attributed a certain amount of variability and “drift” in an individual's handwriting pattern to mental workload, but this phenomenon has not been explored adequately. Especially, there still lacks an automated method for accurately predicting mental workload using handwriting features. To solve the problem, we first conducted an experiment to collect handwriting data under different mental workload conditions. Then, a predictive model (called SVM-GA) on two-level handwriting features (i.e., sentence- and stroke-level) was created by combining support vector machines and genetic algorithms. The results show that (1) the SVM-GA model can differentiate three mental workload conditions with accuracy of 87.36% and 82.34% for the child and adult data sets, respectively and (2) children demonstrate different changes in handwriting features from adults when experiencing mental workload.

In this paper, we propose a hybrid fine-tuned multi-objective memetic algorithm hybridizing different solution fitness evaluation methods for global exploitation and exploration. To search across all regions in objective space, the algorithm uses a widely diversified set of weights at each generation, and employs a simulated annealing to optimize each utility function. For broader exploration, a grid-based technique is adopted to discover the missing nondominated regions on existing tradeoff surface, and a Pareto-based local perturbation is performed to reproduce incrementing solutions trying to fill up the discontinuous areas. Additional advanced feature is that the procedure is made dynamic and adaptive to the online optimization conditions based on a function of improvement ratio to obtain better stability and convergence of the algorithm. Effectiveness of our approach is shown by applying it to multi-objective 0/1 knapsack problem (MOKP).