Byoungho Kang

Real Time Scheduling by Coordination for Optimizing Operations of Equipments in a Container Terminal

Question answering (QA) aims at retrieving precise information from a large collection of documents. Different techniques can be used to find relevant information, and to compare these techniques, it is important to evaluate QA systems. The objective of an Answer Validation task is thus to judge the correctness of an answer returned by a QA system for a question, according to the text snippet given to support it. We participated in such a task in 2006. In this article, we present our strategy for deciding if the snippets justify the answers: a strategy based on our own QA system, comparing the answers it returned with the answer to judge. We discuss our results, then we point out the difficulties of this task.To maximize the productivity of a container terminal, it is important to have the operations of the equipments of different types optimized and synchronized. However, any attempt to search for a globally optimum operation schedule is prohibitive because of the real time constraint. The real time scheduling method proposed in this paper first generates schedules of individual equipments by using simple heuristics. Subsequently, the method analyzes the schedules to locate the job that causes the longest delay and tries to reduce the delay by adjusting the individual schedules of the relevant equipments. This adjustment process repeats until the quality of the schedules is satisfactory. Simulation experiments have shown that our method produces more efficient operation schedules in real time than other methods based on simulated annealing or heuristic-only strategies.

Bayesian Sampling of Virtual Examples to Improve Classification Accuracy

A virtual example is an artificial example that does not exist in the given training set. We sample a virtual example from a Bayesian network constructed with the original training set. The usefulness of a sampled virtual example for learning is measured by the increment of the networks conditional likelihood. A qualified virtual example is saved and used to update the network for the next sampling. By repeating this process we can generate candidate virtual example sets of various sizes. Among these candidates, an appropriately sized virtual example set for a target learning algorithm is chosen through statistical significance tests. Experiments have shown that the virtual examples collected this way can help various learning algorithms to derive classifiers of improved accuracy

Neighborhood selection by probabilistic filtering for load balancing in production scheduling

One of the difficulties of using Artificial Neural Networks (ANNs) to estimate atmospheric temperature is the large number of potential input variables available. In this study, four different feature extraction methods were used to reduce the input vector to train four networks to estimate temperature at different atmospheric levels. The four techniques used were: genetic algorithms (GA), coefficient of determination (CoD), mutual information (MI) and simple neural analysis (SNA). The results demonstrate that of the four methods used for this data set, mutual information and simple neural analysis can generate networks that have a smaller input parameter set, while still maintaining a high degree of accuracy.

Enhanced probabilistic filtering for improving the efficiency of local searches

The probabilistic filtering method filters out an unpromising candidate solution by conducting a simple preliminary evaluation before a complete evaluation in order to improve the efficiency of a local search. In this paper, we improve probabilistic filtering so that it can be applied in general to large-scaled optimization problems. As compared to the previous probabilistic filtering method, our enhanced version includes a scaling and truncation function to increase the discriminating power of probabilistic filtering and repair some defects of the previous bias function in adjusting the level of greediness. Experiments have shown that our method is more effective in improving the performance of a local search than the previous method. It has also been shown that the probabilistic filtering can be effective even when the preliminary evaluation heuristic is somewhat inaccurate, and the lesser the cost of preliminary evaluation, the greater is its effectiveness.

Sampling of virtual examples to improve classification accuracy for nominal attribute data

This paper presents a method of using virtual examples to improve the classification accuracy for data with nominal attributes. Most of the previous researches on virtual examples focused on data with numeric attributes, and they used domain-specific knowledge to generate useful virtual examples for a particularly targeted learning algorithm. Instead of using domain-specific knowledge, our method samples virtual examples from a naive Bayesian network constructed from the given training set. A sampled example is considered useful if it contributes to the increment of the networks conditional likelihood when added to the training set. A set of useful virtual examples can be collected by repeating this process of sampling followed by evaluation. Experiments have shown that the virtual examples collected this way can help various learning algorithms to derive classifiers of improved accuracy.