Sungil Kim

Ordinal Classification of Imbalanced Data with Application in Emergency and Disaster Information Services

Previous ordinal classification methods implicitly assume that the class distribution within a dataset is balanced, which is often not the case for real-world datasets. If the dataset is imbalanced, the previous methods tend to be biased toward the majority class. The authors propose a new method for ordinal classification that attempts to mitigate the impact of imbalanced datasets. They propose a modified version of the weighted k-nearest neighbors method that determines the class membership using the αth quantile of the estimated class probability distribution and thus mitigates the impact of the class imbalance.

Adaptive combined space-filling and D-optimal designs

In the beginning of sequential experimentation, space-filling designs are more appropriate for exploring process behaviour since they do not require any assumptions about the underlying model. In the latter stages of sequential experimentation, however, when data are collected and more knowledge about the process behaviour is gathered, model-based optimal designs may be more appropriate. This article proposes an adaptive combined design (ACD) balancing the characteristics of both design criteria at different stages of the sequential experiments. The tuning parameter associated with the ACD adaptively gauges the amount of process knowledge gain, which is used to improve the estimation of model parameters while still allowing for the exploration of model uncertainties. Rather than employing the weighted-sum method, an -constraint method is proposed to balance the two design criteria. Property investigation shows that the ACD provides better estimation of parameters over the space-filling design, and yet is more robust against model misspecification when compared to optimal designs. Simulated and real-life nanofabrication examples illustrate the needs of the ACD and the interesting features of the tuning parameter in searching for the process optimum.

Batch Sequential Minimum Energy Design with Design-Region Adaptation

Experiments related to nanofabrication often face challenges of resource-limited experimental budgets, highly demanding tolerance requirements, and complicated response surfaces. Therefore, wisely selecting design points is crucial in order to minimize the expense of resources while at the same time ensuring that enough information is gained to accurately address the experimental goals. In this paper, an efficient batch-sequential design methodology is proposed for optimizing high-cost, low-resource experiments with complicated response surfaces. Through the sequential learning of the unknown response surface, the proposed method sequentially narrows down the design space to more important subregions and selects a batch of design points in the reduced design region. The proposed method balances the space filling of the design region and the search for the optimal operating condition. The performance of the proposed method is demonstrated on a nanowire synthesis system as well as on an optimization test function.

Optimization of carbon dioxide-assisted nanoparticle deposition process with uncertain design space

Abstract A sequential design of experiments methodology with adaptive design space has been applied to optimize a nanoparticle deposition process using elevated pressure, elevated temperature carbon dioxide. This methodology is termed Layers of Experiments (LoE) with Adaptive Combined Design (ACD). Optimizing the CO2-assisted nanoparticle deposition system presents significant challenges: uncertainty in the design region, uncertainty in model structure, a lack of information regarding the process from mechanistic considerations and/or empirical studies, significant costs related to materials processing and characterization, and an engineering tolerance requirement on the characteristics of the products. The contribution of the LoE/ACD methodology presented here is that it systematically finds the optimum of a process while robustly managing the aforementioned challenges.

Early detection of vessel delays using combined historical and real-time information

In ocean transportation, detecting vessel delays in advance or in real time is important for fourth-party logistics (4PL) in order to fulfill the expectations of customers and to help customers reduce delay costs. However, the early detection of vessel delays faces the challenges of numerous uncertainties, including weather conditions, port congestion, booking issues, and route selection. Recently, 4PLs have adopted advanced tracking technologies such as satellite-based automatic identification systems (S-AISs) that produce a vast amount of real-time vessel tracking information, thus providing new opportunities to enhance the early detection of vessel delays. This paper proposes a data-driven method for the early detection of vessel delays: in our new framework of refined case-based reasoning (CBR), real-time S-AIS vessel tracking data are utilized in combination with historical shipping data. The proposed method also provides a process of analyzing the causes of delays by matching the tracking patterns of real-time shipments with those of historical shipping data. Real data examples from a logistics company demonstrate the effectiveness of the proposed method.

Incorporation of engineering knowledge into the modeling process: a local approach

Process modelling is the foundation of developing process controllers for monitoring and improving process/system health. Modelling process behaviours using a pure empirical approach might not be feasible due to limitation in collecting large amount of data. Engineering models provide valuable information about processes’ general behaviours but they might not capture distinct characteristics in the particular process studied. Many recent publications presented various ideas of using limited experimental data to adjust engineering models for making them suitable for certain applications. However, the focuses there are global adjustments, where modification of engineering models impacts the entire model-application region. In practice, some engineering models are only valid in a part of experimental data domain. Moreover, many discrepancies between engineering models and experimental data are in local regions. For example, in a chemical vapour deposition process, at high temperatures a process may be described by a diffusion limited model, while at low temperatures the process may be described by a reaction limited model. To address these problems, this article proposes two approaches for integrating engineering and data models: local model calibration and local model averaging. Through the local model calibration, the discrepancies between engineering’s first-principle models and experimental data are resolved locally based on experts’ feedbacks. To combine models adjusted locally in some regions and also models required little adjustments in other regions, a model averaging procedure based on local kernel weights is proposed. The effectiveness of the proposed method is demonstrated on simulated examples, and compared against a well-known existing global-adjustment method.