Robust optimization utilizing the second-order design sensitivity information

This paper presents an effective methodology for robust optimization of electromagnetic devices. To achieve the goal, the method improves the robustness of the minimum of the objective function chosen as a design solution by minimizing the second-order sensitivity information, called a gradient index (GI) and defined by a function of gradients of performance functions with respect to uncertain variables. The constraint feasibility is also enhanced by adding a GI corresponding to the constraint value. The distinctive feature of the method is that it requires neither statistical information on design variables nor calculation of the performance reliability during the robust optimization process. The validity of the proposed method is tested with the TEAM Workshop Problem 2

Standards Education Policy Development: Observations based on APEC Research

This paper stems from a research project carried out for the Asia Pacific Economic Cooperation (APEC) to make an inventory of national standards education policies. Twenty countries - sixteen Asia-Pacific economies and four European nations – have been investigated. The paper relates similarities and differences between these policies to the standardization education activities in place. The paper concludes with policy recommendations

Improving Generalization of Drowsiness State Classification by Domain-Specific Normalization

Abnormal driver states, particularly have been major concerns for road safety, emphasizing the importance of accurate drowsiness detection to prevent accidents. Electroencephalogram (EEG) signals are recognized for their effectiveness in monitoring a driver's mental state by monitoring brain activities. However, the challenge lies in the requirement for prior calibration due to the variation of EEG signals among and within individuals. The necessity of calibration has made the brain-computer interface (BCI) less accessible. We propose a practical generalized framework for classifying driver drowsiness states to improve accessibility and convenience. We separate the normalization process for each driver, treating them as individual domains. The goal of developing a general model is similar to that of domain generalization. The framework considers the statistics of each domain separately since they vary among domains. We experimented with various normalization methods to enhance the ability to generalize across subjects, i.e. the model's generalization performance of unseen domains. The experiments showed that applying individual domain-specific normalization yielded an outstanding improvement in generalizability. Furthermore, our framework demonstrates the potential and accessibility by removing the need for calibration in BCI applications.Comment: Submitted to 2024 12th IEEE International Winter Conference on Brain-Computer Interfac

DeeLeMa: Missing information search with Deep Learning for Mass estimation

We present DeeLeMa, a deep learning network to analyze energies and momenta in particle collisions at high energy colliders, especially DeeLeMa is constructed based on symmetric event topology, and the generated mass distributions show robust peaks at the physical masses after the combinatoric uncertainties, and detector smearing effects are taken into account. DeeLeMa can be widely used in different event topologies by adopting the corresponding kinematic symmetries

Standardization of the Manufacturing Process of Bee Venom Pharmacopuncture Containing Melittin as the Active Ingredient

Background. Pharmacopuncture is a unique treatment in oriental medicine that combines chemical stimulation with conventional acupuncture. However, there are no standardized methods for preparing the herbal medicines used in pharmacopuncture, and it is not clear whether the active ingredients are safe and stable. Several studies have investigated nonstandardized preparation processes, but few investigations have addressed safety and preparation methods. Pharmacopuncture may provide an alternative treatment for incurable diseases. However, it must be as valid and safe as standardized medicine. In this way, the present project may contribute to the industrialization of medicine in Korea. It may also expand health insurance coverage by promoting evidence-based medical insurance benefits. Thus, the present study attempted to standardize and improve the raw materials, preparation, and efficacy of bee venom pharmacopuncture (BVP), which is a highly effective technique in oriental medicine. Method. To purify the crude bee venom, the extract was subjected to a stepped-gradient open column (ODS-A; 120 Å, 150 mesh). Using this method, the yield of melittin was significantly increased and the allergen proteins were effectively removed. The melittin content of the purified bee venom was determined using HPLC, and the product was then diluted to 0.1 mg/mL using injection water in preparation for BVP. Results. In the present study, we standardized the purification process to provide safe and stable BVP by increasing the main effective components and eliminating allergens. This study will be seminal in the industrialization and regulation of BVP. Conclusion. We developed an effective strategy for melittin purification and allergen removal from bee venom to create safe BVP