Stability tests for identifying structural changes in the key performance measures

http://deepblue.lib.umich.edu/bitstream/2027.42/96909/1/MBA_Lee_F_1998_final.pd

The effects of financial, human, and social capital on the perception of financial well-being

A considerable amount of research has focused on several effects of social capital such as economic benefits, forming human capital, and crime rate. However, less attention has been paid to the mediating effect of social capital on perception of financial well-being. Community social capital\u27s effect on individual perception of financial well-being has also received little attention. This paper seeks the answer to these questions: (1) What are social capital\u27s mediating effects on the perception of financial well-being and household financial capital? (2) What are community social capital, financial capital, and human capital\u27s effects on individual perception of financial well-being? Data for this study came from the Northwest Area Foundation Horizons Cluster Social Capital Survey conducted from 2004 to 2005. The survey covered 36 communities participating in the Northwest Area Foundation Horizons Program. The results of the study approved the mediating effects of social capital on the perception of financial well-being and on the forming of household financial capital. Among three community capitals, only community social capital shows a significant effect on perception of financial well-being. Community social capital also shows a greater effect on perception of financial well-being than that of individual social capital. It illustrates social capital\u27s characteristic as a public good rather than a private property. Distribution of three capitals indicated that social capital is more equally distributed than those of household financial capital and human capital

Maximizing Charge Density of Triboelectric Nanogenerator based on Design of Dielectric Layer

Department of Materials Science and EngineeringMost recently, a newly designed energy generating device named the triboelectric nanogenerator (TENG) was reported and various types of TENGs have been demonstrated, proven as a highly efficient, simple, robust and cost-effective technique for efficiently converting various mechanical energies around us to electricity. In principle, the electrical energy is generated as two different materials are brought into contact with each other, in conjunction with the triboelectric electrification and electrostatic induction. The sources of mechanical energy such as winds and moving things are available anywhere and anytime in our surroundings, thus, TENG will be appropriate as a power-supply unit for portable devices although the energy is not big as expected. Since the first demonstration of the TENG on 2012, various TENG structures and new functional materials brought about the significant increase of the instantaneous power density up to several tens of mW/cm2. However, further enhancement is required for faster commercialization, which may be possible by developing functional triboelectric materials, such as dielectrics. First, we set out to design and synthesize Polyvinylidene fluoride (PVDF) graft copolymers to incorporate poly(tert-butyl acrylate) (PtBA) through an atom-transfer radical polymerization (ATRP) technique as an efficient dielectric to enhance the output performance of the TENGs. This increase in the dielectric constant significantly increased the density of the charges that can be accumulated on the copolymer during physical contact. The markedly enhanced output performance is quite stable and reliable in harsh mechanical environments due to the high flexibility of the films. Second, we demonstrate high-output TENGs based on polyimide (PI)-based polymers by introducing functionalities (e.g., electron-withdrawing and electron-donating groups) into the backbone. The TENG based on 6FDA-APS PI, possessing the most negative electrostatic potential and the low-lying lowest unoccupied molecular orbital level, produces the highest effective charge density in practical working conditions without the ion injection process. This may be ascribed to the excellent charge-retention characteristics as well as the enhanced charge transfer capability. This article provides a comprehensive review of effective dielectrics used so far in TENG, as well as the fundamental issues regarding the materials. Finally, we show some strategies for obtaining the properties that the materials should have as effective dielectrics.clos

SMS: 불균형 이진 분류를 위한 인공 데이터 샘플링 기법

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 컴퓨터공학부, 2021. 2. 강유.Given an imbalanced dataset, how can we create high fidelity synthetic minority instances for training robust and unbiased classifiers? Data imbalance is common in mission-critical fields where costs associated with procuring minority instances are prohibitively expensive. Training classifiers on imbalanced datasets result in unreliable predictions and low performance. Oversampling techniques are employed to restore balance to the dataset, allowing the classifier to learn a more accurate representation of the true data distribution. Thus, generating a set of synthetic samples that are i) realistic, ii) containing varying degrees of class confidence, and iii) diverse is essential. Existing methods create samples that do not satisfy all the desired properties. We propose Synthetic Minority Sampler (SMS), an oversampling framework designed for highly imbalanced datasets. SMS employs two generators to create a balanced ratio of normal and borderline samples that teach classifiers a robust and unbiased class representation. SMS accounts for the scarce minority instances via a class-conditional diversity loss to ensure that generated minority samples are diverse. Additionally, SMS stabilizes the training process by introducing a weighted random sampler to balance the class proportion of mini-batches, and data augmentation to prevent the discriminator from overfitting. Experimental results show that models trained on an imbalanced dataset augmented with synthetic data sampled from SMS outperform competitors in the binary classification task, achieving up to 10.06% higher F1-score than the competitors.클래스가 불균형한 데이터가 주어졌을 때 어떻게 소수 클래스에 대한 데이터를 인공적으로 증대하여 클래스 분류 성능을 높일 수 있을까? 데이터 불균형 문제는 고장 진단 및 질병 분류와 같이 한쪽의 클래스 수가 다른 한쪽의 수보다 극단적으로 적을 때 발생하는 문제를 일컫는다. 이러한 불균형한 데이터를 통해 학습된 모델은 잘못된 예측 결과 좋지 못한 분류 성능을 보인다. 이를 해결하기 위해 일반적으로 소수 클래스에 대해 인공적으로 샘플을 증대하여 각 클래스의 샘플의 수를 동일하게 하는 방식을 사용한다. 인공적으로 증대가 된 샘플은 사실적이고 기존의 샘플과 동일하지 않아야 하며 다양한 성질을 포함하여야 하는데 선행 연구들은 이러한 요소를 충족하지 못하고 있다. 해당 논문에서는 불균형한 데이터셋에서 높은 품질의 인공 데이터를 오버샘플링 (oversampling) 하는 프레임워크인 Synthetic Minority Sampler (SMS)를 제안한다. SMS는 두 개의 생성기를 사용하여 구분이 명확한 샘플과 명확하지 않은 샘플을 적절한 비율로 생성하고 이를 통해 분류기를 더욱 견고하고 일반화된 방향으로 학습시킨다. SMS는 해당 논문에서 고안된 손실 함수 (class-conditional diversity loss)를 사용하여 인공적으로 생성된 소수 클래스 샘플의 다양성을 보장한다. 또한 미니 배치의 클래스 비율을 적절하게 배분하는 임의 샘플러와 구분기 (discriminator)의 오버피팅 방지를 위한 데이터 증강 기법을 사용하여 SMS의 학습을 안정화한다. 실험 결과에서는 SMS를 통해 생성된 인공 데이터를 기존의 데이터셋에 추가하여 학습한 모델이 이진 분류 (binary classification) 문제에서 탁월한 성능을 보였으며, 경쟁 메소드보다 10.06% 높은 F1 스코어를 기록하였다.I. Introduction 1 II. Related Works 4 III. Proposed Method 6 3.1 Overview 6 3.2 Normal and Borderline Sample Generation 8 3.3 Class-conditional DiversityLoss 10 3.4 Generators, Discriminator, and Classifier Training 11 3.5 Stabilizing Training 12 IV. Experiments 14 4.1 Experimental Settings 14 4.2 Performance 19 4.3 Synthetic Image Quality and Diversity 22 4.4 Ablation Study 23 V. Conclusion 26 References 27 Abstract in Korean 29Maste