Financial performance evaluation of Japanese manufacturing industries: A combined use of DEA discriminant analysis with principal component analysis

2007 ◽  
Author(s):  
Mika Goto ◽  
Akihiko Suzuki ◽  
Yasuhiro Fuwa ◽  
Tomohiro Sato ◽  
Toshiyuki Sueyoshi
Keyword(s):  
Principal Component Analysis ◽  
Performance Evaluation ◽  
Discriminant Analysis ◽  
Financial Performance ◽  
Principal Component ◽  
Component Analysis ◽  
Manufacturing Industries ◽  
Combined Use

Tobacco Enterprise Logistics Performance Evaluation Based on Principal Component Analysis

2021 ◽  
Vol 7 (5) ◽  
pp. 2445-2453
Author(s):  
Fan Yuqing ◽  
Song Zhigang ◽  
Gao Honghu
Keyword(s):  
Principal Component Analysis ◽  
Performance Evaluation ◽  
Principal Component ◽  
Component Analysis ◽  
Performance Level ◽  
Logistics Management ◽  
Enterprise Management ◽  
Management Performance ◽  
Performance Evaluation Index ◽  
Enterprise Logistics

Objectives: Tobacco logistics matters greatly in the development of the tobacco industry. Logistics performance evaluation is a sub-part of tobacco enterprise management performance evaluation. The in-depth study of logistics performance evaluation can form a more comprehensive enterprise management performance evaluation system, and provide logistics management tools such as control, diagnosis and coordination for enterprise logistics management. Methods: In this paper, a tobacco enterprise logistics performance evaluation index system is constructed from six aspects, including transportation, warehousing, inventory management, informatization, customer service and finance. A tobacco enterprise logistics performance evaluation method based on principal component analysis is put forward. Results: Through calculation, the comprehensive evaluation value of tobacco logistics performance of enterprises X in each year is obtained. The enterprise logistics performance is the worst in 2016. The enterprise logistics performance is the best in 2019. It indicates that the logistics level and ability of tobacco enterprises are improving year by year. Conclusion: Seen from the example, the principal component analysis method can be used to properly evaluate the logistics performance level of tobacco enterprises, and provide objective and quantitative reference data for tobacco enterprises to improve their logistics performance level and benefits.

scholarly journals Analisis Metode Pengenalan Wajah Two Dimensial Principal Component Analysis (2DPCA) dan Kernel Fisher Discriminant Analysis Menggunakan Klasifikasi KNN (K- Nearest Neighbor)

2020 ◽  
Vol 2 (2) ◽  
pp. 29-38
Author(s):  
Abdur Rohman Harits Martawireja ◽  
Hilman Mujahid Purnama ◽  
Atika Nur Rahmawati
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Principal Component ◽  
Component Analysis ◽  
K Nearest Neighbor ◽  
Fisher Discriminant Analysis ◽  
Fisher Discriminant ◽  
Kernel Fisher Discriminant Analysis

Pengenalan wajah manusia (face recognition) merupakan salah satu bidang penelitian yang penting dan belakangan ini banyak aplikasi yang menerapkannya, baik di bidang komersil ataupun di bidang penegakan hukum. Pengenalan wajah merupakan sebuah sistem yang berfungsikan untuk mengidentifikasi berdasarkan ciri-ciri dari wajah seseorang berbasis biometrik yang memiliki keakuratan tinggi. Pengenalan wajah dapat diterapkan pada sistem keamanan. Banyak metode yang dapat digunakan dalam aplikasi pengenalan wajah untuk keamanan sistem, namun pada artikel ini akan membahas tentang dua metode yaitu Two Dimensial Principal Component Analysis dan Kernel Fisher Discriminant Analysis dengan metode klasifikasi menggunakan K-Nearest Neigbor. Kedua metode ini diuji menggunakan metode cross validation. Hasil dari penelitian terdahulu terbukti bahwa sistem pengenalan wajah metode Two Dimensial Principal Component Analysis dengan 5-folds cross validation menghasilkan akurasi sebesar 88,73%, sedangkan dengan 2-folds validation akurasi yang dihasilkan sebesar 89,25%. Dan pengujian metode Kernel Fisher Discriminant dengan 2-folds cross validation menghasilkan akurasi rata rata sebesar 83,10%.

2D Image Matrix-Based Discriminator

2006 ◽  
pp. 258-286
Author(s):  
David Zhang ◽  
Xiao-Yuan Jing ◽  
Jian Yang
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Two Dimensional ◽  
Fisher Linear Discriminant ◽  
Linear Discriminant ◽  
Image Projection ◽  
Projection Techniques

This chapter presents two straightforward image projection techniques — two-dimensional (2D) image matrix-based principal component analysis (IMPCA, 2DPCA) and 2D image matrix-based Fisher linear discriminant analysis (IMLDA, 2DLDA). After a brief introduction, we first introduce IMPCA. Then IMLDA technology is given. As a result, we summarize some useful conclusions.

scholarly journals Development of a Piezoelectric-Based Odor Reproduction System

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 870
Author(s):  
Tengteng Wen ◽  
Dehan Luo ◽  
Yongjie Ji ◽  
Pingzhong Zhong
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Electronic Nose ◽  
Principal Component ◽  
Component Analysis ◽  
Piezoelectric Transducers ◽  
Linear Discriminant ◽  
Reproduction System ◽  
Machine Olfaction

Odor reproduction, a branch of machine olfaction, is a technology through which a machine represents various odors by blending several odor sources in different proportions and releases them. In this paper, an odor reproduction system is proposed. The system includes an atomization-based odor dispenser using 16 micro-porous piezoelectric transducers. The authors propose the use of an electronic nose combined with a Principal Component Analysis–Linear Discriminant Analysis (PCA–LDA) model to evaluate the effectiveness of the system. The results indicate that the model can be used to evaluate the system.

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