8 research outputs found
30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)
Proceedings of COMADEM 201
MATLAB
A well-known statement says that the PID controller is the "bread and butter" of the control engineer. This is indeed true, from a scientific standpoint. However, nowadays, in the era of computer science, when the paper and pencil have been replaced by the keyboard and the display of computers, one may equally say that MATLAB is the "bread" in the above statement. MATLAB has became a de facto tool for the modern system engineer. This book is written for both engineering students, as well as for practicing engineers. The wide range of applications in which MATLAB is the working framework, shows that it is a powerful, comprehensive and easy-to-use environment for performing technical computations. The book includes various excellent applications in which MATLAB is employed: from pure algebraic computations to data acquisition in real-life experiments, from control strategies to image processing algorithms, from graphical user interface design for educational purposes to Simulink embedded systems
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Laboratory Directed Research and Development Program FY 2004 Annual Report
The Oak Ridge National Laboratory (ORNL) Laboratory Directed Research and Development (LDRD) Program reports its status to the U.S. Department of Energy (DOE) in March of each year. The program operates under the authority of DOE Order 413.2A, 'Laboratory Directed Research and Development' (January 8, 2001), which establishes DOE's requirements for the program while providing the Laboratory Director broad flexibility for program implementation. LDRD funds are obtained through a charge to all Laboratory programs. This report describes all ORNL LDRD research activities supported during FY 2004 and includes final reports for completed projects and shorter progress reports for projects that were active, but not completed, during this period. The FY 2004 ORNL LDRD Self-Assessment (ORNL/PPA-2005/2) provides financial data about the FY 2004 projects and an internal evaluation of the program's management process. ORNL is a DOE multiprogram science, technology, and energy laboratory with distinctive capabilities in materials science and engineering, neutron science and technology, energy production and end-use technologies, biological and environmental science, and scientific computing. With these capabilities ORNL conducts basic and applied research and development (R&D) to support DOE's overarching national security mission, which encompasses science, energy resources, environmental quality, and national nuclear security. As a national resource, the Laboratory also applies its capabilities and skills to the specific needs of other federal agencies and customers through the DOE Work For Others (WFO) program. Information about the Laboratory and its programs is available on the Internet at <http://www.ornl.gov/>. LDRD is a relatively small but vital DOE program that allows ORNL, as well as other multiprogram DOE laboratories, to select a limited number of R&D projects for the purpose of: (1) maintaining the scientific and technical vitality of the Laboratory; (2) enhancing the Laboratory's ability to address future DOE missions; (3) fostering creativity and stimulating exploration of forefront science and technology; (4) serving as a proving ground for new research; and (5) supporting high-risk, potentially high-value R&D. Through LDRD the Laboratory is able to improve its distinctive capabilities and enhance its ability to conduct cutting-edge R&D for its DOE and WFO sponsors. To meet the LDRD objectives and fulfill the particular needs of the Laboratory, ORNL has established a program with two components: the Director's R&D Fund and the Seed Money Fund. As outlined in Table 1, these two funds are complementary. The Director's R&D Fund develops new capabilities in support of the Laboratory initiatives, while the Seed Money Fund is open to all innovative ideas that have the potential for enhancing the Laboratory's core scientific and technical competencies. Provision for multiple routes of access to ORNL LDRD funds maximizes the likelihood that novel and seminal ideas with scientific and technological merit will be recognized and supported
ΠΠΎΠ²ΡΠ΅ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΡΠ΅ ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ Π² ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΈ ΡΠ»ΠΎΠΆΠ½ΡΡ ΡΡΡΡΠΊΡΡΡ : ΠΌΠ°ΡΠ΅ΡΠΈΠ°Π»Ρ Π΄Π²Π΅Π½Π°Π΄ΡΠ°ΡΠΎΠΉ ΠΊΠΎΠ½ΡΠ΅ΡΠ΅Π½ΡΠΈΠΈ Ρ ΠΌΠ΅ΠΆΠ΄ΡΠ½Π°ΡΠΎΠ΄Π½ΡΠΌ ΡΡΠ°ΡΡΠΈΠ΅ΠΌ 4-8 ΠΈΡΠ½Ρ 2018 Π³.
ΠΠ²Π΅Π½Π°Π΄ΡΠ°ΡΠ°Ρ ΠΊΠΎΠ½ΡΠ΅ΡΠ΅Π½ΡΠΈΡ Ρ ΠΌΠ΅ΠΆΠ΄ΡΠ½Π°ΡΠΎΠ΄Π½ΡΠΌ ΡΡΠ°ΡΡΠΈΠ΅ΠΌ Β«ΠΠΎΠ²ΡΠ΅ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΡΠ΅ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ Π² ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΈ ΡΠ»ΠΎΠΆΠ½ΡΡ
ΡΡΡΡΠΊΡΡΡΒ» Π±ΡΠ»Π° ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½Π° Π² ΠΏΠΎΡΠ΅Π»ΠΊΠ΅ ΠΠ°ΡΡΠ½Ρ ΠΠ»ΡΠ°ΠΉΡΠΊΠΎΠ³ΠΎ ΠΊΡΠ°Ρ Ρ 4 ΠΏΠΎ 8 ΠΈΡΠ½Ρ 2018 Π³. ΠΠ°ΡΠ΅ΡΠΈΠ°Π»Ρ ΡΠ±ΠΎΡΠ½ΠΈΠΊΠ° ΠΎΡΠΈΠ΅Π½ΡΠΈΡΠΎΠ²Π°Π½Ρ Π½Π° ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ ΡΠΏΠ΅ΡΠΈΠ°Π»ΠΈΡΡΠ°ΠΌΠΈ Π² ΠΎΠ±Π»Π°ΡΡΠΈ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΡΡ
ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ Π² ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
ΡΡΠ΅ΡΠ°Ρ
ΡΠ΅Π»ΠΎΠ²Π΅ΡΠ΅ΡΠΊΠΎΠΉ Π΄Π΅ΡΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ, Π²ΠΊΠ»ΡΡΠ°Ρ Π²ΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΠΈ ΡΠ΅Π»Π΅ΠΊΠΎΠΌΠΌΡΠ½ΠΈΠΊΠ°ΡΠΈΠΎΠ½Π½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡ, ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΠ΅, Π°ΡΡ
ΠΈΡΠ΅ΠΊΡΡΡΡ ΠΈ Π³ΡΠ°Π΄ΠΎΡΡΡΠΎΠΈΡΠ΅Π»ΡΡΡΠ²ΠΎ, ΠΎΡ
ΡΠ°Π½Ρ ΠΏΡΠΈΡΠΎΠ΄Ρ, Π·Π΄ΡΠ°Π²ΠΎΠΎΡ
ΡΠ°Π½Π΅Π½ΠΈΠ΅, ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΡ ΡΠΈΡΡΠ΅ΠΌ ΠΈΡΠΊΡΡΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΠ°, ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ Π΄ΠΈΡΠΊΡΠ΅ΡΠ½ΡΡ
ΠΈ ΡΡΠΎΡ
Π°ΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΡΡΡΠΊΡΡΡ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΈ ΡΠ²ΡΠ·ΠΈ.ΠΠ° ΡΠΈΡ. Π». Π»ΠΎΠ³ΠΎΡΠΈΠΏ 140 Π»Π΅Ρ Π’Π
Increasing Accuracy Performance through Optimal Feature Extraction Algorithms
This research developed models and techniques to improve the three key modules of popular recognition systems: preprocessing, feature extraction, and classification. Improvements were made in four key areas: processing speed, algorithm complexity, storage space, and accuracy. The focus was on the application areas of the face, traffic sign, and speaker recognition. In the preprocessing module of facial and traffic sign recognition, improvements were made through the utilization of grayscaling and anisotropic diffusion. In the feature extraction module, improvements were made in two different ways; first, through the use of mixed transforms and second through a convolutional neural network (CNN) that best fits specific datasets. The mixed transform system consists of various combinations of the Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT), which have a reliable track record for image feature extraction. In terms of the proposed CNN, a neuroevolution system was used to determine the characteristics and layout of a CNN to best extract image features for particular datasets. In the speaker recognition system, the improvement to the feature extraction module comprised of a quantized spectral covariance matrix and a two-dimensional Principal Component Analysis (2DPCA) function. In the classification module, enhancements were made in visual recognition through the use of two neural networks: the multilayer sigmoid and convolutional neural network. Results show that the proposed improvements in the three modules led to an increase in accuracy as well as reduced algorithmic complexity, with corresponding reductions in storage space and processing time