物件導向軟體之架構(Object-Oriented Software Construction)探討

Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

Laboratory-based courses play a critical role in scientific education. Automation is changing the nature of these laboratories, and there is a long-running debate about the value of hands-on versus simulated laboratories. In addition, the introduction of remote laboratories adds a third category to the debate. Through a review of the literature related to these labs in education, the authors draw several conclusions about the state of current research. The debate over different technologies is confounded by the use of different educational objectives as criteria for judging the laboratories: Hands-on advocates emphasize design skills, while remote lab advocates focus on conceptual understanding. We observe that the boundaries among the three labs are blurred in the sense that most laboratories are mediated by computers, and that the psychology of presence may be as important as technology. We also discuss areas for future research.

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Hands-on, simulated, and remote laboratories: A comparative literature review

Applications, systems and methods for permitting simultaneous use of a file by two or more computers over a network may include storing the file locally in a local storage device associated with a local computer; providing access to the file by at least one remote computer which is connectable to the local computer via the network, and wherein at least one of the computers is connectable to the network through a firewall element. The file may be stored in at least one remote storage device associated with the at least one remote computer, respectively, and versions of the file contained on the one or more remote storage devices are synchronized with that on the local device by transmitting over the network connecting the one or more remote storage devices with the local device, at least one of Delta files and Inverse Delta files between the remote storage devices and the local storage device. A method of remotely observing computer activity on a second computer remote with respect to a first computer is also provided. Further, file sharing systems and methods are provided for sharing files among computers.

Managed peer-to-peer applications, systems and methods for distributed data access and storage

The core mission of National Institute of Neurological Disorders and Stroke (NINDS) is twofold. First, NINDS seeks fundamental knowledge about the brain and nervous system. Second, NINDS aims to use that knowledge to reduce the burden of neurological diseases. In support of its mission, NINDS performs and funds basic, translational, and clinical neuroscience research on more than 600 neurological diseases, including genetic diseases (e.g. Huntington’s disease; muscular dystrophy), developmental disorders (e.g. cerebral palsy), neurodegenerative diseases (e.g. Parkinson’s disease; Alzheimer’s disease; multiple sclerosis), metabolic diseases (e.g. Gaucher’s disease), cerebrovascular diseases (e.g. stroke; vascular dementia), trauma (e.g. spinal cord and head injury), convulsive disorders (e.g. epilepsy), infectious diseases (e.g. AIDS dementia) and brain tumors. Common marmosets (Callithrix jacchus) offer unique, powerful advantages to both components of the NINDS mission. In support of the first component, marmosets are particularly well suited for neuroanatomical and functional brain studies, as their brains retain the typical anatomical and functional organization of the primate brain. A major advantage is that the marmoset is a lissencephalic primate, which greatly facilitates the mapping of functional brain areas by neuroimaging techniques, such as fMRI and optical imaging, as well as by electrophysiology, with high spatial resolution. In support of the second component, marmosets are excellent models of neurological disorders. Unlike rodents, marmosets are outbred and every individual is genetically different. Further, the marmoset brain has a gray-to-white matter ratio comparable to humans, which strongly facilitates modeling diseases such as multiple sclerosis and small vessel disease. The species also exhibits the breadth of cognitive sophistication that distinguishes primates from other taxonomic groups. Finally, geneedited marmosets can be generated with an intergeneration time and establishment of transgenic lines 2-3 times faster than other primate species, which makes marmosets be the ideal primate species for the development of genetically engineered lines. For all of the above reasons, marmosets are poised to be a central player to advance the core mission of the NINDS.

National Institute of Neurological Disorders and Stroke

The paper presents a measurement method for power quality analysis in electrical power systems. The method is the evolution of an iterative procedure already set up by the authors and allows the most relevant disturbances in electrical power systems to be detected, localized and estimated automatically. The detection of the disturbance and its duration are attained by a proper application, on the sampled signal, of the continuous wavelet transform (CWT). Disturbance amplitude is estimated by decomposing, in an optimized way, the signal in frequency subbands by means of the discrete time wavelet transform (DTWT). The proposed method is characterized by high rejection to noise, introduced by both measurement chain and system under test, and it is designed for an agile disturbance classification. Moreover, it is also conceived for future implementation both in a real-time measurement equipment and in an off-line analysis tool. In the paper firstly the theoretical background is reported and briefly discussed. Then, the proposed method is described in detail. Finally, some case-studies are examined in order to highlight the performance of the method.

A measurement method based on the wavelet transform for power quality analysis

A remote laboratory for teaching purposes in the field of measuring experiments was activated. Students can access automatic measuring setups and instruments via geographic network and directly carry out real experiments. With respect to current literature solutions, this has been realized on the basis of de-facto networking standards so that students are required to use only a simple commercial Web Internet browser. Moreover, a concurrence of more users on the same measuring setup is allowed. The remote laboratory concept allows measuring resources spread on different geographically remote sites to be utilized by a wide deal of students. In this way, a more complete educational proposal can be economically offered by several geographically remote laboratories specialized in different measuring fields.

A measurement laboratory on geographic network for remote test experiments

A methodology is presented for developing a digital signal-processing architecture capable of simultaneous and automated detection and classification of transient signals. The basic unit of the aforementioned architecture is the wavelet network, which combines the ability of the wavelet transform of analyzing nonstationary signals with the classification capability of artificial neural networks. By exploiting the modularity as well as original strategies concerning wavelet network implementation and training, the method succeeds in enhancing the classification performance with respect to other available solutions.

Wavelet network-based detection and classification of transients

Remotely accessible laboratory for electronic measurement teaching

Analog-to-digital converters (ADCs) are critical components of signal-processing systems. ADC errors can compromise the overall accuracy and the effectiveness of the whole system. This leads researchers to direct increasing attention to error correction topics. In this paper, some ADC error compensation methods are briefly introduced according to a classification criterion based on the main research trends.

Pasquale Daponte

Papers

A measurement method based on the wavelet transform for power quality analysis

A measurement laboratory on geographic network for remote test experiments

Wavelet network-based detection and classification of transients

Remotely accessible laboratory for electronic measurement teaching

A state of the art on ADC error compensation methods