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Moongu Jeon

Bio: Moongu Jeon is an academic researcher from Gwangju Institute of Science and Technology. The author has contributed to research in topics: Computer science & Artificial intelligence. The author has an hindex of 31, co-authored 232 publications receiving 3605 citations. Previous affiliations of Moongu Jeon include Dankook University & University of Minnesota.


Papers
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Journal ArticleDOI
TL;DR: A neuromorphic system for visual pattern recognition realized in hardware and presented and implemented with passive synaptic devices based on modified spike-timing-dependent plasticity, which has been successfully demonstrated by training and recognizing number images from 0 to 9.
Abstract: This paper presents a neuromorphic system for visual pattern recognition realized in hardware. A new learning rule based on modified spike-timing-dependent plasticity is also presented and implemented with passive synaptic devices. The system includes an artificial photoreceptor, a $\hbox{Pr}_{0.7}\hbox{Ca}_{0.3}\hbox{MnO}_{3} $ -based memristor array, and CMOS neurons. The artificial photoreceptor consisting of a CMOS image sensor and a field-programmable gate array converts an image into spike signals, and the memristor array is used to adjust the synaptic weights between the input and output neurons according to the learning rule. A leaky integrate-and-fire model is used for the output neuron that is built together with the image sensor on a single chip. The system has 30 input neurons that are interconnected to 10 output neurons through 300 memristors. Each input neuron corresponding to a pixel in a 5 $\times$ 6 pixel image generates voltage pulses according to the pixel value. The voltage pulses are then weighted and integrated by the memristors and the output neurons, respectively, to be compared with a certain threshold voltage above which an output neuron fires. The system has been successfully demonstrated by training and recognizing number images from 0 to 9.

249 citations

Journal ArticleDOI
TL;DR: This work adapt and extend the discriminant analysis projection used in pattern recognition and shows that by using the generalized singular value decomposition (GSVD), it can achieve the same goal regardless of the relative dimensions of the term-document matrix.
Abstract: In today's vector space information retrieval systems, dimension reduction is imperative for efficiently manipulating the massive quantity of data. To be useful, this lower-dimensional representation must be a good approximation of the full document set. To that end, we adapt and extend the discriminant analysis projection used in pattern recognition. This projection preserves cluster structure by maximizing the scatter between clusters while minimizing the scatter within clusters. A common limitation of trace optimization in discriminant analysis is that one of the scatter matrices must be nonsingular, which restricts its application to document sets in which the number of terms does not exceed the number of documents. We show that by using the generalized singular value decomposition (GSVD), we can achieve the same goal regardless of the relative dimensions of the term-document matrix. In addition, applying the GSVD allows us to avoid the explicit formation of the scatter matrices in favor of working directly with the data matrix, thus improving the numerical properties of the approach. Finally, we present experimental results that confirm the effectiveness of our approach.

174 citations

Proceedings ArticleDOI
01 Dec 2012
TL;DR: Feasibility of a high speed pattern recognition system using 1k-bit cross-point synaptic RRAM array and CMOS-based neuron chip has been experimentally demonstrated and learning capability of a neuromorphic system comprising RRAM synapses andCMOS neurons has been confirmed experimentally, for the first time.
Abstract: Feasibility of a high speed pattern recognition system using 1k-bit cross-point synaptic RRAM array and CMOS-based neuron chip has been experimentally demonstrated. Learning capability of a neuromorphic system comprising RRAM synapses and CMOS neurons has been confirmed experimentally, for the first time.

169 citations

Journal ArticleDOI
TL;DR: A modified binary particle swarm optimization (BPSO) is presented which adopts concepts of the genotype–phenotype representation and the mutation operator of genetic algorithms, and its main feature is that the BPSO can be treated as a continuous PSO.
Abstract: This paper presents a modified binary particle swarm optimization (BPSO) which adopts concepts of the genotype–phenotype representation and the mutation operator of genetic algorithms. Its main feature is that the BPSO can be treated as a continuous PSO. The proposed BPSO algorithm is tested on various benchmark functions, and its performance is compared with that of the original BPSO. Experimental results show that the modified BPSO outperforms the original BPSO algorithm.

161 citations

Journal ArticleDOI
TL;DR: The proposed PCMO-based memristive synapse exhibits the necessary gradual and symmetrical conductance changes, and has been successfully adapted to a neural network system that is likely to intrigue many researchers and stimulate a new research direction.
Abstract: Memristive synapses, the most promising passive devices for synaptic interconnections in artificial neural networks, are the driving force behind recent research on hardware neural networks Despite significant efforts to utilize memristive synapses, progress to date has only shown the possibility of building a neural network system that can classify simple image patterns In this article, we report a high-density cross-point memristive synapse array with improved synaptic characteristics The proposed PCMO-based memristive synapse exhibits the necessary gradual and symmetrical conductance changes, and has been successfully adapted to a neural network system The system learns, and later recognizes, the human thought pattern corresponding to three vowels, ie /a /, /i /, and /u/, using electroencephalography signals generated while a subject imagines speaking vowels Our successful demonstration of a neural network system for EEG pattern recognition is likely to intrigue many researchers and stimulate a new research direction

158 citations


Cited by
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Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

01 Jan 2002

9,314 citations

Reference EntryDOI
15 Oct 2004

2,118 citations