The Time Delay Neural Network (TDNN) is one of the neural network architectures that give excellent performance in tasks involving classification of temporal signals, such as phoneme classification, on-line gesture and handwriting recognition, and many others. One particular problem that occurs in on-line recognition tasks is how to deal with input patterns that are incorrectly recognized becau...

Previous research for short-term traffic prediction mostly forecasts only one time interval ahead. Such a methodology may not be adequate for response to emergency circumstances and road maintenance activities that last for a few hours or a longer period. In this study, various approaches, including naïve factor methods, exponential weighted moving average (EWMA), autoregressive integrated movi...

This paper presents a multi-strategic and hybrid approach for large-scale integrated speech and natural language processing, employing connectionist, statistical and symbolic techniques. The developed spoken Korean processing engine (SKOPE) integrates connectionist TDNN-based phoneme recognition technique with statistical Viterbi-based lexical decoding and symbolic morphological/phonological an...

Research into advanced traveler information systems (ATIS) for rural roads is limited. However, highway agencies expect to implement intelligent transportation systems (ITS) in both urban and rural areas. In this paper, genetic algorithms (GAs) are used to design both time delay neural network (TDNN) models as well as locally weighted regression (LWR) models to predict shortterm traffic for two...

A new tightly coupled speech and natural language integration model is presented for a TDNN-based large vocabulary continuous speech recognition system. Unlike the popular n-best techniques developed for integrating mainly HMM-based speech and natural language systems in word level, which is obviously inadequate for the morphologically complex agglutinative languages, our model constructs a spo...

Connectionist Rpeech recognition systems are often handicapped by an inconsistency between training and testing criteria. This problem is addressed by the Multi-State Time Delay Neural Network (MS-TDNN), a hierarchical phonf'mp and word classifier which uses DTW to modulate its connectivit.y pattern, and which is directly trained on word-level targets. The consistent use of word accuracy as a c...

|In this paper we present an algorithm for recognizing walking pedestrians in sequences of grayscale stereo images taken from a moving camera pair. The method has been designed for use in a driver assistance system for the inner city environment warning the car driver of tra c participants that might cause dangerous situations. Our algorithm is divided into two parts: First, a preliminary detec...

Humans possess an outstanding ability to understand contact states, thereby leading appropriate actions. In this study, we consider the operation of autonomous machining tasks on heterogeneous materials using robots; mechanism poses a challenge as robotic is mainly performed homogeneous current technology. Herein, propose method recognize e.g. and deformation types fragile objects, selectively ...