when no examples of both faults occurring together are included in the training set. The main conclusion is that the spectral representation provides the best recognition performance and that the primary discriminatory information for distinguishing the two types of faults (impeller unbalance and cracked impeller) is contained in the first three harmonics. This work is relevant to the Navy's research program on helicopter gearbox fault diagnosis. CHINA The neural network research in China is also spread out in many universities. The work is very much application oriented, as can be seen by the list of topics and locations in Table 3. Of particular interest to the Navy is the work of Jungang Xu, Zhong Wang, and Youan Ke at the Beijing Institute of Technology on optimum frequency selection for radar target classification by a neural network. The radar cross sections at multiple frequencies are used as the inputs to a backpropagation neural network for radar target classification. The frequencies corresponding to the input nodes that have maximum sensitivities are selected as optimum frequencies needed for classification. The method is applied successfully in classifying simple radar targets in an Anechoic Chamber. Obviously this result has significance in automatic radar target classifications for the Navy. HONG KONG Research is concentrated in the three big universities in Hong Kong. At the Hong Kong University of Science and Technology, there is work on the automatic determination of multilayer feedforward network size for supervised learning and work on using the Householder encoding algorithm for discrete bidirectional associative memory (BAM). The original BAM as proposed by Bart Kosko of the University Table 2. Research Topics and Locations in Australia Topic On-line identification of nonlinear systems Time series analysis Hybrid systems Higher order neural nets Rotating machine fault diagnosis Learning in feedforward networks Texture segmentation Dynamic channel assignment Classification of intracardiac electrocardiograms Probabilistic neural network Reinforcement learning Object recognition for robots Kohonen algorithm on transputers Visual reconstruction networks Network of exponential neurons Self-organizing network for object recognition Genetic algorithms Unsupervised learning for neural trees Kolmogorov representation theorem Location Univ. of Western Australia Univ. of Melbourne Swinburne Inst. of Tech. Telecom Research Labs Royal Melbourne Inst. of Tech. Univ. of Melbourne Univ. of Western Australia Queensland Univ. of Tech. Univ. of Western Australia Basis Function (RBF) classifier of traditionally they have been very strong At City Polytechnic of Hong Kong, a new Hopfield type of training algorithm has been developed whereby the orthogonally coded memories are obtained iteratively. Also a structured backpropagation network has been developed that adapts to the problem. Clearly these are initial efforts in applying neural network technology. At the Chinese University of Hong Kong, the neural network research efforts are spearheaded by Professor Lai-Wan Chan, who is a graduate of the Imperial College in London. They have developed a novel learning algorithm for a recurrent backpropagation network and a system for detecting threedimensional (3-D) motion from a sequence of image frames. At the Chinese University, BAM is studied from the viewpoint of match filtering. Sufficient conditions are found for INDIA stability and attractivity for BAM networks. Also there is work on neural networks that learn the decision boundaries with nonlinear clustering, similar to the Reduced Coulomb Energy (RCE) model of Leon Cooper and the Radial Research appears to be centered at the Indian Institute of Science and the Center for Artificial Intelligence and Robotics. It seems that there should be a lot more work going on in India because At the Indian Institute of Science, a great deal of work is devoted to the study of bidirectional associative memories (BAM). The extended hypercube, as well as the INMOS transputerbased computer system, is used to simulate the parallelism in BAM. The Fokker-Planck equation is also used to study the dynamics of associative memories. The Hopfield-based model for distributed representation of objects and for use as content addressable memory is also investigated. There is also research work on genetic algorithms and learning automata for pattern recognition. Associative memory Self-improving associative network Transform domain backpropagation algorithm Character recognition using holographic memory Object oriented neural network language Fault tolerance in self-organizing nets Tsinghua Univ. Zhejiang Univ. Nanjing Aeronautical Inst. Tsinghua Univ. South China Univ. of Tech. Inst. of Electronics South China Univ. of Tech. Tsinghua Univ. Changsha Inst. of Tech. Inst. of Auto. Academia Sinica South China Univ. of Tech. Beijing Univ. of Post & Telecom. Zhejiang Univ. Southeast Univ. Tsinghua Univ. Peking Univ. number of researchers involved in neural Of particular interest to the Navy is the work of P.Y. Mundkur and U.B. Desai at the Indian Institute of Technology on automatic target recognition. A sequence of images taken from high altitude reconnaissance flights is used as input to the network. The network consists of two cascaded modules, a multilayer perceptron network followed by a modified Maxnet, KOREA to provide translation-invariant recognition of the targets in clutter. The network is partitioned into subnets and a backpropagation algorithm is used to train the subnets. Preliminary results show promise, but a lot more research needs to be done yet. JAPAN Japan clearly is the most technologically advanced country in Asia. The Most of the neural network research is centered at the Korea Advanced Institute of Science and Technology, although there are a few efforts at the other universities and industry. Of particular interest to the Navy is the work of Dae-Young Yim, Sung-Il Chien, and Hyun Son at Kyungpook National University on multiclass 3-D identification and orientation estimation using multilayer feedforward neural networks. Top-down views of three different aircraft (DC-10, Phantom, and MiG21) at different azimuth and roll angles are used for training the network. The training set consists of 216 images (72 images per aircraft at 10° roll and azimuth angles). Two multilayer feedforward neural nets are used, one for classifying the type of aircraft and another for estimating the orientation angle. Out of the three aircraft types, correct classification approaches the 98.6% level, and the accuracy of estimating the orientation is at 89.4%. Of course, these performance levels are extremely good for such a small number of different aircraft types. This work has implications in military automatic target recognition as well as in commercial aviation. Table 5 is a listing of the location of different neural network research in Korea. Error correction learning in three-layer network Adaptive decision feedback equalization Three-layer backpropagation for pattern recognition Finite size multilayer net for polynomials Fuzziness in 3-D surface depth perception Model for conscious and unconscious processing Fuzzy training algorithm for phoneme recognition Pattern classification for remote sensing Rule evaluating neural networks Multiply descent cost competitive learning CombNET-II for written digit classification Feedforward network for robot motion control Finite times of search in multilayer networks Limb function discrimination using EMG signals Ultrasonic 3-D visual sensor Space perception model Human hand position control learning Keio Univ. Nagoya Univ. NEC Corp. Kisarazu National Tech. College Kyoto Inst. of Tech. NTT Commun. Switching Lab Receptive field network for kanji recognition Basin size in autoassociative memory Learning algorithm based on information theory Neural network based adaptive control system Neuromorphic sensing and control Controller for autonomous underwater robot Trajectory generation for biped robot Inverse kinematic calculations Self-learning robot vision system NN/II network for pattern recognition Time warping network for phoneme recognition Structure detection by neural sequence associator Cluster formation in random neural network Parallel ASIC VLSI neurocomputer Inducing algorithm for LTP in hippocampus T-model neural network with learning ability Solving the dynamic traveling salesman problem Sharp Corp. Location Univ. of Osaka Prefecture NTT Transmission System Lab NTT Human Interface Lab Matsushita Electric Yokohama National Univ. Hitachi VLSI Engineering Corp. Kanazawa Univ. Kobe Univ. National Kisarazu Tech. College Univ. of Tokyo Kobe Univ. Hitachi Ltd. Waseda Univ. Kyoto Univ. NTT Human Interface Lab Univ. of Tokushima Hitachi Ltd. Fukui Univ. Tamagawa Univ. Tokyo Univ. of Agric. & Tech. Tokyo Denki Univ. Telecom Systems, Inc. Sony Co., Ltd. Univ. of Osaka Prefecture Toyota Tech. Inst. NTT Human Interface Lab Table 5. Research Topics and Locations in Korea Topic Iterative autoassociative memory ARMA model time series modeling Benchmarks for learning algorithms 3-D aircraft orientation identification Image parameter estimation by error propagation Hopfield network for self-tuning control Functional approximation Hopfield network for obstacle avoidance Perceptrons for image recognition Neural network for systolic design Location Kyungpook National Univ. Korea Adv. Inst. of Sci. & Tech. Korea Adv. Inst. of Sci. & Tech. Pohang Inst. of Sci. & Tech. Kyungpook National Univ. Korea Adv. Inst. of Sci. & Tech. Yonsei Univ. Korea Adv. Inst. of Sci. & Tech. Kwangwoon Univ. Korea Adv. Inst. of Sci. & Tech. Pohang Inst. of Sci. & Tech. Electronics & Telecomm Res. Inst. Korea Adv. Inst. of Sci. & Tech. Korea Adv. Inst. of Sci. & Tech. POSTECH Korea Adv. Inst. of Sci. & Tech. |