The development of the prototype to detect fault for the LP Pump equipment.

We propose a method for histogram equalization using supplement sets to improve the performance of speaker recognition when the training and test utterances are very short. The supplement sets are derived using outputs of selection or clustering algorithms from the background speakers’ utterances. The proposed approach is used as a feature normalization method for building histograms when there are insufficient input utterance samples. In addition, the proposed method is used as an i-vector normalization method in an i-vector-based probabilistic linear discriminant analysis (PLDA) system, which is the current state-of-the-art for speaker verification. The ranks of sample values for histogram equalization are estimated in ascending order from both the input utterances and the supplement set. New ranks are obtained by computing the sum of different kinds of ranks. Subsequently, the proposed method determines the cumulative distribution function of the test utterance using the newly defined ranks. The proposed method is compared with conventional feature normalization methods, such as cepstral mean normalization (CMN), cepstral mean and variance normalization (MVN), histogram equalization (HEQ), and the European Telecommunications Standards Institute (ETSI) advanced front-end methods. In addition, performance is compared for a case in which the greedy selection algorithm is used with fuzzy C -means and K -means algorithms. The YOHO and Electronics and Telecommunications Research Institute (ETRI) databases are used in an evaluation in the feature space. The test sets are simulated by the Opus VoIP codec. We also use the 2008 National Institute of Standards and Technology (NIST) speaker recognition evaluation (SRE) corpus for the i-vector system. The results of the experimental evaluation demonstrate that the average system performance is improved when the proposed method is used, compared to the conventional feature normalization methods.

In this paper, we propose a robust speaker feature extraction method using kernel multimodal Fisher discriminant analysis (kernel MFDA). Kernel MFDA has been designed to have the characteristics both of kernel principal component analysis (kernel PCA) and kernel Fisher discriminant analysis (kernel FDA). Therefore, the feature vectors extracted by kernel MFDA are denoised as well as discriminated. For evaluation, we compare our proposed method with principal component analysis (PCA) and kernel PCA on the speaker verification systems.

In this paper, we propose kernel multimodal fisher discriminant analysis (kernel MFDA), a new non-linear feature transformation method, which can be applied to large-scale problems such as speaker recognition tasks. Our proposed method has characteristics of kernel fisher discriminant analysis (kernel FDA) as well as kernel principal component analysis (kernel PCA). The memory requirement of our proposed method is much lower than the other kernel methods. In the experiments, we apply our proposed method to a speaker identification task, and then we compare the accuracy of this method with kernel FDA and kernel PCA in clean and noisy environments. As the results, our proposed method outperforms kernel PCA.

We propose a robust speaker identification system in noisy environments using greedy kernel principal component analysis. We expect that kernel PCA can project important information to some axes and the noise to some other axes in the arbitrary high dimensional space resulting in denoising of the input features. However, it is not easy to use kernel PCA for speaker identification because the storage required for the kernel matrix grows quadratically, and the computational cost grows linearly with the number of training vectors. Therefore, we use greedy kernel PCA which can approximate kernel PCA with small representation error. In the experiments, we compare the accuracy of the greedy kernel PCA with that of the baseline Gaussian mixture models using MFCCs and PCA in noisy environment. As the results, the greedy kernel PCA outperforms conventional methods.

In this paper, we propose a new feature transformation method that is optimized for diagonal covariance Gaussian mixture models which is used for a speaker identification system. We first define an object function as the distances between the Gaussian mixture components and rotate each plane in the feature space to maximize the object function. The optimal degrees of the rotations are found using the particle swarm optimization algorithm. We applied the transformation to a speaker identification task in unknown noisy environments. The proposed transformation is compared with conventional principle component analysis and linear discriminant analysis. The results show that the proposed feature transformation method outperformed existing methods in very high noise environment.

In clock network synthesis, sometimes skew constraints are required only within certain groups of clock sinks and do not exist between different groups. This is the so-called associative skew clock routing problem. Although the number of constraints is reduced, the problem becomes more difficult to solve due to the enlarged solution space. The perhaps only previous work used a very primitive delay model and cannot handle difficult instances in which sink groups are intermingled. We reuse existing techniques to solve this problem, including the difficult instances, based on a more accurate and popular delay model. Experimental results show that our algorithm can reduce the total clock routing wirelength by 12% on average compared to greedy-DME which is one of the best zero skew routing algorithms

This paper describes a design of a tactile sensor, which can measure three components forces. The bio-mimetic tactile sensor, alternative to human’s finger, is composed of 100 micro force sensors, and its size being 10 mm x 10 mm. Each micro force sensor has a square membrane, and its force capacity is 0.3 N in the three-axis directions. On the other hand, the measuring method of three-component force signals without interference was suggested in case of four strain gages and the number of minimum data lines. We have fabricated the tactile sensor using flexible MEMS technology based on polymer material.

The integration of a driving simulator and an unmanned vehicle by means of a performance concept is suggested. Autonomous navigation is one of the most difficult research topics, having constraints on mobility and speed, and lack of environmental information. However, many innovations on the vehicle provide the appropriate automatic control in vehicle subsystem for reducing human error. The master system has a host computer and simulator and the slave system is electronic vehicle system. The slave vehicle system consists of three parts. First, laser sensor system for keeping the front sensory system and ultrasonic sensor system for escaping collision at the side. Second, acceleration system and brake control system for longitudinal motion control. Third, steering control system for lateral motion control. In this research, mechanical and electronic parts are implemented to operate unmanned vehicle as a whole-integrated system. Driving simulator has a 6 degree-of-freedom motion-base and 3-channel fixed-based simulator. They are fully interactive, highly realistic and, based on personal computer, can be updated easily. Driving simulator is constituted by a vision system, a sound system, the system for control force loading and the 6-axis Stewart platform for motion base. This paper focuses on the integration of remote controlled unmanned vehicle and driving simulator. The vehicle mainly controlled lateral direction and longitudinal direction with actuators for controlling vehicle movement and sensors for closed-loop system.