In this article, the authors consider a class of semilinear elliptic equations on fractal sets under some new conditions, which are more weaker than those in usual cases. The authors get the non-trivial and non-negati...In this article, the authors consider a class of semilinear elliptic equations on fractal sets under some new conditions, which are more weaker than those in usual cases. The authors get the non-trivial and non-negative solution of the zero boundary Dirichlet problem using Mountain Pass Lemma.展开更多
A high resolution range profile(HRRP) is a summation vector of the sub-echoes of the target scattering points acquired by a wide-band radar.Generally, HRRPs obtained in a noncooperative complex electromagnetic environ...A high resolution range profile(HRRP) is a summation vector of the sub-echoes of the target scattering points acquired by a wide-band radar.Generally, HRRPs obtained in a noncooperative complex electromagnetic environment are contaminated by strong noise.Effective pre-processing of the HRRP data can greatly improve the accuracy of target recognition.In this paper, a denoising and reconstruction method for HRRP is proposed based on a Modified Sparse Auto-Encoder, which is a representative non-linear model.To better reconstruct the HRRP, a sparse constraint is added to the proposed model and the sparse coefficient is calculated based on the intrinsic dimension of HRRP.The denoising of the HRRP is performed by adding random noise to the input HRRP data during the training process and fine-tuning the weight matrix through singular-value decomposition.The results of simulations showed that the proposed method can both reconstruct the signal with fidelity and suppress noise effectively, significantly outperforming other methods, especially in low Signal-to-Noise Ratio conditions.展开更多
A three-dimensional （3-D） physiological articulatory model was developed to account for the biomechanical properties of the speech organs in speech production. Control of the model to investigate the mechanism of sp...A three-dimensional （3-D） physiological articulatory model was developed to account for the biomechanical properties of the speech organs in speech production. Control of the model to investigate the mechanism of speech production requires an efficient control module to estimate muscle activation patterns, which is used to manipulate the 3-D physiological articulatory model, according to the desired articulatory posture. For this purpose, a feedforward control strategy was developed by mapping the articulatory target to the corresponding muscle activation pattern via the intrinsic representation of vowel articulation. In this process, the articulatory postures are first mapped to the corresponding intrinsic representations; then, the articulatory postures are clustered in the intrinsic representations space and a nonlinear function is approximated for each cluster to map the intrinsic representation of vowel articulation to the muscle activation pattern by using general regression neural networks （GRNN）. The results show that the feedforward control module is able to manipulate the 3-D physiological articulatory model for vowel production with high accuracy both acoustically and articulatorily.展开更多
基金Sponsored by the National NSFC under grant(10631020)
文摘In this article, the authors consider a class of semilinear elliptic equations on fractal sets under some new conditions, which are more weaker than those in usual cases. The authors get the non-trivial and non-negative solution of the zero boundary Dirichlet problem using Mountain Pass Lemma.
基金co-supported by the National Natural Science Foundation of China(Nos.61671463,61471379,61790551 and 61102166)。
文摘A high resolution range profile(HRRP) is a summation vector of the sub-echoes of the target scattering points acquired by a wide-band radar.Generally, HRRPs obtained in a noncooperative complex electromagnetic environment are contaminated by strong noise.Effective pre-processing of the HRRP data can greatly improve the accuracy of target recognition.In this paper, a denoising and reconstruction method for HRRP is proposed based on a Modified Sparse Auto-Encoder, which is a representative non-linear model.To better reconstruct the HRRP, a sparse constraint is added to the proposed model and the sparse coefficient is calculated based on the intrinsic dimension of HRRP.The denoising of the HRRP is performed by adding random noise to the input HRRP data during the training process and fine-tuning the weight matrix through singular-value decomposition.The results of simulations showed that the proposed method can both reconstruct the signal with fidelity and suppress noise effectively, significantly outperforming other methods, especially in low Signal-to-Noise Ratio conditions.
基金Supported partly by the Promoting Science and Technology by the Japan Ministry of Education,Culture,Sports,Science and Technology and the SCOPE of the Ministry of Internal Affairs and Communications (MIC),Japan (No.071705001)
文摘A three-dimensional （3-D） physiological articulatory model was developed to account for the biomechanical properties of the speech organs in speech production. Control of the model to investigate the mechanism of speech production requires an efficient control module to estimate muscle activation patterns, which is used to manipulate the 3-D physiological articulatory model, according to the desired articulatory posture. For this purpose, a feedforward control strategy was developed by mapping the articulatory target to the corresponding muscle activation pattern via the intrinsic representation of vowel articulation. In this process, the articulatory postures are first mapped to the corresponding intrinsic representations; then, the articulatory postures are clustered in the intrinsic representations space and a nonlinear function is approximated for each cluster to map the intrinsic representation of vowel articulation to the muscle activation pattern by using general regression neural networks （GRNN）. The results show that the feedforward control module is able to manipulate the 3-D physiological articulatory model for vowel production with high accuracy both acoustically and articulatorily.
