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This paper proposed a novel approach for the Power Quality (PQ) disturbances classification based on the wavelet transform and self organizing learning array (SOLAR) system. Wavelet transform is utilized to extract feature vectors for various PQ disturbances based on the multiresolution analysis (MRA). These feature vectors then are applied to a SOLAR system for training and testing. SOLAR has three advantageous over a typical neural network: data driven learning, local interconnections and entropy based self-organization. Several typical PQ disturbances are taken into consideration in this paper. Comparison research between the proposed method, the support vector machine (SVM) method and existing literature reports show that the proposed method can provide accurate classification results. By the hypothesis test of the averages, it is shown that there is no statistically significant difference in performance of the proposed method for PQ classification when different wavelets are chosen. This means one can choose the wavelet with short wavelet filter length to achieve good classification results as well as small computational cost. Gaussian white noise is considered and the Monte Carlo method is used to simulate the performance of the proposed method in different noise conditions. [ABSTRACT FROM AUTHOR]

Recently, the low-voltage powerline communication has become a hot research topic for electrical advancements. However, effective implementation of high-quality signal transmission over this kind of network presents difficulties, because of the unpredictable load effects, noise, high attenuation, reflection, and resonance effects. Furthermore, as the frequency of the reflection signal is the same as that of the sending signal, it is difficult to pick up the useful information from the received signal. This paper aims to find out an effective way to deal with this problem. Since there is phase difference between the reflection signal and the sending signal, the complex wavelet transform is used to get additional information. By use of the additional information, a novel method to obtain the useful signal is proposed. Experiment results show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Background. Cinobufagin has been widely used in the treatment of carcinoma and plays an important role in the relief of cancer pain. But the involved mechanism remains unknown. Aim. To investigate the changes in thermal and mechanical hyperalgesia in paw cancer pain in mice and the action mechanism of cinobufagin using a paw cancer pain model. Methods. 60 female mice were randomly divided into 5 groups: control group, model group, cinobufagin group, cinobufagin +NAL-M group, and morphine group; except ones in control group, mice were inoculated with H22 hepatoma cells in the right hind paw. From the 9th day after inoculation, mice were administrated drug once daily lasting for 8 days. The pain behavior was determined on the 2nd, 4th, 6th, and 8th days before and after administration. On the last day, they were sacrificed. The levels of β-END, CRF, and IL-1β were analyzed by ELISA; immunohistochemistry was performed to detect the expressions of β-END, POMC, and μ-OR in the tumor and adjacent tissue. Results. The thresholds of thermal pain and mechanical pain were significantly increased by cinobufagin. Moreover, the expressions of β-END, CRF, POMC, and μ-OR were significantly upregulated by cinobufagin. The analgesic effect of cinobufagin was blocked by the peripheral opioid receptor antagonist NAL-M. Conclusions. Cinobufagin significantly relieved cancer pain in mice and raised their pain threshold, mainly upregulating the expression levels of β-END and μ-OR in the hind paw tumor and adjacent tissue. [ABSTRACT FROM AUTHOR]

Indirubin, one of the key components of medicinal plants including Isatis tinctoria, Polygonum tinctorium, and Strobilanthes cusia, possesses great medicinal efficacy in the treatment of chronic myelocytic leukemia (CML). Due to misidentification and similar name, materials containing indirubin and their close relatives frequently fall prey to adulteration. In this study, we selected an internal transcribed spacer 2 (ITS2) for distinguishing these indirubin-containing species from five of their usual adulterants, after assessing identification efficiency of matK, rbcL, psbA-trnH, and ITS2 among these species. The results of genetic distances and neighbor-joining (NJ) phylogenetic tree indicated that ITS2 region is a powerful DNA barcode to accurately identify these indirubin-containing species and discriminate them fromtheir adulterants.Additionally, high performance liquid chromatography (HPLC) was used to verify indirubin in different organs of the above species.The results showed that indirubin had been detected in the leaves of Is. tinctoria, P. tinctorium, S. cusia, and Indigo Naturalis (made from their mixture), but not in their roots, or in the leaves of their adulterants. Therefore, this study provides a novel and rapid method to identify and verify indirubin-containing medicinal plants for effective natural treatment of CML. [ABSTRACT FROM AUTHOR]

In this paper, a new concept of formation learning control is introduced to the field of formation control of multiple autonomous underwater vehicles (AUVs), which specifies a joint objective of distributed formation tracking control and learning/identification of nonlinear uncertain AUV dynamics. A novel two-layer distributed formation learning control scheme is proposed, which consists of an upper-layer distributed adaptive observer and a lower-layer decentralized deterministic learning controller. This new formation learning control scheme advances existing techniques in three important ways: 1) the multi-AUV system under consideration has heterogeneous nonlinear uncertain dynamics; 2) the formation learning control protocol can be designed and implemented by each local AUV agent in a fully distributed fashion without using any global information; and 3) in addition to the formation control performance, the distributed control protocol is also capable of accurately identifying the AUVs' heterogeneous nonlinear uncertain dynamics and utilizing experiences to improve formation control performance. Extensive simulations have been conducted to demonstrate the effectiveness of the proposed results.

This paper presents a data-based finite-horizon optimal control approach for discrete-time nonlinear affine systems. The iterative adaptive dynamic programming (ADP) is used to approximately solve Hamilton-Jacobi-Bellman equation by minimizing the cost function in finite time. The idea is implemented with the heuristic dynamic programming (HDP) involved the model network, which makes the iterative control at the first step can be obtained without the system function, meanwhile the action network is used to obtain the approximate optimal control law and the critic network is utilized for approximating the optimal cost function. The convergence of the iterative ADP algorithm and the stability of the weight estimation errors based on the HDP structure are intensively analyzed. Finally, two simulation examples are provided to demonstrate the theoretical results and show the performance of the proposed method.

This paper proposes a novel event-triggered adaptive dynamic programming (ADP) control method for nonlinear continuous-time system with unknown internal states. Comparing with the traditional ADP design with a fixed sample period, the event-triggered method samples the state and updates the controller only when it is necessary. Therefore, the computation cost and transmission load are reduced. Usually, the event-triggered method is based on the system entire state which is either infeasible or very difficult to obtain in practice applications. This paper integrates a neural-network-based observer to recover the system internal states from the measurable feedback. Both the proposed observer and the controller are aperiodically updated according to the designed triggering condition. Neural network techniques are applied to estimate the performance index and help calculate the control action. The stability analysis of the proposed method is also demonstrated by Lyapunov construct for both the continuous and jump dynamics. The simulation results verify the theoretical analysis and justify the efficiency of the proposed method.