An approach to syndrome differentiation in traditional chinese medicine based on neural network

Although the traditional knowledge representation based on rules is simple and explicit, it is not effective in the field of syndrome differentiation in Traditional Chinese Medicine (TCM), which involves many uncertain concepts. To represent uncertain knowledge of syndrome differentiation in TCM, two methods were presented respectively based on certainty factors and certainty intervals. Exploiting these two methods, an approach to syndrome differentiation in TCM was proposed based on neural networks to avoid some limitations of other approaches. The main advantage of the approach is that it may realize uncertain inference of syndrome differentiation in TCM, whereas it doesn't request experts to provide all possible combinations for certainty degrees of symptoms and syndromes. Rather than Back Propagation (BP) algorithm but its modification was employed to improve the capability of generalization of neural networks. First, the standard feedforward multilayer BP neural network and its modification were introduced. Next, two methods for knowledge representation, respectively based on certainty factors and certainty intervals, were presented Then, the algorithm was proposed based on neural network for the uncertain inference of syndrome differentiation in TCM. Finally, an example was demonstrated to illustrate the algorithm

Use of human gestures for controlling a mobile robot via adaptive CMAC network and fuzzy logic controller

Mobile robots with manipulators have been more and more commonly applied in extreme and hostile environments to assist or even replace human operators for complex tasks. In addition to autonomous abilities, mobile robots need to facilitate the human–robot interaction control mode that enables human users to easily control or collaborate with robots. This paper proposes a system which uses human gestures to control an autonomous mobile robot integrating a manipulator and a video surveillance platform. A human user can control the mobile robot just as one drives an actual vehicle in the vehicle’s driving cab. The proposed system obtains human’s skeleton joints information using a motion sensing input device, which is then recognized and interpreted into a set of control commands. This is implemented, based on the availability of training data set and requirement of in-time performance, by an adaptive cerebellar model articulation controller neural network, a finite state machine, a fuzzy controller and purposely designed gesture recognition and control command generation systems. These algorithms work together implement the steering and velocity control of the mobile robot in real-time. The experimental results demonstrate that the proposed approach is able to conveniently control a mobile robot using virtual driving method, with smooth manoeuvring trajectories in various speeds

Type-2 Fuzzy Hybrid Controller Network for Robotic Systems

Dynamic control, including robotic control, faces both the theoretical challenge of obtaining accurate system models and the practical difficulty of defining uncertain system bounds. To facilitate such challenges, this paper proposes a control system consisting of a novel type of fuzzy neural network and a robust compensator controller. The new fuzzy neural network is implemented by integrating a number of key components embedded in a Type-2 fuzzy cerebellar model articulation controller (CMAC) and a brain emotional learning controller (BELC) network, thereby mimicking an ideal sliding mode controller. The system inputs are fed into the neural network through a Type-2 fuzzy inference system (T2FIS), with the results subsequently piped into sensory and emotional channels which jointly produce the final outputs of the network. That is, the proposed network estimates the nonlinear equations representing the ideal sliding mode controllers using a powerful compensator controller with the support of T2FIS and BELC, guaranteeing robust tracking of the dynamics of the controlled systems. The adaptive dynamic tuning laws of the network are developed by exploiting the popular brain emotional learning rule and the Lyapunov function. The proposed system was applied to a robot manipulator and a mobile robot, demonstrating its efficacy and potential; and a comparative study with alternatives indicates a significant improvement by the proposed system in performing the intelligent dynamic control

Temperature-calibrated high-precision refractometer using a tilted fiber Bragg grating

We present a refractometer with main- and vernier-scale to measure the refractive index (RI) of liquids with high precision by using the fine spectrum structure of a tilted fiber Bragg grating (TFBG). The absolute RI values are determined by the accurate wavelength of cut-off mode resonances. The main- and vernier-scale are calibrated by measuring large groups of fine spectra at different cut-off mode resonances in a small RI range, and the use of vernier-scale certainly reduces the RI measurement uncertainty resulted from the discrete cladding mode resonances. The performance of the TFBG-based vernier refractometer is experimentally verified by exploring the temperature dependence of RI of anhydrous ethanol in a near infrared region, showing an enhanced accuracy to the order of 10−4, high repeatability and temperature self-calibration capability

GANCCRobot:Generative Adversarial Nets based Chinese Calligraphy Robot

Robotic calligraphy, as a typical application of robot movement planning, is of great significance for the inheritance and education of calligraphy culture. The existing implementations of such robots often suffer from its limited ability for font generation and evaluation, leading to poor writing style diversity and writing quality. This paper proposes a calligraphic robotic framework based on the generative adversarial nets (GAN) to address such limitation. The robot implemented using such framework is able to learn to write fundamental Chinese character strokes with rich diversities and good quality that is close to the human level, without the requirement of specifically designed evaluation functions thanks to the employment of the revised GAN. In particular, the type information of the stroke is introduced as condition information, and the latent codes are applied to maximize the style quality of the generated strokes. Experimental results demonstrate that the proposed model enables a calligraphic robot to successfully write fundamental Chinese strokes based on a given type and style, with overall good quality. Although the proposed model was evaluated in this report using calligraphy writing, the underpinning research is readily applicable to many other applications, such as robotic graffiti and character style conversion

Small-period long-period fiber grating with improved refractive index sensitivity and dual-parameter sensing ability

We UV inscribe and characterize a long-period fiber grating with a period of 25 μm. A series of polarization-dependent dual-peak pairs can be seen in the transmission spectrum, even though only the symmetrical refractive index modification is introduced. The fabricated grating exhibits a lower temperature sensitivity compared with standard long-period gratings and an enhanced refractive index sensitivity of ∼312.5 nm?RIU averaged from 1.315 to 1.395, which is more than four-fold higher than standard long-period gratings in this range. The full width at half-maximum of the fabricated grating is only about 0.6 nm, allowing for high-resolution sensing. Moreover, the grating period is so small that the attenuation dip corresponding to a high-order Bragg resonance can also be seen, which can act as a monitor of the unwanted perturbation to realize dual-parameter sensing

Diagnosis in traditional Chinese Medicine using Artificial Neural Networks: State-of-the-Art and perspectives

Traditional Chinese Medicine (TCM), one of China's splendid cultural heritages, is the science dealing with human physiology, pathology, diagnosis, treatment and prevention of diseases. With the development of modern science, people come to consider the way of the moderniation of TCM. Recently, many researchers mainly in China attempt to realize Diagnosis in TCM based on Artificial Neural Networks (DTCMANN). This paper aims at providing an overview of recent DTCMANN studies in TCM field, and focuses on the introduction and summarization of the existing research work about DTCMANN. A review of five major situations, where DTCMANN approaches are applied, has been presented For each situation, the DTCMANN approaches employed are outlined as well as the corresponding results. Current research on DTCMANN shows that it is both feasible and promising, and that it is still nearly a piece of virgin soil. The future research direction of DTCMANN is also pointed out based on a discussion of the existing research work