Eligibility Propagation to Speed up Time Hopping for Reinforcement Learning

A mechanism called Eligibility Propagation is proposed to speed up the Time Hopping technique used for faster Reinforcement Learning in simulations. Eligibility Propagation provides for Time Hopping similar abilities to what eligibility traces provide for conventional Reinforcement Learning. It propagates values from one state to all of its temporal predecessors using a state transitions graph. Experiments on a simulated biped crawling robot confirm that Eligibility Propagation accelerates the learning process more than 3 times.Comment: 7 page

Piecewise Trend Approximation: A Ratio-Based Time Series Representation

A time series representation, piecewise trend approximation (PTA), is proposed to improve efficiency of time series data mining in high dimensional large databases. PTA represents time series in concise form while retaining main trends in original time series; the dimensionality of original data is therefore reduced, and the key features are maintained. Different from the representations that based on original data space, PTA transforms original data space into the feature space of ratio between any two consecutive data points in original time series, of which sign and magnitude indicate changing direction and degree of local trend, respectively. Based on the ratio-based feature space, segmentation is performed such that each two conjoint segments have different trends, and then the piecewise segments are approximated by the ratios between the first and last points within the segments. To validate the proposed PTA, it is compared with classical time series representations PAA and APCA on two classical datasets by applying the commonly used K-NN classification algorithm. For ControlChart dataset, PTA outperforms them by 3.55% and 2.33% higher classification accuracy and 8.94% and 7.07% higher for Mixed-BagShapes dataset, respectively. It is indicated that the proposed PTA is effective for high dimensional time series data mining

Visualization of Complex Networks based on Dyadic Curvelet Transform

A visualization method is proposed for understanding the structure of complex networks based on an extended Curvelet transform named Dyadic Curvelet Transform (DClet). The proposed visualization method comes to answer specific questions about structures of complex networks by mapping data into orthogonal localized events with a directional component via the Cartesian sampling sets of detail coefficients. It behaves in the same matter as human visual system, seeing in terms of segments and distinguishing them by scale and orientation. Compressing the network is another fact. The performance of the proposed method is evaluated by two different networks with structural properties of small world networks with N = 16 vertices, and a globally coupled network with size N = 1024 and 523 776 edges. As the most large scale real networks are not fully connected, it is tested on the telecommunication network of Iran as a real extremely complex network with 92 intercity switching vertices, 706 350 E1 traffic channels and 315 525 transmission channels. It is shown that the proposed method performs as a simulation tool for successfully design of network and establishing the necessary group sizes. It can clue the network designer in on all structural properties that network has

Fuzzy Local Trend Transform based Fuzzy Time Series Forecasting Model

A fuzzy local trend transform based fuzzy time series forecasting model is proposed to improve practicability and forecast accuracy by providing forecast of local trend variation based on the linguistic representation of ratios between any two consecutive points in original time series. Local trend variation satisfies a wide range of real applications for the forecast, the practicability is thereby improved. Specific values based on the forecasted local trend variations that reflect fluctuations in historical data are calculated accordingly to enhance the forecast accuracy. Compared with conventional models, the proposed model is validated by about 50% and 60% average improvement in terms of MLTE (mean local trend error) and RMSE (root mean squared error), respectively, for three typical forecasting applications. The MLTE results indicate that the proposed model outperforms conventional models significantly in reflecting fluctuations in historical data, and the improved RMSE results confirm an inherent enhancement of reflection of fluctuations in historical data and hence a better forecast accuracy. The potential applications of the proposed fuzzy local trend transform include time series clustering, classification, and indexing

Mechanism Design and Performance Analysis of a Sitting/Lying Lower Limb Rehabilitation Robot

To meet the various need of stroke patients’ rehabilitation training and carry out complex task training in real scenes, the structure of a lower limb rehabilitation robot with movements in the sagittal plane and coronal plane is usually complicated. A new sitting/lying lower limb rehabilitation robot (LOBO) with a simple mechanism form is proposed, which is designed based on a 2-PRR parallel mechanism. First, the kinematics, singularity, and condition number of the 2-PRR parallel mechanism are analyzed, which provides the basis for mechanism parameter design. Then, through the proportional–derivative control principle, real-time tracking of LOBO’s designed trajectory is realized. Finally, the length parameters of volunteers’ lower limbs are collected, and experimental verification is conducted in LOBO’s passive training mode. The experimental results show the feasibility of LOBO’s movement in the human sagittal and coronal planes. LOBO will help human lower limbs realize the synchronous continuous rehabilitation training of hip, knee, and ankle joints spatially, which could drive the rehabilitation movement of patients’ lower limbs in the sagittal plane and coronal plane in future clinical research. LOBO can also be applied to muscle strength training for the elderly to combat the effects of aging

Mechanism Design and Performance Analysis of a Sitting/Lying Lower Limb Rehabilitation Robot

To meet the various need of stroke patients’ rehabilitation training and carry out complex task training in real scenes, the structure of a lower limb rehabilitation robot with movements in the sagittal plane and coronal plane is usually complicated. A new sitting/lying lower limb rehabilitation robot (LOBO) with a simple mechanism form is proposed, which is designed based on a 2-PRR parallel mechanism. First, the kinematics, singularity, and condition number of the 2-PRR parallel mechanism are analyzed, which provides the basis for mechanism parameter design. Then, through the proportional–derivative control principle, real-time tracking of LOBO’s designed trajectory is realized. Finally, the length parameters of volunteers’ lower limbs are collected, and experimental verification is conducted in LOBO’s passive training mode. The experimental results show the feasibility of LOBO’s movement in the human sagittal and coronal planes. LOBO will help human lower limbs realize the synchronous continuous rehabilitation training of hip, knee, and ankle joints spatially, which could drive the rehabilitation movement of patients’ lower limbs in the sagittal plane and coronal plane in future clinical research. LOBO can also be applied to muscle strength training for the elderly to combat the effects of aging

Research on Monocular-Vision-Based Finger-Joint-Angle-Measurement System

The quantitative measurement of finger-joint range of motion plays an important role in assessing the level of hand disability and intervening in the treatment of patients. An industrial monocular-vision-based knuckle-joint-activity-measurement system is proposed with short measurement time and the simultaneous measurement of multiple joints. In terms of hardware, the system can adjust the light-irradiation angle and the light-irradiation intensity of the marker by actively adjusting the height of the light source to enhance the difference between the marker and the background and reduce the difficulty of segmenting the target marker and the background. In terms of algorithms, a combination of multiple-vision algorithms is used to compare the image-threshold segmentation and Hough outer- and inner linear detection as the knuckle-activity-range detection method of the system. To verify the accuracy of the visual-detection method, nine healthy volunteers were recruited for experimental validation, and the experimental results showed that the average angular deviation in the flexion/extension of the knuckle was 0.43° at the minimum and 0.59° at the maximum, and the average angular deviation in the adduction/abduction of the knuckle was 0.30° at the minimum and 0.81° at the maximum, which were all less than 1°. In the multi-angle velocimetry experiment, the time taken by the system was much less than that taken by the conventional method