Dynamic modeling and CPG-based trajectory generation for a masticatory rehab robot

Human mastication is a complex and rhythmic biomechanical process which is regulated by a brain stem central pattern generator (CPG). Masticatory patterns, frequency and amplitude of mastication are different from person to person and significantly depend on food properties. The central nervous system controls the activity of muscles to produce smooth transitions between different movements. Therefore, to rehab human mandibular system, there is a real need to use the concept of CPG for development of a new methodology in jaw exercises and to help jaw movements recovery. This paper proposes a novel method for real-time trajectory generation of a mastication rehab robot. The proposed method combines several methods and concepts including kinematics, dynamics, trajectory generation and CPG. The purpose of this article is to provide a methodology to enable physiotherapists to perform the human jaw rehabilitation. In this paper, the robotic setup includes two Gough–Stewart platforms. The first platform is used as the rehab robot, while the second one is used to model the human jaw system. Once the modeling is completed, the second robot will be replaced by an actual patient for the selected physiotherapy. Gibbs–Appell’s formulation is used to obtain the dynamics equations of the rehab robot. Then, a method based on the Fourier series is employed to tune parameters of the CPG. It is shown that changes in leg lengths, due to the online changes of the mastication parameters, occur in a smooth and continuous manner. The key feature of the proposed method, when applied to human mastication, is its ability to adapt to the environment and change the chewing pattern in real-time parameters, such as amplitudes as well as jaw movements velocity during mastication.     

Predicting performance in ASEAN banks: an integrated fuzzy MCDM–neural network approach

This paper presents a performance assessment of 88 Association of Southeast Asian Nations banks from 2010 to 2013, using an integrated three‐stage approach on financial criteria that emulates the CAMELS rating system. More precisely, fuzzy analytic hierarchy process is used first to assess the relative weights of a number of criteria related to capital adequacy (C), asset quality (A), management quality (M), earnings (E), liquidity (L), and sensitivity to market risk (S) based on the opinion of 88 Association of Southeast Asian Nations experts. Then, these weights are used as technique for order of preference by similarity to ideal solution inputs to assess their relative efficiency. Lastly, neural networks are combined with technique for order of preference by similarity to ideal solution results to produce a model for banking performance with effective predictive ability. The results reveal that contextual variables have a prominent impact on efficiency. Specifically, parsimony in equity leveraging derived from Islamic finance principles may be the underlying cause in explaining higher efficiency levels.     

Adaptive Network-Based Fuzzy Inference Systems Coupled with Genetic Algorithms for Predicting Soil Permeability Coefficient

This paper extends hybrid-type optimization models of genetic algorithm adaptive network-based fuzzy inference system (GA-ANFIS) for predicting the soil permeability coefficient (SPC) of different types of soil. In these models, GA optimizes parameters of a subtractive clustering technique that controls the structure of the ANFIS model’s fuzzy rule base. Simultaneously, a hybrid leaning algorithm is employed in the ANFIS, as a trained fuzzy inference system (FIS), which optimally determines the parameter sets of the examined FISs in ANFIS. Using an updated large database of SPCs consisting of 338 fine-grained, 178 mixed and 94 granular soil samples, GA-ANFIS framework constructs different models of predicting the permeability coefficient of respectively fine-grained, mixed and granular soils. A fuzzy C-mean technique has been used to cluster the entire data samples of each type of soil and divide them uniformly into training and testing data sets. Different prediction models of SPC have been trained and tested for each of the three soil types, and the appropriate models have been selected. The selected models have been compared with ANN and modified-by-GA empirical prediction models. Results show that the constructed GA-ANFIS models outperform the other models in terms of the prediction accuracy and the generalization capability.     

Cit-CSP及其应用

介绍了Cit-CSP--Cit/E-commerce信息安全保障平台子系统，并阐述了它所提供的消息摘要、块加密、加密(公钥)、签名和MAC等功能和服务。     

Controller Design Based On Wavelet Neural Adaptive Proportional Plus Conventional Integral-Derivative For Bilateral Teleoperation Systems With Time-Varying Parameters

International Journal of Control, Automation and Systems - In this study, a new controller method based on wavelet neural adaptive proportional plus conventional integral-derivative (WNAP+ID)...     

A powerful hybrid clustering method based on modified stem cells and Fuzzy C-means algorithms

One of the simple techniques for Data Clustering is based on Fuzzy C-means (FCM) clustering which describes the belongingness of each data to a cluster by a fuzzy membership function instead of a crisp value. However, the results of fuzzy clustering depend highly on the initial state selection and there is also a high risk for getting the best results when the datasets are large. In this paper, we present a hybrid algorithm based on FCM and modified stem cells algorithms, we called it SC-FCM algorithm, for optimum clustering of a dataset into K clusters. The experimental results obtained by using the new algorithm on different well-known datasets compared with those obtained by K-means algorithm, FCM, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC) Algorithm demonstrate the better performance of the new algorithm.     

Crop yield prediction from remotely sensed vegetation indices and primary productivity in arid and semi-arid lands

Global demands for biomass and arable lands are expected to double in the next 35 years. Scarcity of water resources in arid and semi-arid areas poses a serious threat to their agricultural productivity and hence their food security. In this study, we examine whether crop yields can be predicted from remotely sensed vegetation indices and remotely sensed estimates of primary productivity. Spatial relationships between remotely sensed enhanced vegetation index (EVI), net photosynthesis (P_Net), and gross and net primary production (GPP and NPP, respectively) in irrigated semi-arid and arid agro-ecosystems since the beginning of the century are analysed. The conflict-affected country of Syria is selected as the case study. Relationships between EVI and crop yield are investigated in an effort to enhance food production estimates in affected areas outside governmental jurisdictions. Estimates of NPP derived from reported irrigated agriculture crop data in a semi-arid and an arid zone are compared to remotely sensed NPP in a geospatial environment. Results show that winter crop yields are correlated with spring GPP in semi-arid zones of the study area (R² = 0.85). Summer crop yield can be predicted from either cumulative summer EVI (R² = 0.77) or P_Net in most zones. Where fully irrigated fields are surrounded by hyper-arid landscape, summer P_Net was negative in all instances and EVI was inversely correlated with yield. NPP from crops was much higher (290 gC m^?2 year^?1) in those regions than MOD17 NPP (70 gC m^–2), where 1.0 g of carbon is equivalent to 2.2 g of oven-dry organic matter (= 45% carbon by weight). The gap was less in semi-arid zones (2–39% difference). Overall crop-derived NPP for the period 2000–2013 was 322 versus 300 gC m^–2 for that remotely sensed within the cropped zones of the political units. The results of this study are crucial to derive accurate estimates of irrigated agriculture productivity and to study the effect of the latter on net ecosystem carbon storage.     

Memory-efficient real-time map building using octree of planes and points

This paper presents an octree-based map building algorithm for mobile home-service robots. The robot is equipped with a time-of-flight camera, which produces point clouds of the environment surfaces. Given the successive input of point clouds, a 3D map is incrementally computed in real time. The map is accurate and memory-efficient because the octree nodes containing points on a plane are merged and represented simply by an index to the plane. The real-time performance is achieved largely due to the parallel processing capability of the many-core Graphics Processing Unit used for plane extraction.