A New Prediction Method of Displacement Errors Caused by Low Stiffness for Industrial Robot.

This paper presents a new method, a fast prediction method based on the Cartesian stiffness model and equivalent spring stiffness (FPM-CSES), to calculate displacement errors of deformation caused by low stiffness for industrial robot. First, the Cartesian stiffness model based on the Jacobian matrix was established for a robot, and then the displacement error model of deformations caused by external force was established based on Cartesian stiffness. Second, the transmission system of the robot's joint was analyzed, and an equivalent method for joint stiffness was presented based on a series spring system. Meanwhile, the stiffness of the key components including the servo motor, harmonic reducer, and timing belt was deduced in detail. Finally, a compared simulation and a measurement experiment were conducted on a 6-joint series robot. It was found that the FPM-CSES could calculate any configuration among the robot's workspace. Compared with the finite element analysis (FEA) method, the presented method is feasible and more efficient. The experimental results showed that the prediction accuracy of the FPM-CSES is rather high, with an average rate of more than 83.72%. Hence, the prediction method presented in this study is simple, fast, and reliable, and could be used to predict and analyze the displacement errors caused by the cutting force, and provide the basis for trajectory planning and error compensation, enhancing the robot's machining performance.

Dietary diversity and mental health in preschoolers in rural China.

OBJECTIVE: To investigate the prevalence of mental health problems among preschoolers in rural China and examine the relationship between dietary diversity and mental health. DESIGN: A cross-sectional survey analysis was performed. Child mental health was assessed with the Strengths and Difficulties Questionnaire (SDQ). Child dietary diversity was assessed with the dietary diversity score (DDS), which was calculated based on nine food groups using a 24-h recall method. Data were analysed using unadjusted and adjusted logistic regression models. SETTING: Two nationally designated poverty counties in Hunan Province of China. PARTICIPANTS: Preschoolers (n 1334) aged 3-5 years, preschools (n 26). RESULTS: Of 950 preschoolers with data on both dietary diversity and mental health, 663 (70 %) were classified as having at least one kind of mental health problem. The prevalences of emotional symptoms, conduct problems, symptoms of hyperactive/inattention, peer relationship problems and poor prosocial behaviour were 39, 27, 23, 12 and 26 %, respectively. Male preschoolers showed higher risks of having mental health problems than their female counterparts on each SDQ subscale except for conduct problems. Moreover, a higher DDS was significantly associated with a lower likelihood of having symptoms of hyperactivity/inattention, peer relationship problems and prosocial behaviour problems after adjustment for confounders (preschoolers' age, gender, cognitive ability, parental migration status, primary caregiver's education and household socio-economic status). CONCLUSIONS: The prevalence of mental health problems was high among preschoolers in rural China. Improving child dietary diversity might be an important strategy to consider in the design of interventions to improve child mental health.

Analysis of Vibration Monitoring Data of Flexible Suspension Lifting Structure Based on Time-Varying Theory.

An elevator is a typical flexible lifting machine. In order to monitor the vibration of elevator structure, the vibration characteristics of an elevator with a traction ratio of 1:1 has been tested experimentally. Sensors were arranged on the platform frame, car roof, and hoist rope to test the vibrations of the elevator in both ascending and descending conditions. The transverse, longitudinal, and coupled transverse-longitudinal vibrations were compared and analyzed. Further, the short-time Fourier transform (STFT) method was used to examine the power spectral density (PSD) of the test results, and the main frequency distribution and influencing factors of the vibration of elevator components were investigated. The results revealed that the transverse and longitudinal vibrations of the platform frame were low-frequency vibrations, which was attributed to the elastic interaction between the platform frame and the car frame. The form and amplitude of longitudinal vibration of the car frame were basically consistent with those of the platform frame, but PSD of the transverse vibration had an obvious peak in the high-frequency region. The transverse and longitudinal vibration frequencies of the hoist rope were higher. Furthermore, the peak PSD value of transverse vibration of the hoist rope was 421 times larger than that of the car frame, so a small disturbance at the end of the rope could lead to a huge disturbance in the center of the rope. Overall, this study provides useful insights on designing an elevator monitoring sensor and relevant data processing.

High-precision displacement measurement method for three degrees of freedom-compliant mechanisms based on computer micro-vision.

A practical method for the high-precision displacement measurements of the three degrees of freedom-compliant mechanisms based on computer micro-vision is proposed. The method consists of two steps. In the first step, the candidate pixels are selected using a ring projection transform matching approach. In the second step, the exact location is determined by an improved pseudo-Zernike moment method. The setup of the micro-vision system is also introduced. A series of simulations is carried out and the results show that the proposed algorithm enjoys extremely high precision and robustness in the presence of image translations and rotations. Finally, a micro-vision system and a laser interferometer measurement (LIM) system are built up to validate and compare the actual performances of the proposed method. The experimental results demonstrate that the proposed approach can obtain a high accuracy and shows higher operability and stability than the LIM system. Moreover, the measuring accuracy can reach a pixel.

Association of dietary diversity and cognition in preschoolers in rural China.

OBJECTIVES: We sought to investigate the cognition of preschoolers in rural China and examine the relationship between dietary diversity and cognition. METHODS: We performed a cross-sectional survey analysis. In 1334 preschoolers ages 3 to 5, from 26 preschools in two nationally designated poverty counties in Hunan Province in China, we calculated the dietary diversity score (DDS) using a 24-h recall method. To measure children's cognitive ability, we assessed the Working Memory Index and Verbal Comprehension Index on the Mandarin-language version of the Wechsler Preschool and Primary Scale of Intelligence, Fourth Edition. Multiple linear regression models were used to estimate the association between DDS and cognitive test scores. RESULTS: A total of 22% of children had a Working Memory Index that was either extremely low or borderline, and 31% of children had a Verbal Comprehension Index that was either extremely low or borderline. The mean (± SD) DDS was 5.65 ± 1.30. Those preschoolers with medium DDS (5 or 6) or high DDS (7 to 9) scored higher on both Working Memory Index-respectively, mean difference (MD), 1.327; 95% confidence interval (CI), 0.246-3.901; and MD, 2.067, 95% CI, 0.261-4.641-and Verbal Comprehension Index-MD, 0.168; 95% CI, 0.072-2.135; and MD, 0.398; 95% CI, 0.032-2.137-than did those with low DDS (0 to 4). CONCLUSIONS: Consuming a more diverse diet may contribute to better cognition in preschoolers. Future research is needed to better understand the possible causal effect of dietary diversity on cognitive development.

Dietary Diversity among Preschoolers: A Cross-Sectional Study in Poor, Rural, and Ethnic Minority Areas of Central South China.

The aim of this study was to document the dietary diversity status of preschool children in poor, rural, and ethnic minority areas of Central South China and examine its associated factors both at home and in preschools. A cross-sectional study including 1328 preschool children aged three or five years from two nationally designated poverty counties in Hunan Province was conducted. A dietary diversity score (DDS) was constructed to measure the dietary patterns based on the 24 h recall method. The mean DDS among the sample children was 5.77 (95% confidence interval: 5.70⁻5.83, range 1 to 9) with a standard deviation of 1.22. Both household characteristics (including the education level of the child's primary caregiver and the nutritional knowledge of the caregiver) and preschool factors (including the nutritional knowledge of the child's preschool principal and teachers, nutritional training to children, and the preschool kitchen manager) were positively associated with children's DDS. The dietary diversity status of children in poor, rural, and ethnic minority areas of Central South China is much lower than that of their peers in other areas. Nutritional education should be provided to caregivers, preschool staff, and children to narrow the gap.

Robust time delay estimation of bioelectric signals using least absolute deviation neural network.

The time delay estimation (TDE) is an important issue in modern signal processing and it has found extensive applications in the spatial propagation feature extraction of biomedical signals as well. Due to the extreme complexity and variability of the underlying systems, biomedical signals are usually nonstationary, unstable and even chaotic. Furthermore, due to the limitations of the measurement environments, biomedical signals are often noise-contaminated. Therefore, the TDE of biomedical signals is a challenging issue. A new TDE algorithm based on the least absolute deviation neural network (LADNN) and its application experiments are presented in this paper. The LADNN is the neural implementation of the least absolute deviation (LAD) optimization model, also called unconstrained minimum L1-norm model, with a theoretically proven global convergence. In the proposed LADNN-based TDE algorithm, a given signal is modeled using the moving average (MA) model. The MA parameters are estimated by using the LADNN and the time delay corresponds to the time index at which the MA coefficients have a peak. Due to the excellent features of L1-norm model superior to Lp-norm (p > 1) models in non-Gaussian noise environments or even in chaos, especially for signals that contain sharp transitions (such as biomedical signals with spiky series or motion artifacts) or chaotic dynamic processes, the LADNN-based TDE is more robust than the existing TDE algorithms based on wavelet-domain correlation and those based on higher-order spectra (HOS). Unlike these conventional methods, especially the current state-of-the-art HOS-based TDE, the LADNN-based method is free of the assumption that the signal is non-Gaussian and the noises are Gaussian and, thus, it is more applicable in real situations. Simulation experiments under three different noise environments, Gaussian, non-Gaussian and chaotic, are conducted to compare the proposed TDE method with the existing HOS-based method. Real application experiment is conducted to extract time delay information between every two adjacent channels of gastric myoelectrical activity (GMA) to assess the spatial propagation characteristics of GMA during different phases of the migrating myoelectrical complex (MMC).

Global exponential stability of delayed competitive neural networks with different time scales.

A competitive neural network model was recently proposed to describe the dynamics of cortical maps, where there are two types of memories: long-term and short-term memories. Such a network is characterized by a system of differential equations with two types of variables, one models the fast neural activity and the other models the slow modification of synaptic strength. In this paper, we introduce a time delay parameter into the neural network model to characterize the signal transmission delays in real neural systems and the finite switch speed in the circuit implementations of neural networks. Then, we analyze the global exponential stability of the delayed competitive neural networks with different time scales. We allow the model has non-differentiable and unbounded functions, and use the nonsmooth analysis techniques to prove the existence and uniqueness of the equilibrium, and derive a new sufficient condition ensuring global exponential stability of the networks.

Chaotic behavior of gastric migrating myoelectrical complex.

Nonlinear feedback induces oscillation, whereas dynamic equilibrium between positive and negative nonlinear feedback generates rhythm. Physiological rhythms are central to life. No absolutely stable or periodic rhythm exists in living tissues. It has been extensively reported that many rhythms in human and animal organs, such as the heart and brain, are, in fact chaotic. The aim of this paper is to investigate whether the migrating myoelectrical complex (MMC) of the stomach was chaotic. The study was performed in eight healthy female hound dogs (15-22 kg), implanted with four pairs of bipolar electrodes on the serosa of the stomach along the greater curvature. After the dogs were completely recovered from the surgery, one complete cycle of gastric MMC was recorded from the serosal electrodes. Using Takens' embedding theorem, two parameters reflecting chaotic behavior, the attractor and the Lyapunov exponent of the myoelectrical recording, were reconstructed and computed, respectively. Statistical analysis was performed to investigate the difference in the Lyapunov exponents among different phases of the MMC. The results show that the MMC of the stomach is chaotic. Different phases of the MMC are characterized with different shapes of the attractors and different values of Lyapunov exponents. The characteristic chaotic behavior of the gastric MMC may be utilized for the identification of different phases.

Some sufficient conditions for global exponential stability of delayed Hopfield neural networks.

In this paper, we have derived some sufficient conditions for existence and uniqueness of equilibrium and global exponential stability in delayed Hopfield neural networks by using a different approach from the usually used one where the existence, uniqueness of equilibrium and stability are proved in two separate steps, rather we first prove global exponential convergence to 0 of the difference between any two solutions of the original neural networks, the existence and uniqueness of equilibrium is the direct results of this procedure. We obtain the conditions by suitable construction of Lyapunov functionals and estimation of derivates of the Lyapunov functionals by the well-known Young's inequality and Holder's inequality. The proposed conditions are related to p-norms of vector or matrix, p in [1, infinity] and thus unify and generalize some results in the literature.

Step to improve neural cryptography against flipping attacks.

Synchronization of neural networks by mutual learning has been demonstrated to be possible for constructing key exchange protocol over public channel. However, the neural cryptography schemes presented so far are not the securest under regular flipping attack (RFA) and are completely insecure under majority flipping attack (MFA). We propose a scheme by splitting the mutual information and the training process to improve the security of neural cryptosystem against flipping attacks. Both analytical and simulation results show that the success probability of RFA on the proposed scheme can be decreased to the level of brute force attack (BFA) and the success probability of MFA still decays exponentially with the weights' level L. The synchronization time of the parties also remains polynomial with L. Moreover, we analyze the security under an advanced flipping attack.