Robot-object contact perception using symbolic temporal pattern learning

This paper investigates application of machine learning to the problem of contact perception between a robots gripper and an object. The input data comprises a multidimensional time-series produced by a force/torque sensor at the robots wrist, the robots proprioceptive information, namely, the position of the end-effector, as well as the robots control command. These data are used to train a hidden Markov model (HMM) classifier. The output of the classifier is a prediction of the contact state, which includes no contact, a contact aligned with the central axis of the valve, and an edge contact. To distinguish between contact states, the robot performs exploratory behaviors that produce distinct patterns in the time-series data. The patterns are discovered by first analyzing the data using a probabilistic clustering algorithm that transforms the multidimensional data into a one-dimensional sequence of symbols. The symbols produced by the clustering algorithm are used to train the HMM classifier. We examined two exploratory behaviors: a rotation around the x-axis, and a rotation around the y-axis of the gripper. We show that using these two exploratory behaviors we can successfully predict a contact state with an accuracy of 88 ± 5 % and 81 ± 10 %, respectively

Controlled Tactile Exploration and Haptic Object Recognition

In this paper we propose a novel method for in-hand object recognition. The method is composed of a grasp stabilization controller and two exploratory behaviours to capture the shape and the softness of an object. Grasp stabilization plays an important role in recognizing objects. First, it prevents the object from slipping and facilitates the exploration of the object. Second, reaching a stable and repeatable position adds robustness to the learning algorithm and increases invariance with respect to the way in which the robot grasps the object. The stable poses are estimated using a Gaussian mixture model (GMM). We present experimental results showing that using our method the classifier can successfully distinguish 30 objects.We also compare our method with a benchmark experiment, in which the grasp stabilization is disabled. We show, with statistical significance, that our method outperforms the benchmark method

Factors Affecting Minor Psychiatric Disorder in Southern Iranian Nurses: A Latent Class Regression Analysis

Background: Mental health is one of the most important dimensions of life and its quality. Minor Psychiatric Disorder as a type of mental health problem is prevalent among health workers. Nursing is considered to be one of the most stressful occupations. Objectives: This study aimed to evaluate the prevalence of minor psychiatric disorder and its associated factors among nurses in southern Iran. Patients and Methods: A cross-sectional study was carried out on 771 nurses working in 20 cities of Bushehr and Fars provinces in southern Iran. Participants were recruited through multi-stage sampling during 2014. The General Health Questionnaire (GHQ-12) was used for screening of minor psychiatric disorder in nurses. Latent Class Regression was used to analyze the data. Results: The prevalence of minor psychiatric disorder among nurses was estimated to be 27.5%. Gender and sleep disorders were significant factors in determining the level of minor psychiatric disorder (P Values of 0.04 and < 0.001, respectively). Female nurses were 20% more likely than males to be classified into the minor psychiatric disorder group. Conclusions: The results of this study provide information about the prevalence of minor psychiatric disorder among nurses, and factors, which affect the prevalence of such disorders. These findings can be used in strategic planning processes to improve nurses’ mental health

Intelligent Traffic Management and Load Balance Based on Spike ISDN-IoT

Ministry of Higher Education and Scientific Research, University of Baghdad, Ira

Marine health of the Arabian Gulf: Drivers of pollution and assessment approaches focusing on desalination activities

The Arabian Gulf is one of the most adversely affected marine environments worldwide, which results from combined pollution drivers including climate change, oil and gas activities, and coastal anthropogenic disturbances. Desalination activities are one of the major marine pollution drivers regionally and internationally. Arabian Gulf countries represent a hotspot of desalination activities as they are responsible for nearly 50% of the global desalination capacity. Building desalination plants, up-taking seawater, and discharging untreated brine back into the sea adversely affects the biodiversity of the marine ecosystems. The present review attempted to reveal the potential negative effects of desalination plants on the Gulf's marine environments. We emphasised different conventional and innovative assessment tools used to assess the health of marine environments and evaluate the damage exerted by desalination activity in the Gulf. Finally, we suggested effective management approaches to tackle the issue including the significance of national regulations and regional cooperation

A log analysis study of 10 years of ebook consumption in academic library collections

Even though libraries have been offering eBooks for more than a decade, very little is known about eBook access and consumption in academic library collections. This paper addresses this gap with a log analysis study of eBook access at the library of the University of Waikato. This in-depth analysis covers a period spanning 10 years of eBook use at this university. We draw conclusions about the use of eBooks at this institution and compare the results with other published studies of eBook usage at tertiary institutes