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Design of the Proportional-Integral-Derivative (PID) controller for an industrial process represents a challenge due to process complexity and non-linearity. Traditional methods such as Ziegler-Nichols (ZN) for PID controller tuning do not provide an optimal gain; thus, might leave the system with potential instability condition and cause significant losses and damages to the system. This paper investigates the merits of evolutionary and swarm-based optimization algorithms in fine-tuning the parameters of a PID controller. Here, Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) algorithm were utilized to optimize the PID controller for a DC motor system. Various fitness functions were provided for the presented algorithms to compute the performance of the controller. A new fitness function was proposed to achieve an outstanding control response for the DC motor system. Results demonstrate the efficacy of the proposed methods in improving closed loop system response.

SESSION TITLE: Clinical Prediction and Diagnosis of OSA

Robotic systems have been evolving since decades and touching almost all aspects of life, either for leisure or critical applications. Most of traditional robotic systems operate in well-defined environments utilizing pre-configured on-board processing units. However, modern and foreseen robotic applications ask for complex processing requirements that exceed the limits of on-board computing power. Cloud computing and the related technologies have high potential to overcome on-board hardware restrictions and can improve the performance efficiency. This research highlights the advancements in robotic systems with focus on cloud robotics as an emerging trend. There exists an extensive amount of effort to leverage the potentials of robotic systems and to handle arising shortcomings. Moreover, there are promising insights for future breed of intelligent, flexible, and autonomous robotic systems in the Internet of Things era.

This work proposes a new evolutionary multilayer perceptron neural networks using the recently proposed Bird Swarm Algorithm. The problem of finding the optimal connection weights and neuron biases is first formulated as a minimization problem with mean square error as the objective function. The BSA is then used to estimate the global optimum for this problem. A comprehensive comparative study is conducted using 13 classification datasets, three function approximation datasets, and one real-world case study (Tennessee Eastman chemical reactor problem) to benchmark the performance of the proposed evolutionary neural network. The results are compared with well-regarded conventional and evolutionary trainers and show that the proposed method provides very competitive results. The paper also considers a deep analysis of the results, revealing the flexibility, robustness, and reliability of the proposed trainer when applied to different datasets.

SESSION TITLE: Clinical Prediction and Diagnosis of OSA

AbstractThe autonomous navigation of robots in unknown environments is a challenge since it needs the integration of a several subsystems to implement different functionality. It needs drawing a map of the environment, robot map localization, motion planning or path following, implementing the path in real-world, and many others; all have to be implemented simultaneously. Thus, the development of autonomous robot navigation (ARN) problem is essential for the growth of the robotics field of research. In this paper, we present a simulation of a swarm intelligence method is known as Particle Swarm Optimization (PSO) to develop an ARN system that can navigate in an unknown environment, reaching a pre-defined goal and become collision-free. The proposed system is built such that each subsystem manipulates a specific task which integrated to achieve the robot mission. PSO is used to optimize the robot path by providing several waypoints that minimize the robot traveling distance. The Gazebo simulator was used to test the response of the system under various envirvector representing a solution to the optimization problem.onmental conditions. The proposed ARN system maintained robust navigation and avoided the obstacles in different unknown environments. vector representing a solution to the optimization problem.

SESSION TITLE: Clinical Prediction and Diagnosis of OSA

Greater informational masking is observed when the target and masker speech are more perceptually similar. Fundamental frequency (f0) contour, or the dynamic movement of f0, is thought to provide cues for segregating target speech presented in a speech masker. Most of the data demonstrating this effect have been collected using digitally modified stimuli. Less work has been done exploring the role of f0 contour for speech-in-speech recognition when all of the stimuli have been produced naturally. The goal of this project was to explore the importance of target and masker f0 contour similarity by manipulating the speaking style of talkers producing the target and masker speech streams. Sentence recognition thresholds were evaluated for target and masker speech that was produced with either flat, normal, or exaggerated speaking styles; performance was also measured in speech spectrum shaped noise and for conditions in which the stimuli were processed through an ideal-binary mask. Results confirmed that similarities in f0 contour depth elevated speech-in-speech recognition thresholds; however, when the target and masker had similar contour depths, targets with normal f0 contours were more resistant to masking than targets with flat or exaggerated contours. Differences in energetic masking across stimuli cannot account for these results.

Articles in this issue examine (1) the primary sources of variability in reading and language achievement among Spanish-speaking English learners (ELs) in the United States, (2) the extent to which poor performance at the end of grade 2 is identifiable in developmental trajectories beginning in kindergarten, (3) the relations among core reading constructs of phonological awareness and decoding in both English and Spanish and the factors that affect their relationship, (4) the performance of different approaches to identification and the factors that influence how well they work, as well as (5) the growing literature focused on intervention for reading problems in this population. This article examines the literature on language minority students and disability identification and analyzes a large-scale longitudinal dataset (>4,000 ELs; >15,000 observations) to systematically characterize and describe the oral language and reading development of Spanish-speaking children designated as ELs from kindergarten to second grade, considering a range of factors that may potentially contribute to that characterization and its relation to academic performance. This systematic characterization should facilitate the development of an empirical basis for a theoretically grounded framework of typical development in ELs in order to more precisely identify those children with language and learning disabilities.

This study investigated early indicators of Spanish-speaking English learners (ELs) at risk for reading difficulties at the end of Grade 2 by examining their early bilingual oral language development, taking into account language of academic instruction. Standardized measures of reading and narrative samples were collected in English and Spanish from kindergarten to Grade 2 from 1,243 ELs primarily instructed in English or Spanish. Conditional growth curve models yielded four primary findings of reading and oral language development. First, ELs with low reading achievement at the end of Grade 2 demonstrated early reading difficulties during kindergarten. Second, although ELs demonstrated overall higher reading achievement in their instructed language, this difference decreased over time. Third, ELs with low reading achievement at the end of Grade 2 demonstrated lower oral language skills in each language over time. Fourth, ELs demonstrated overall higher oral language skills in their instructed language, yet these differences varied over time. The study provided a detailed description of the longitudinal relations among the bilingual reading and oral language skills of Spanish-speaking ELs during the early school years. These findings help to inform the processes of early identification and intervention for Spanish-speaking ELs who are likely to demonstrate reading achievement difficulties.

Organoboron chemistry began more than 150 years ago when the synthesis and reactivity of triethylborane was described. Organoboron chemistry research has rapidly increased within the past few decades due to the increased usage of organoboron compounds in organic synthesis and biomedical applications. This chapter gives an overview of the general physical and chemical characteristics of select organoboron compounds. Only compounds incorporating a boron–carbon bond are discussed in detail.

This chapter presents a case study from an American university’s partnership with a Polish university. The case involves an assignment to international students studying for their master’s degrees: translating “key concepts in intercultural dialogue” for publication on the Center for Intercultural Dialogue’s website. There were several problems of translation of the task assignment. One problem is the translation of expectations. Learning outcomes occur for professors, too, and the chapter offers reflection on what lessons were realized for future teaching in the international classroom.