Heuristic Health Resource Referral (H2R2) Engine

Searching for health resources is difficult for many individuals because it requires domain knowledge and understanding of search engines techniques. Our system proposes a paradigm shift whereby users provide as much or as little information as they feel comfortable, and we endeavor to match them with relevant health resources. The system first attempts to identify risk factors through a heuristic engine that employs fuzzy logic and then searches for health resources based on the user’s profile. We aim to unburden the user from having to understand complex health information and sometimes esoteric search techniques. Our preliminary findings show that a fuzzy-based rule engine has utility for determining alcohol suggested care and finding health resources for both alcohol and cigarette dependencies

A functional limit theorem for lattice oscillating random walk

The paper is devoted to an invariance principle for Kemperman's model of oscillating random walk on $\mathbb{Z}$. This result appears as an extension of the invariance principal theorem for classical random walks on $\mathbb{Z}$ or reflected random walks on $\mathbb{N}_0$. Relying on some natural Markov sub-process which takes into account the oscillation of the random walks between $\mathbb{Z}^-$ and $\mathbb{Z}^+$, we first construct an aperiodic sequence of renewal operators acting on a suitable Banach space and then apply a powerful theorem proved by S. Gou\"ezel

Development of Multi-Robotic Arm System for Sorting System Using Computer Vision

This paper develops a multi-robotic arm system and a stereo vision system to sort objects in the right position according to size and shape attributes. The robotic arm system consists of one master and three slave robots associated with three conveyor belts. Each robotic arm is controlled by a robot controller based on a microcontroller. A master controller is used for the vision system and communicating with slave robotic arms using the Modbus RTU protocol through an RS485 serial interface. The stereo vision system is built to determine the 3D coordinates of the object. Instead of rebuilding the entire disparity map, which is computationally expensive, the centroids of the objects in the two images are calculated to determine the depth value. After that, we can calculate the 3D coordinates of the object by using the formula of the pinhole camera model. Objects are picked up and placed on a conveyor branch according to their shape. The conveyor transports the object to the location of the slave robot. Based on the size attribute that the slave robot receives from the master, the object is picked and placed in the right position. Experiment results reveal the effectiveness of the system. The system can be used in industrial processes to reduce the required time and improve the performance of the production line

Design and development of a delta robot system to classify objects using image processing

In this paper, a delta robot is designed to grasp objects in an automatic sorting system. The system consists of a delta robot arm for grasping objects, a belt conveyor for transmitting objects, a camera mounted above the conveyor to capture images of objects, and a computer for processing images to classify objects. The delta robot is driven by three direct current (DC) servo motors. The controller is implemented by an Arduino board and Raspberry Pi 4 computer. The Arduino is programmed to provide rotation to each corresponding motor. The Raspberry Pi 4 computer is used to process images of objects to classify objects according to their color. An image processing algorithm is developed to classify objects by color. The blue, green, red (BGR) image of objects is converted to HSV color space and then different thresholds are applied to recognize the object’s color. The robot grasps objects and put them in the correct position according to information received from Raspberry. Experimental results show that the accuracy when classifying red and yellow objects is 100%, and for green objects is 97.5%. The system takes an average of 1.8 s to sort an object

Relationships of language learning variables in the acquisition of third languages in a multilingual context

This paper reinforces the importance of third language acquisition research in a multilingual context focusing on language experience, vocabulary learning strategies, emotional self-regulation strategies, and language anxiety. This study explored three languages: Chinese, Korean, and Vietnamese, to know if there are significant relationships between the above variables in third language acquisition. Three specific sets of survey questionnaires were distributed to various students enrolled in foreign language courses offered in a language department in a university. To focus on the relationships of language learning variables of a foreign language, correlational design was used to analyze the survey questionnaires' responses for each language course. Among students who were enrolled in Chinese, significant relationships were established between language anxiety and language experience, language anxiety, and emotional self-regulation strategies. Significant associations were found between emotional self-regulation strategies and language experience, emotional self-regulation strategies, and vocabulary strategies, and language performance and language experience for those students enrolled in the Korean language. In learning Vietnamese, significant relationships were found between language anxiety and vocabulary strategies, language performance, and language anxiety. Teachers may need to re-evaluate prepared teaching and learning materials, for example, material difficulty, to help students alleviate anxiety in learning

VIETNAMESE ENGLISH-MAJORED STUDENTS’ USE OF LISTENING STRATEGIES

The crucial role of listening skill in language learning has been well acknowledged, yet attention to this skill remains modest. Numerous studies investigating learners’ listening performance have identified listening strategies as a key factor contributing to the success of effective listeners. This study, using a Likert-scale questionnaire, examined the listening strategies employed by 81 Vietnamese English-majored students, who were divided into two groups - effective and less effective listeners based on an IELTS proficiency test. Findings showed that listening strategies were used at a relatively high level with the metacognitive group employed most frequently compared to cognitive and socio-affective strategies. Lowering anxiety, predicting and planning, resourcing, repetition, and cooperation were found most commonly employed individual strategies. Although no significant differences were found between the groups’ use of the three overarching strategy categories, several discrepancies were identified concerning their use of individual strategies, which provides important implications for listening pedagogical adjustments in this particular context. Article visualizations

Path Following and Avoiding Obstacle for Mobile Robot Under Dynamic Environments Using Reinforcement Learning

Obstacle avoidance for mobile robot to reach the desired target from a start location is one of the most interesting research topics. However, until now, few works discuss about working of mobile robot in the dynamic and continuously changing environment. So, this issue is still the research challenge for mobile robots. Traditional algorithm for obstacle avoidance in the dynamic, complex environment had many drawbacks. As known that Q-learning, the type of reinforcement learning, has been successfully applied in computer games. However, it is still rarely used in real world applications. This research presents an effectively method for real time dynamic obstacle avoidance based on Q-learning in the real world by using three-wheeled mobile robot. The position of obstacles including many static and dynamic obstacles and the mobile robot are recognized by fixed camera installed above the working space. The input for the robot is the 2D data from the camera. The output is an action for the robot (velocities, linear and angular parameters). Firstly, the simulation is performed for Q-learning algorithm then based on trained data, The Q-table value is implemented to the real mobile robot to perform the task in the real scene. The results are compared with intelligent control method for both static and dynamic obstacles cases. Through implement experiments, the results show that, after training in dynamic environments and testing in a new environment, the mobile robot is able to reach the target position successfully and have better performance comparing with fuzzy controller

Support vector machine-based object classification for robot arm system

In this paper, a support vector machine (SVM) model is trained to classify objects in the automatic sorting system using a robot arm. The robot arm is used to grab objects and move them to the right position according to their shape predicted by the SVM model. The position of objects in the image is identified by using the contouring technique. The centroid of objects is calculated from the image moment of the object's contour. The calibration is conducted to get the parameters of the camera and combine with the pinhole camera model to compute the 3D position of the objects. The feature vector for SVM training is the zone feature and the SVM kernel is the Gaussian kernel. In the experiment, the SVM model is used to classify four objects with different shapes. The results show that the accuracy of the SVM classifier is 99.72%, 99.4%, 99.4% and 99.88% for four objects, respectively