Chen Yp

Bio: Chen Yp is an academic researcher from National Cheng Kung University. The author has contributed to research in topics: Robot kinematics & Robot. The author has an hindex of 2, co-authored 4 publications receiving 11 citations.

Reciprocal Learning for Robot Peers

Abstract: This paper proposes a robot peer reciprocal learning system in which robot peers can not only cooperatively accomplish a difficult task but also help each other to learn better. In this system, each robot is an independent individual and has the ability to make individual decisions. They can communicate about image information, individual decisions, and current state to formulate mutual decisions and motions. For learning a new concept, we propose a mutual learning method, which allows the robots to learn from each other by exchanging weights in their neural network concept learning system. The simulation results show that the robots can learn from each other to build general concepts from limited training, while improving both of their performances at the same time. Finally, we design two cooperative tasks, which require the robots to formulate mutual sequential motions and keep communicating to manage their motions. The robotic experiments demonstrate that the proposed robot peer reciprocal learning system can help robots achieve difficult tasks in appropriate and cooperative ways, just as humans do.

A 3D vision based object grasping posture learning system for home service robots

Abstract: This paper proposes a 3D vision based object grasping posture learning system. In this system, the robot recognizes the orientation of the object to decide the grasping posture, whereas selects a feasible grasping point by detecting the surrounding. When the planned posture is not good enough, the proposed learning system adjusts the position of the end effector real time. The learning system is inspired by a book entitled, Thinking, Fast and Slow, and consists of two subsystems. The subsystem I judges whether the pose of the object is learned before, and plans a grasping posture by past experience. When the pose of the object is not learned before, the subsystem II learns a position adjustment by the real time information form the motor angels and the images. Finally, the method proposed in this paper is applied to the home service robot and is proven the feasibility by the experimental results.

Cloud-Based Multi-Agent Cooperation for IoT Devices Using Workflow-Nets

Abstract: Most Internet of Things (IoT)-based service requests require excessive computation which exceeds an IoT device’s capabilities. Cloud-based solutions were introduced to outsource most of the computation to the data center. The integration of multi-agent IoT systems with cloud computing technology makes it possible to provide faster, more efficient and real-time solutions. Multi-agent cooperation for distributed systems such as fog-based cloud computing has gained popularity in contemporary research areas such as service composition and IoT robotic systems. Enhanced cloud computing performance gains and fog site load distribution are direct achievements of such cooperation. In this article, we propose a workflow-net based framework for agent cooperation to enable collaboration among fog computing devices and form a cooperative IoT service delivery system. A cooperation operator is used to find the topology and structure of the resulting cooperative set of fog computing agents. The operator shifts the problem defined as a set of workflow-nets into algebraic representations to provide a mechanism for solving the optimization problem mathematically. IoT device resource and collaboration capabilities are properties which are considered in the selection process of the cooperating IoT agents from different fog computing sites. Experimental results in the form of simulation and implementation show that the cooperation process increases the number of achieved tasks and is performed in a timely manner.

Paraneoplastic syndromes of gynecologic neoplasms.

Abstract: PURPOSE AND DESIGNThe purpose of this review is to define and describe the paraneoplastic syndromes associated with gynecologic neoplasms. A comprehensive search of MEDLINE from 1966 to January 1996 and Cancerlit was performed. One hundred twenty-two reports were reviewed.RESULTSTwenty-four paraneoplastic syndromes have been associated with gynecologic malignancies. Six anatomic systems are affected by these syndromes. However, except for disseminated intravascular coagulation and hypercalcemia, these syndromes are rare.CONCLUSIONParaneoplastic syndromes are not frequently associated with gynecologic malignancies. The diagnosis of these syndromes is essential, as they can be occasionally life-threatening. Some paraneoplastic syndromes can be used as marker of progression or regression of the underlying malignancy.