Test case prioritization(TCP) technique is an efficient approach to improve regression testing activities. With the continuous improvement of industrial testing requirements, traditional single-objective TCP is limited greatly, and multi-objective test case prioritization(MOTCP) technique becomes one of the hot topics in the field of software testing in recent years. Considering the problems of traditional genetic algorithm(GA) and swarm intelligence algorithm in solving MOTCP problems, such as falling into local optimum quickly and weak stability of the algorithm, a MOTCP algorithm based on multi-population cooperative particle swarm optimization(MPPSO) was proposed in this paper. Empirical studies were conducted to study the influence of iteration times on the proposed MOTCP algorithm, and compare the performances of MOTCP based on single-population particle swarm optimization(PSO) and MOTCP based on non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ) with the MOTCP algorithm proposed in this paper. The results of experiments show that the TCP algorithm based on MPPSO has stronger global optimization ability, is not easy to fall into local optimum, and can solve the MOTCP problem better than TCP algorithm based on the single-population PSO and NSGA-Ⅱ.
A supportive mobile robot for assisting the elderly is an emerging requirement mainly in countries like Japan where population ageing become relevant in near future.Falls related injuries are considered as a critical issue when taking into account the physical health of older people.A personal assistive robot with the capability of picking up and carrying objects for long/short distances can be used to overcome or lessen this problem.Here,we design and introduce a 3 D dynamic simulation of such an assistive robot to perform pick and place of objects through visual recognition.The robot consists of two major components;a robotic arm or manipulator to do the pick and place,and an omnidirectional wheeled robotic platform to support mobility.Both components are designed and operated according to their kinematics and dynamics and the controllers are integrated for the combined performance.The objective was to improve the accuracy of the robot at a considerably high speed.Designed mobile manipulator has been successfully tested and simulated with a stereo vision system to perform object recognition and tracking in a virtual environment resembling aroom of an elderly care.The tracking accuracy of the mobile manipulator at an average speed of 0.5 m/s is 90%and is well suited for the proposed application.
Temporal information is pervasive and crucial in medical records and other clinical text,as it formulates the development process of medical conditions and is vital for clinical decision making.However,providing a holistic knowledge representation and reasoning framework for various time expressions in the clinical text is challenging.In order to capture complex temporal semantics in clinical text,we propose a novel Clinical Time Ontology(CTO)as an extension from OWL framework.More specifically,we identified eight timerelated problems in clinical text and created 11 core temporal classes to conceptualize the fuzzy time,cyclic time,irregular time,negations and other complex aspects of clinical time.Then,we extended Allen’s and TEO’s temporal relations and defined the relation concept description between complex and simple time.Simultaneously,we provided a formulaic and graphical presentation of complex time and complex time relationships.We carried out empirical study on the expressiveness and usability of CTO using real-world healthcare datasets.Finally,experiment results demonstrate that CTO could faithfully represent and reason over 93%of the temporal expressions,and it can cover a wider range of time-related classes in clinical domain.
