http://dspace.cityu.edu.hk:80/handle/2031/751 Wed, 01 May 2013 03:06:36 GMT 2013-05-01T03:06:36Z http://dspace.cityu.edu.hk:80/handle/2031/6652 Title: Force characterization and motion planning in automated cell manipulation by optical tweezers Authors: Wu, Yanhua ( 吳燕華) Abstract: Optical tweezers, which are based on the transfer of photon momentum, can trap and move microscale and nanoscale particles without physical contact. Rapid and precise transportation of live cells can benefit cell microsurgery, rare cell isolation, tissue engineering and cell-to-cell interactions. Increasing demands for both accuracy and efficiency in biological cell manipulation highlight the need for automation with robotics technology. Understanding the forces exerted on live cells is essential to biomechanical characterization and cell manipulation. However, traditional numerical force measurement assumes live cells to be ideal objects, ignoring their complicated inner structures and rough membranes. Furthermore, little reported research has specifically considered the synergy of dynamic analysis in motion planning during automated transportation. The problem of planning cell motion with optimized motion parameters, using cell dynamics analysis, is still very challenging. This thesis aims to characterize the mechanical forces applied to live cells in optical traps, and use the mechanical parameters thus obtained to plan cell motion during automated transportation. The research principally consists of the following three elements. First, the forces applied to live cells are calibrated by a novel static viscous-drag-force method. Unlike existing approaches, the proposed method does not assume the live cells to share the same optical and/or drag parameters as those of polystyrene/silica beads. By binding a micro polystyrene sphere to the live cell and moving the mixture with optical tweezers, the drag force on the cell can be obtained by subtracting the drag force on the sphere from the total drag force on the mixture, under the condition of an extremely low Reynolds number. The trapping force on the cell is then obtained from the drag force when the cell is in the force equilibrium state. Second, motion planning strategy, which is designed using dynamics analysis of the optically trapped cell, is used to determine the ideal movement velocity of the cell. Due to property changes in the aqueous medium and laser during cell transportation, the calibrated dynamic parameters may vary, and thus, the cell movement velocity designed using these parameters should be adjusted online. A proportional-integral (PI) scheme is used to adjust the cell movement velocity online, to ensure that the cell moves at an ideal speed and does not escape from the laser focus. Dynamics analysis results are used to design the PI scheme. Third, an optimal path for cell movement is planned, using a modified A-star algorithm, which introduces an additional cost to penalize waypoints where the direction of movement changes. The algorithm balances smoothness and movement cost. Finally, experiments on moving yeast cells are conducted to demonstrate the effectiveness of the proposed approach. The main contribution of this study lies in the development of a new experimental method to characterize the mechanical forces exerted on live cells, and the application of the dynamic analysis results to motion planning for automated cell transportation. Notes: CityU Call Number: TK8360.O69 W8 2011; ix, 95 leaves : ill. 30 cm.; Thesis (Ph.D.)--City University of Hong Kong, 2011.; Includes bibliographical references (leaves 84-94) Sat, 01 Jan 2011 00:00:00 GMT http://dspace.cityu.edu.hk:80/handle/2031/6652 2011-01-01T00:00:00Z http://dspace.cityu.edu.hk:80/handle/2031/6651 Title: Quantitative characterization of defect in ultrasonic guided waves-based pipeline inspection Authors: Wang, Xiaojuan ( 王曉娟) Abstract: Pipelines constitute crucial infrastructure in the oil, gas, chemical, and water transport industries. In-service pipelines are prone to a variety of defects that stem from the deleterious effects of fatigue, aging, external impacts, and corrosion from hazardous operating environments. There is thus an urgent need for inspection techniques that can detect pipeline defects and characterize those defects to allow maintenance and replacement operations to be carried out accurately and efficiently. The ultrasonic guided-wave technique is an active inspection approach that has undergone recent evolution and demonstrated great potential for the nondestructive testing of materials and structures in a variety of fields. However, defect characterization in guided wave-based pipeline inspection remains extremely challenging because of the complexities involved in the interaction between guided waves and pipeline defects, and the resulting reflection signal that conveys multidimensional geometrical information in those defects. In this work, the reflection-related problems of the guided waves from pipeline defects are first investigated through extensive laboratory experiments and numerical simulations in parallel with specific concerns about the reflections at the defect edges. The results of this investigation show the reflection from a defect to be the joint result of the interference between the reflections at its front and back edges. The edge reflection components embedded in the defect reflection signal exhibit different features, thereby further increasing the complexity of the total defect reflection signal. The relationships between each edge reflection and the three-dimensional geometrical parameters of the defect, including its axial length and circumferential extent and radial depth, are identified. The results indicate that the pattern of the reflection waveform from a pipeline defect is affected primarily by the defect's axial length, whereas the reflection amplitude is strongly determined by the geometrical parameters of its edges. The findings presented herein provide new perspectives and offer useful guidance on the interpretation of the reflection signals in guided wave based pipeline defect inspection. Based on this foundation, a new strategy for characterizing pipeline defects is formulated. The framework of this strategy requires that the edge reflection components first be decomposed from the defect reflection signal and then analyzed to enable the quantitative characterization of defect. The applicability of the new characterization strategy to arbitrary pipeline defects is explored semi-analytically using transmission-line model technique. As the primary components of defect reflection signal, the edge reflection signals are generated from partial reflection defect edge with respect to the entire edge, which is termed as effective edge and closely related to the critical geometrical features of the defect. The characteristic dimension of pipeline defect is further employed to describe the results obtained under the framework of the new strategy. This strategy reduces the complexity of analyzing the defect reflection problem because it removes the destructive effects of the interference between the reflection components. Moreover, it provides additional defect geometry-related information sources, thereby enhancing the reliability and accuracy of quantitative defect characterization. In implementing edge reflection-based characterization strategy, the problems of signal decomposition and analysis remain. Two methods of decomposing the defect reflection signal into edge reflection components are developed in this thesis. These methods permit a determination of the characteristic axial length of the defect, which corresponds to the relative distance between two decomposed edge reflections. The guided wave reflection at the edge of defect is also investigated under different defect cases, with the results showing that the extent of the F(1,3) mode generated during the edge reflection due to mode conversion is comparable to that of the incident L(0,2) mode, which can provide circumferential information of the defect. The relationship between edge reflection and the geometrical parameters of defect edge is further constructed using the least squares support vector machine method, the results of which can be used in conjunction with the circumference extent to determine the radial depth. Various types of defects, including simulated notches, artificial notches, and real pipe corrosion, are considered to demonstrate the capabilities of the methods developed herein. Notes: CityU Call Number: TJ930 .W36 2011; xiv, 172 leaves : ill. 30 cm.; Thesis (Ph.D.)--City University of Hong Kong, 2011.; Includes bibliographical references (leaves 166-172) Sat, 01 Jan 2011 00:00:00 GMT http://dspace.cityu.edu.hk:80/handle/2031/6651 2011-01-01T00:00:00Z http://dspace.cityu.edu.hk:80/handle/2031/6650 Title: Remote machine condition monitoring and fault diagnosis systems through the Internet and mobile communication Authors: Wang, Wanbin ( 王万賓) Abstract: Machine condition monitoring is important for ensuring the efficiency of machines in a factory and the safety of workers. A variety of vibration-based analysis techniques have been long used to diagnose the health status of machines. Since the emergence of the Internet, remote machine monitoring systems operating through the Internet have attracted much scholarly attention. Although mobile communications are convenient for everyday uses, and can also be used in some industrial applications, researchers have not yet seriously considered remote machine condition monitoring systems based on the mobile communication. In this dissertation, some novel research on remote machine condition monitoring and fault diagnosis systems which operate through the Internet and mobile communication are presented. This research is based on emerging computer technologies, such as JSP (JavaServer Pages), XML (Extensible Markup Language) and J2ME (Java 2 Platform, Micro Edition). Three novel architectures were designed and their prototypes were implemented so that the condition monitoring of machine status became achievable on the Internet as well as on mobile phones/PDAs. In order to allow maintenance staff to obtain the machine health status generated by an existing on-site machine monitoring system remotely, especially through their mobile phones, the first architecture was presented and a prototype was built based on SAMS (Smart Asset Maintenance System). SAMS is an on-site machine maintenance system developed by the Smart Engineering Asset Management Laboratory of the City University of Hong Kong. This prototype allows users to check the health status (presented in the form of data, image and video) of operating machines through a mobile phone or a computer. In addition, when the machine status is abnormal, an automatic alarm trigger module can actively send alert messages to designated users' mobile phones and call these phones to make sure that the concerned maintenance staff be aware to read the alerting messages. The language XML is used to encode machine data and diagnostic results. Then this prototype can provide different machine health information according to the requests of the users. The second architecture is based on dynamic Web technologies. This architecture allows authorized users to remotely control the parameters for sensory data acquisition and analysis programs running in a server located at a factory. Therefore, the remotely located user has the freedom to select the preferred parameters for the data acquisition as well as the algorithms for fault diagnosis. To extend the capabilities of this architecture so that it is applicable to mobile communications, the client software running in a mobile phone/PDA was built. A remotely located user can then inspect the health status of an inspected machine through a computer connected by board band to the Internet, or a mobile phone/PDA. Based on this architecture, a prototype was built. A LabVIEW program running in the server is used to analyze the collected machine operating data. Through a computer or a mobile terminal, a remotely located user can set up his desired parameters for sensory data acquisition/analysis programs and obtain results generated according to his preferred algorithms and parameters. In order to increase the functionality of a Smartphone/PDA for remote machine condition monitoring, instead of requesting the server to run the selected algorithm for fault diagnosis, the third architecture was designed within which the diagnostic algorithms can be executed in a Smartphone/PDA. Therefore, the remote user can perform his desired fault diagnosis algorithm any time on his Smartphone/PDA. A prototype was designed to host Fast Fourier Transform (FFT) on the Smartphone/PDA. This FFT algorithm coded by J2ME is used to analyze the collected machine data and then generate the diagnostic results directly on the user's Smartphone/PDA. The ability to perform machine fault diagnosis on a Smartphone/PDA is novel and has tremendous potential for use in future applications as new hardware and software technologies are developed for Smartphones/PDAs. Besides, in order to reduce the number of data files required to be transferred to the mobile terminals in the third architecture, an AI based method is proposed to help select a monitoring sensory point. Through this method, an expert, who views the FFT spectrum through his Smartphones/PDAs and then assesses the status of the machine, can get a machine data file from different monitoring sensory points. Compared with other data files, this data file has the worst similarity with the data files collected when machine is running normally. The new method is implemented based on the concept of k-Nearest Neighbor (kNN). It only requires prior knowledge on the tested machine when it is running at normal condition. First, the temporal signals collected from the machine at different time in normal running conditions are converted to their respective frequency spectra. Second, the distance between two FFT based spectra is defined to describe the similarity of two machine data files. Third, the similarities of given new samples to data files collected from a machine running at normal condition are used to select a data file which will be transmitted to the designated Smartphones/PDAs. The simulation has shown the effectiveness of this method. In summary, the first architecture is based on an existing on-site machine maintenance system and is an economic means for obtaining a machine's health status information through the Internet and mobile communication. The second architecture allows users to control server-side machine monitoring software, and actively analyze a machine's status through a remotely located computer or mobile phone/PDA. The third architecture allows an authorized user to perform machine fault diagnosis directly on his Smartphone or PDA. Notes: CityU Call Number: TJ213 .W3395 2011; 239 leaves : ill. 30 cm.; Thesis (Ph.D.)--City University of Hong Kong, 2011.; Includes bibliographical references (leaves 173-190) Sat, 01 Jan 2011 00:00:00 GMT http://dspace.cityu.edu.hk:80/handle/2031/6650 2011-01-01T00:00:00Z http://dspace.cityu.edu.hk:80/handle/2031/6646 Title: The impact of entrepreneurship education on entrepreneurial intention of engineering students Authors: Lo, Choi Tung ( 盧彩彤) Abstract: Entrepreneurship education has become very popular nowadays both in management schools and engineering schools. However, the impact of entrepreneurship education on entrepreneurial intention of engineering students remains in question. What is the value of entrepreneurship education? What should be taught and how to teach the subject? In order to develop guidelines for entrepreneurship education for engineering students, this thesis aims to propose an entrepreneurship education model by empirically investigating how specific education components influence the entrepreneurial intention of engineering students. To achieve the aim, four objectives need to be addressed. The first one is to identify a theoretical approach and develop a conceptual model for studying the impact of entrepreneurship education on entrepreneurial intention of engineering students. The second one is to test the effectiveness of entrepreneurship education in terms of entrepreneurial intention. The third one is to empirically test the influence of education components on entrepreneurial intention. Finally, the fourth one is to develop an entrepreneurship education model and provide guidelines for entrepreneurship education. An extensive review on entrepreneurship and education was conducted in order to achieve the first objective. The theory of planned behavior (TPB) was found appropriate to be the theoretical basis of entrepreneurship education because it provides most information about the formation process of entrepreneurial intention at both personal and social level. Further, entrepreneurship is a planned behavior that a new business is seldom created suddenly without planning, and thus it is best predicted by entrepreneurial intention. The second objective was reached by a comparison study between entrepreneurship students and control group students. The third objective was achieved through testing the effect of specific education components on entrepreneurial intention. The fourth objective was achieved by exploring the results from the theoretical and practical perspectives. Based on the TPB and elaboration of entrepreneurship education into four components, a conceptual model linking entrepreneurship education and entrepreneurial intention was proposed. Ten sets of hypotheses were formulated in the conceptual model. A survey of 411 engineering students was conducted in order to test the model. Of the respondents, 201 took an entrepreneurship course (entrepreneurship group) and 210 did not take the entrepreneurship course (control group). There were two major data analyses in this thesis. First, the two groups of students were compared by t-test and ANOVA. The results show that there are significant differences in their entrepreneurial intentions confirming the effectiveness of entrepreneurship education on enhancing entrepreneurial intention. Second, the conceptual model was tested by SEM (structural equation modeling) path analysis in order to identify the specific relationship between entrepreneurship education components and entrepreneurial intention. Among others, three paths are tested to be significant. They are the paths 1) from know-why to attitude toward entrepreneurship, 2) from know-who to subjective norm (i.e., social influence), and 3) from know-how to perceived behavior control (i.e., self-efficacy or capability). Further, know-what is considered as the basic element which facilitates other components. The findings also reveal significant dependent relationships among the three antecedent attitudes of entrepreneurial intention. For example, subjective norm plays an important role in facilitating attitude toward entrepreneurship as well as perceived behavioral control. Perceived behavioral control can also improve one's attitude toward entrepreneurship. The model suggests the systematic impact of entrepreneurship education on entrepreneurial intention. Both theoretical and practical implications are explored from the results. Theoretically, this study identifies a robust approach to study the impact of entrepreneurship education on entrepreneurial intention. Further, it provides more detailed information on how entrepreneurial intention forms, considering the interrelationships among the antecedent attitudes. Moreover, this study provides significant implications for the teaching of entrepreneurship by suggesting an intention-focus approach. Practically, the findings offer useful guidelines for teachers to develop teaching strategies for entrepreneurship. The most salient feature of this study is that it bridges specific education components and entrepreneurial intention, providing significant insight into how the key components influence the entrepreneurial attitudes and intentions of students. It is probably the first study to fill the gap in the knowledge required for fostering entrepreneurial intention through entrepreneurship education. Further, this thesis employs SEM path analysis for modeling the students' entrepreneurial intentions. Fitness of the overall model (rather than the separated relationships in regression analysis) that path analysis concerns provides more reliable results on the influence of specific education components on entrepreneurial intention. Notes: CityU Call Number: HB615 .L6 2011; xi, 282 leaves 30 cm.; Thesis (Ph.D.)--City University of Hong Kong, 2011.; Includes bibliographical references (leaves 242-261) Sat, 01 Jan 2011 00:00:00 GMT http://dspace.cityu.edu.hk:80/handle/2031/6646 2011-01-01T00:00:00Z