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Entrepreneurial Learning through peer net work

Proposal on Entrepreneurial Learning Through Peer Network     Research Overview The research will be focusing on peer networking in the context of entrepreneurial learning and the need to plan, execute as well as realize research approaches in weighing in primary and secondary research and the need to integrate resources of ideas, theories and evidences regarding peer network and entrepreneurial learning, the assimilation of research objectives and questions is essential for creating and organizing literature studies upon which the level of awareness is objective driven and provide causal link to such implications for practice and other domains. The presenting of background, problem statement, literature review, significance and methodology does assume successful research process complete with knowledge formation and genuine information, as to be presented in a planned manner. Research objectives For the research objectives, the following points need to be considered in order to have basis of questions deemed for literature undertaking <li class="MsoNormal" style="margin-bottom: 7.5pt"> To be able to provide understanding and detailed information about entrepreneurial learning and peer networking </li> <li class="MsoNormal" style="margin-bottom: 7.5pt">  To be able to review as well as synthesize relevant computer science and economics literature that relates to peer networks, </li> <li class="MsoNormal" style="margin-bottom: 7.5pt"> To raise ideal questions for researchers who are interested in realizing good entrepreneurial behavior of peer networks </li> <li class="MsoNormal" style="margin-bottom: 7.5pt"> To recognize different entrepreneurship learning in order to understand peer networks </li> Research questions An emerging body of research seeks to integrate entrepreneurship  into the study of peer networks. Significant questions to be addressed for this research will have to be the following: 1.       What does entrepreneurial learning is all about? Discuss its aspects/process 2.       How does peer networking be achieved in the assimilation of learning? What are some of the examples of peer networking that points to entrepreneurial spirit and idealism? Explain 3.       Why does entrepreneurial learning assume good grounds for realizing effective opportunities in such business? How does it function in peer networking? Provide indications 4.       How does research links to conceptual framework of entrepreneurial learning? How peer networks are adopted to achieve stable business drive? Literature overview For literature overview, it can be that “entrepreneurial learning is often described as a continuous process that facilitates the development of necessary knowledge for being effective in starting up and managing new ventures thus, starting point for studying the process of entrepreneurial learning could hence be to draw a distinction between the experience of entrepreneur and knowledge being acquired” ( 1994). Another, literature on entrepreneurial learning is the entrepreneurs’ amount of management experience. For example,   (1998) showed that entrepreneurs’ years of work experience increased their venture survival rates and reduced the likelihood of fresh venture failure. Moreover, considering that entrepreneurs often face uncertainty about the value of the goods and services that they plan to produce, it seems fair to assume that also industry-specific experience can have a strong influence on their development of entrepreneurial knowledge (2003; 2000). This implies that peer networking groups with prior experience in business industry often have better understanding of how to meet demand conditions in that marketplace, as industry experience provides information that others cannot gather it. Aside, it can be argued that entrepreneurs’ experiences can include various peer networking events, even if these networks are connected to the start up of innovative business are being related to experiences of fresh business venture ( 1985;  1985), and provide entrepreneur with inputs to develop entrepreneurial learning channels.   Research significance   The research significance can assume advances in theoretical knowledge of entrepreneurial learning by reviewing and synthesizing available research in conceptual framework that explains the process of entrepreneurial learning through peer networks, as the research will be utmost significant to fresh graduates who have the entrepreneurial spirit to start a business in collaboration with his/her peers or colleagues as well as to those neophyte business entrepreneurs who have the potential to keep business on a high note.         Research Methodology   The main research method for this study is the application of descriptive research upon which research sampling utilization is to be based in Sri Lanka. There is ample need to describe and apply certain model/framework that identify valuable components linking to entrepreneurial learning, onto peer networking experience, the effectiveness in recognizing and acting on entrepreneurial opportunities of newness to business. The need to integrate case studies regarding entrepreneurial learning through peer networking selecting samples of entrepreneurship sense in the Sri Lanka context and sample research, upon extensive efforts in investigating learning effects of entrepreneurs’ experience on peer networking to distinguish concept assumptions (2001;  1999).   Implications for Practice The review and synthesis of the literature suggest that entrepreneurship is primarily learned by experience and discovery, and that entrepreneurial learning should be conceived as lifelong process, where knowledge is continuously shaped as networking experience does take place ( 2000). Hence, the attempts in stimulating entrepreneurial activities through formal training and education are essential. There calls for further research on entrepreneurial learning in peer network realization upon developing useful field of study within entrepreneurship network domain to be known in the research approach.           Credit:ivythesis.typepad.com

Entrepreneurial Learning through peer net work

Proposal on Entrepreneurial Learning Through Peer Network Research Overview The research will be focusing on peer networking in t...

Dissertation on Nonlinear Output Frequency Response Function and its application in fault detection

for                                                              (1.3)             In the equation above, denotes the  n th order output frequency response of the system. This expression reveals   how the non-linear mechanisms operate on the input signal to produce output response in frequency domain.                                                                                                                                              (1.4)   is the definition of the nth-order Generalized Frequency Response Function (GFRF)     (Billings and Yusof 1996, Boaghe  et al . 2000, 2002) , and       denotes the integration of          over the n-dimensional hyper plane    Equation (1.3) extends the well-known linear relationship (1.1)  to the non-linear case.   From equation (1.1), it shows that the possible output frequencies are exactly the same as the frequencies in the input for   linear system. For equation (1.2), the situation of non-linear system becomes more complicated than that in linear case,   that is, the relationship between input and output frequencies is generally given by                             (1.5)     Where represents the non-negative frequency ranges of the system output, and denotes the non-negative frequency   ranges produced by the  n th-order system non-linearity.    In 1997, Lang and Billings derived an explicit expression for the output frequency range    of non-linear systems when   subjected to a general input with a spectrum given by                                                when                      0          otherwise   where b>a The result obtained is                                                                                       (1.7)               means to take the integer part                for k=0,…….,            In (1.7) p* could be taken as 1,2,…,   , the specific value of which depends on the system nonlinearities. If the     system GFRFs     for i=1,…,q-1, and   , then p*=q. This is the first analytical description   for the output  frequencies of non-linear systems, which is an extension of the relationship between the input and output   frequencies of linear systems in equation (1.1) to the non-linear case.     The theory above shows the basic properties of the relationship between the input and output frequencies. However, more   details about the relocation of the frequency domain response taking place of a non-linear system can not be acquired.   Therefore, a new frequency domain analyse method of non-linear system which is known as   NOFRFs is introduced in the next section.            Charpter 2   Non-linear output frequency response functions (NOFRFs)   2.1  Some basic concept   First consider two different systems in time domain Static linear system      y(t) =ku(t)   Static non-linear system   y (t)=ku(t)   The frequency domain representations of the systems above are               Y(jw)= kU(jw)   and        Y(jw)= kU(jw)                    respectively.  U(jw)  represents the Fourier transform of u(t)  and it can be written   as                                            (2.1)     The equation (2.1) should be taken as an expression of a second order non-linear system.   In this case, a more generalized expression can be deduced                      Equation (2.2) is the Fourier transform of the system input u(t) raised to the  n th power. From the concepts above, the   expression of output frequency response of a stable time invariant linear system described in (1) can be rewritten as   Y(jw)=G(jw)U(jw)   Where   Y(jw)= Y (jw), G(jw)= G (jw), U(jw)= U (jw)               Under the definition that  Un(jw)is the natural extension of  U(jw) to the  n th order homogeneous non-linear situation, equation (3) can be rewritten as     = G(jw)U (jw)       under the condition       The concept of NOFRFs is defined as                   which was introduced by Lang and Billings [1]   One of the important properties of NOFRFs is   Y (jw)= Y (jw) = G(jw)U (jw) 2.2  Evaluation of  G(jw)   In this section, a direct evaluation of  G(jw)will be educed from the definition in  (2.5).   From equation (2.6), it is known that   Y (jw) = G(jw)U (jw)=     =   in which   and        are the real and imaginary part of  ,  and   are the real and imaginary part of  . Therefore                    in which   and                  Consider the case of   where  u(t)=u(t) where  is a constant and u(t)   is the input signal.   The equation above can be written as                     in which  and  are the real and imaginary part of    ,  and are the real and imaginary part of  G *.   Stimulate the system with input signals              where From NOFRFs, the output frequency response under    input can be expressed as below.         where     and                         Using a least square based approach, the expression of  G * can be written as                                    (2.7)              =     The requirement of this algorithm for the determination of the non-linear output frequency response is experimental or   simulation data of the system under different input excitations. In the following chapter, this algorithm will be applied   to an example non-linear system to evaluate its NOFRFs.     Charpter 3  NOFRFs based analysis for a mechanical oscillator     3.1  Introduction of the non-linear system   In this section, the procedure of quantitatively calculates the NOFRFs to investigate the generation of new frequency   components of output signal in non-linear system will be applied to a non-linear system.   The system which can be described by the non-linear system is shown below                         Figure 1. A mechanical oscillator   the differential equation of this system can be described as                                          (3.1)       where u(t) and y(t) are the input and output of the system. and   are the system parameters. In this case, assuming m =1, c =20,  ,   , , to study the behaviour of the non-linear system in frequency domain when subject to a specific input using the analysis procedure in §1.3                                        3.2  Analysis of system output frequency response    The specific input is given by                         (3.2)            t  =  -511 0.005 sec, …, 512 0.005 sec   Having been given the input signal, the output can be obtained in MATLAB by solving ordinary difference equation.   MATLAB uses Runge-Kutta-Fehlberg method solving ordinary difference equations (ODE). The result is calculated   within limited sampling point. In this case, the MATLAB function ode45 will be used to calculate the time domain output   signal which is shown in Figure 3 (b). The code is shown in Appendix A.  Figure 4 shows the spectra of input and   corresponding output signal using Fast Fourier Transform from the time domain input and output signals, the MATLAB   code is shown in Appendix B. From Figure 4, it is very clear that the output spectra have richer frequency components   than those of input signal. Which indicates that some of the input energy is transferred by the system from the input   frequency band (30,55) Hz to a lower frequency range (0,30) Hz. This phenomenon is a very significant property of non-   linear systems. However, further study on the properties of this non-linear system will depends on the investigation of   non-linear output frequency response corresponding to input (3.2).                 const          constant          0                                              x=       y               output           1000  u                input             2000     e                noise             3000    subsystem  =up to 9 subsystems are allowed = number from 1 to 9 times 100   lag        number from 1 to 99      The maximum power that a term can be raised to is 4.   Using the code convention described above, the NARMAX models can be represented as the matrix showing in Appendix F.   Given an input signal, the corresponding output response can be generated using MATLAB in SUN workstation.   The signal (3.1) is chosen as the input signal to the three models       The output responses corresponding to input signal (4.1) of the three given non-linear   systems are generated in laboratory using Sun workstation, and the code is shown in   Appendix G. The input and corresponding output signal of the three studied systems   are showing below:                                                              (1.7)               means to take the integer part                for k=0,…….,          From (4.1), it is easy to find out that the corresponding frequency range of  G(jw) n=1,2,3, is (30,55) Hz,(60,100) Hz and   (50,800) Hz respectively.   First, considering the first order homogeneous non-linear system, the first order transform functions over a frequency   range (30, 55) Hz of each model are generated and shown in figure 18 in the form of spectrum.   <div style="mso-element:frame;mso-element-frame-hspace:7.1pt;mso-element-wrap:
auto;mso-element-anchor-vertical:page;mso-element-anchor-horizontal:page;
mso-element-left:128.4pt;mso-element-top:733.2pt;mso-height-rule:exactly"> </div>                                                                                       Figure 19. The first-order non-linear output transfer functions of three models     From Figure 19, it can be clearly observed that the non-linear output transfer function of the first model has the minimal   gain comparing with other two models while the third model holds a maximum gain.   The following non-linear output transfer functions belong to the second order homogenous non-linear system and the   NOFRFs over a frequency range of (60,100) Hz are showing below:       <div style="mso-element:frame;mso-element-frame-hspace:7.1pt;mso-element-wrap:
auto;mso-element-anchor-vertical:page;mso-element-anchor-horizontal:page;
mso-element-left:128.4pt;mso-element-top:40.45pt;mso-height-rule:exactly"> </div>         Figure 20. The second-order non-linear output transfer functions of three models   Obviously, the NOFRFs that are shown in Figure 20 share the same trend with Figure 19. of model 1 has a maximum value around  1.5 in frequency range (60,100) Hz while the other two models hold a maximum value of 0.03 and 400 respectively.   Then, the non-linear output transfer functions of the third order non-linear   components of these three models in frequency range (5,80) Hz are evaluated and   shown in Figure 21: <div style="mso-element:frame;mso-element-frame-hspace:7.1pt;mso-element-wrap:
auto;mso-element-anchor-vertical:page;mso-element-anchor-horizontal:page;
mso-element-left:116.4pt;mso-element-top:513.15pt;mso-height-rule:exactly"> </div>                                                            Figure 21. The third-order non-linear output transfer functions of three models   Again, the same phenomenon is presented in Figure 21 that is exactly the same with previous two homogeneous non-   linear systems.   For more details of the comparison of these three models, the integrals of each G(jw) over the investigated frequency   ranges are evaluated and which are shown in the table 3. From the table below, the different between each model is very   evident.                                                 Table 3 compare of integral of   for each model       4.4  Conclusion   In this chapter, the concept of NOFRFs is applied to a real mechanical structure andthe output transfer function up to   fourth order homogeneous non-linear systems are evaluated which are shown in figure 18, figure 19 and figure 20. From   above paragraphs, it is able to compare non-linear output transfer function of each model up   to third order in specified frequency range. The result shows a direct relationship between the faults of non-linear system   and the size of output transfer functions. From Table 3, it is even more evident that model 3 always holds the largest   magnitude of G(jw)   than other models in each order. It is also known that the first model is a faultless system while   model 3 has the most significant fault. The study in different order of  G(jw) and various frequency ranges all indicate   that the more faulty systems are, the larger the magnitudes of output transfer function. This conclusion indicates that the   algorithm of NOFRFs can be used in fault detection of non-linear systems to identify the size of faults of non-linear   systems.       Charpter 5  Conclusion     In this paper, a methodology for fault detection in non-linear systems based on the concept of NOFRF (Non-linear Output   Frequency Response Function) has been introduced, and been applied to two different types of non-linear models.   In chapter 2, the first model is given in ODE (Ordinary Differential Equation) form. With a given input signal in time   domain, the corresponding output response is directly generated in MATLAB using function ode45. The NOFRFs are   then calculated using the approach introduced in Chapter 1 and the validity of this approach also been validated.   The second system is given in NARMAX model in chapter 3. A set of codes are written to obtain its output corresponding   to given input in SUN workstation. In this case, three Aluminium plates with different type of fault are introduced in   NARMAX model form. By studying and comparing the NOFRFs of each plate in certain frequency band, it is easy to   discover the relationship between the types of fault and size of  . In this way, it can be found that NOFRFs-based   approach is an effective method in the application of fault detection.      </div>   BIBLIOGRAPHY     Banks, Stephen P, Mathematical theories of nonlinear systems.  London: Prentice- Hall International , 1988Cook, P. A. Nonlinear dynamical systems, 2  ed.  New York;  nd London: Prentic Hall , c1994 Coleman, Matthew P, An introduction to partial differential equations with MATLAB/Matthew P.  Coleman. London: Chapman & Hall/CRC , c2005 H. Zhang, S. A. Billings, Analysing nonlinear systems in the frequency domain, I the transfer function,  Mechanical Systems and Signal Processing  7 (1993) 531-550 H. Zhang, S. A. Billings, Analysing nonlinear systems in the frequency domain,  : the phase response,  Mechanical Systems and Signal Processing  7 (1993) 531-550 Ingle, Vinay K. Digital signal processing using MATLAB.  Pacific Grove, Calif. London: Brooks/Cole Pub , 2000 Kharab, Abdelwahab. An introduction to numerical methods: aMATLAB approach. Boca Raton, Fla. ;  London : Chapman & Hall/CRC , 2002 Khalil, Hassan K., Nonlinear systems, 3  ed (international ed.),  Upper Saddle River,  rd N.J. London: Prentice Hall, c2002.  L. M. Li, S. A. Billings, Generalized frequency response functions and output response synthesis for MIMO non-linear systems.  International Journal of Control , 79:1, 53 – 62 Mohammed Dahleh, Munther A. Dahleh, George Verghese, Lectures on Dynamic Systems and Control.  Department of Electrical Engineering and Computer Science, Massachuasetts Institute of Technology Penny, J. E. T. (John E. T.), Numerical methods using MATLAB,  New York ; London: Ellis Horwood , 1995. Z. Q Lang, S. A. Billings, Output frequency characteristics of nonlinear systems. Research report no.546,  Sheffield: University of Sheffield , 1994 Z. Q Lang, S. A. Billings, Output frequencies of nonlinear systems. Research report no.610,  Sheffield: University of Sheffield , 1995 Z. Q Lang, S. A. Billings, Energy transfer Properties of nonlinear systems in the frequency domain,  International Journal of Control  78 (2005) 354-342 Z. Q Lang, S. A. Billings, R. Yue, J. Li, Output frequency response function of nonlinear Volterra systems Z.K. Peng, Z.Q. Lang and S.A. Billings, Crack detection using nonlinear output frequency response functions.  University of Sheffield              </div> Credit:ivythesis.typepad.com

Dissertation on Nonlinear Output Frequency Response Function and its application in fault detection

for (1.3) In the eq...