Final X-Hab 2012 Project Report Project on “Geo-Lab reduced gravity sample holders/manipulator tools”
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Despite the critical importance of project completion timeliness, management practices in place today remain inadequate for addressing the persistent problem of project completion tardiness. A major culprit in late projects is uncertainty, which most, if not all, projects are inherently subject to. This uncertainty resides in the estimates for activity durations, the occurrence of unplanned and unforeseen events, and the availability of critical resources. In response to this problem, this research developed a comprehensive simulation based methodology for conducting quantitative project completion time risk analysis. It is called the Project Assessment by Simulation Technique (PAST). This new tool enables project stakeholders to visualize uncertainty or risk, i.e. the likelihood of their project completing late and the magnitude of the lateness, by providing them with a completion time distribution function of their projects. Discrete event simulation is used within PAST to determine the completion distribution function for the project of interest. The simulation is populated with both deterministic and stochastic elements. The deterministic inputs include planned project activities, precedence requirements, and resource requirements. The stochastic inputs include activity duration growth distributions, probabilities for events that can impact the project, and other dynamic constraints that may be placed upon project activities and milestones. These stochastic inputs are based upon past data from similar projects. The time for an entity to complete the simulation network, subject to both the deterministic and stochastic factors, represents the time to complete the project. Repeating the simulation hundreds or thousands of times allows one to create the project completion distribution function. The Project Assessment by Simulation Technique was demonstrated to be effective for the on-going NASA project to assemble the International Space Station. Approximately $500 million per month is being spent on this project, which is scheduled to complete by 2010. NASA project stakeholders participated in determining and managing completion distribution functions produced from PAST. The first result was that project stakeholders improved project completion risk awareness. Secondly, using PAST, mitigation options were analyzed to improve project completion performance and reduce total project cost.
Implementing guidance, navigation, and control (GN&C) theory principles and applying them to the human element of project management and control is not a new concept. As both the literature on the subject and the real-world applications are neither readily available nor comprehensive with regard to how such principles might be applied, this paper has been written to educate the project manager on the "laws of physics" of his or her project (not to teach a GN&C engineer how to become a project manager) and to provide an intuitive, mathematical explanation as to the control and behavior of projects. This paper will also address how the fundamental principles of modern GN&C were applied to the National Aeronautics and Space Administration's (NASA) Constellation Program (CxP) space suit project, ensuring the project was managed within cost, schedule, and budget. A project that is akin to a physical system can be modeled and managed using the same over arching principles of GN&C that would be used if that project were a complex vehicle, a complex system(s), or complex software with time-varying processes (at times nonlinear) containing multiple data inputs of varying accuracy and a range of operating points. The classic GN&C theory approach could thus be applied to small, well-defined projects; yet when working with larger, multiyear projects necessitating multiple organizational structures, numerous external influences, and a multitude of diverse resources, modern GN&C principles are required to model and manage the project. The fundamental principles of a GN&C system incorporate these basic concepts: State, Behavior, Feedback Control, Navigation, Guidance and Planning Logic systems. The State of a system defines the aspects of the system that can change over time; e.g., position, velocity, acceleration, coordinate-based attitude, and temperature, etc. The Behavior of the system focuses more on what changes are possible within the system; this is denoted in the state of the system. The behavior of a system, as captured in the system modeling, when properly done will aid in accurately predicting future system performance. The Feedback Control system understands the state and behavior of the system and uses feedback to adjust control inputs into the system. The feedback, which is the right arm of the Control system, allows change to be affected in the overall system; it therefore is important to not only correctly identify the system feedback inputs, but also the system response to the feedback inputs. The Navigation system takes multiple data inputs and based on a priori knowledge of the inputs, develops a statistically based weighting of the inputs and measurements to determine the system's state. Guidance and Planning Logic of the system, complete with an understanding of where the system is (provided by the Navigation system), will in turn determine where the system needs to be and how to get it there. With any system/project, it is critical that the objective of the system/project be clearly defined -- not only to plan but to measure performance and to aid in guiding the system or the project. The system principles discussed above, which can be and have been applied to the current CxP space suit development project, can also be mapped to real-world constituents, thus allowing project managers to apply systems theories that are well defined in engineering and mathematics to a discipline (i.e., Project Management) that historically has been based in personal experience and intuition. This mapping of GN&C theory to Project Management will, in turn, permit a direct, methodical approach to Project Management, planning and control providing a tool to help predict (and guide) performance and an understanding of the project constraints, how the project can be controlled, and the impacts to external influences and inputs. This approach, to a project manager, flows down to the three bottom-line variables of cost, schedule, and scope ando the needed control of these three variables to successfully perform and complete a project.
George Meade defeated Robert E. Lee, one of the greatest military leaders of all time. How did he do it? By using the skills he had learned as a project manager and outperforming Lee in all aspects of project management. Most project managers are familiar with the Project Management Institute's "Guide to the Project Management Body of Knowledge" (PMBOK), which identifies the skills and knowledge crucial to successful project management. Project managers need to make sure that all the elements of a project work together. They must develop and execute plans and coordinate changes to those plans. A project manager must define the scope of the work, break it into manageable pieces, verify and control what work is being done, and make sure that the work being done is essential to the project. Every project manager knows the challenges of schedule and the value of schedule slack. Project managers must get the resources they need and use them effectively. Project managers get the people they need and use their talents to achieve mission success. Projects generate huge amounts of information. A key to project success is getting sufficient and accurate information to the people who need it when they need it. Project managers must identify and quantify the risks that jeopardize project success and make plans for dealing with them. Studying Meade and Lee's performances at Gettysburg can help modern project managers appreciate, develop, and use the skills they need to be good project managers. The circumstances may be different, but the basic principles are the same. This dramatic event in American history shows how the skills of project management can be used in almost any situation. Former project manager George Meade used those skills to change the tide of the Civil War.
Large projects almost always exceed their budgets. Estimating cost is difficult and estimated costs are usually too low. Three different reasons are suggested: bad luck, overoptimism, and deliberate underestimation. Project management can usually point to project difficulty and complexity, technical uncertainty, stakeholder conflicts, scope changes, unforeseen events, and other not really unpredictable bad luck. Project planning is usually over-optimistic, so the likelihood and impact of bad luck is systematically underestimated. Project plans reflect optimism and hope for success in a supposedly unique new effort rather than rational expectations based on historical data. Past project problems are claimed to be irrelevant because "This time it's different." Some bad luck is inevitable and reasonable optimism is understandable, but deliberate deception must be condemned. In a competitive environment, project planners and advocates often deliberately underestimate costs to help gain project approval and funding. Project benefits, cost savings, and probability of success are exaggerated and key risks ignored. Project advocates have incentives to distort information and conceal difficulties from project approvers. One naively suggested cure is more openness, honesty, and group adherence to shared overall goals. A more realistic alternative is threatening overrun projects with cancellation. Neither approach seems to solve the problem. A better method to avoid the delusions of over-optimism and the deceptions of biased advocacy is to base the project cost estimate on the actual costs of a large group of similar projects. Over optimism and deception can continue beyond the planning phase and into project execution. Hard milestones based on verified tests and demonstrations can provide a reality check.
The purpose of HSR&TD is to deliver human support technologies to the Exploration Systems Mission Directorate (ESMD) that will be selected for future missions. This requires identifying promising candidate technologies and advancing them in technology readiness until they are acceptable. HSR&TD must select an may of technology development projects, guide them, and either terminate or continue them, so as to maximize the resulting number of usable advanced human support technologies. This paper proposes an effective project scoring methodology to support managing the HSR&TD project portfolio. Researchers strongly disagree as to what are the best technology project selection methods, or even if there are any proven ones. Technology development is risky and outstanding achievements are rare and unpredictable. There is no simple formula for success. Organizations that are satisfied with their project selection approach typically use a mix of financial, strategic, and scoring methods in an open, established, explicit, formal process. This approach helps to build consensus and develop management insight. It encourages better project proposals by clarifying the desired project attributes. We propose a project scoring technique based on a method previously used in a federal laboratory and supported by recent research. Projects are ranked by their perceived relevance, risk, and return - a new 3 R's. Relevance is the degree to which the project objective supports the HSR&TD goal of developing usable advanced human support technologies. Risk is the estimated probability that the project will achieve its specific objective. Return is the reduction in mission life cycle cost obtained if the project is successful. If the project objective technology performs a new function with no current cost, its return is the estimated cash value of performing the new function. The proposed project selection scoring method includes definitions of the criteria, a project evaluation questionnaire, and a scoring formula.
The Guidance and Control Software (GCS) project was the last in a series of software reliability studies conducted at Langley Research Center between 1977 and 1994. The technical results of the GCS project were recorded after the experiment was completed. Some of the support documentation produced as part of the experiment, however, is serving an unexpected role far beyond its original project context. Some of the software used as part of the GCS project was developed to conform to the RTCA/DO-178B software standard, "Software Considerations in Airborne Systems and Equipment Certification," used in the civil aviation industry. That standard requires extensive documentation throughout the software development life cycle, including plans, software requirements, design and source code, verification cases and results, and configuration management and quality control data. The project documentation that includes this information is open for public scrutiny without the legal or safety implications associated with comparable data from an avionics manufacturer. This public availability has afforded an opportunity to use the GCS project documents for DO-178B training. This report provides a brief overview of the GCS project, describes the 4-volume set of documents and the role they are playing in training, and includes configuration management and quality assurance documents from the GCS project. Volume 4 contains six appendices: A. Software Accomplishment Summary for the Guidance and Control Software Project; B. Software Configuration Index for the Guidance and Control Software Project; C. Configuration Management Records for the Guidance and Control Software Project; D. Software Quality Assurance Records for the Guidance and Control Software Project; E. Problem Report for the Pluto Implementation of the Guidance and Control Software Project; and F. Support Documentation Change Reports for the Guidance and Control Software Project.
This Paper under-estimates answers to the following question under various constraints: If a geofencing algorithm uses a map projection to determine whether a position is inside/outside a polygon region, how far outside/inside the polygon can the point be and the algorithm determine that it is inside/outside (the opposite and therefore incorrect answer)? Geofencing systems for unmanned aircraft systems (UAS) often model stay-in and stay-out regions using 2D polygons with minimum and maximum altitudes. The vertices of the polygons are typically input as latitude-longitude pairs, and the edges as paths between adjacent vertices. There are numerous ways to generate these paths, resulting in numerous potential locations for the edges of stay-in and stay-out regions. These paths may be geodesics on a spherical model of the earth or geodesics on the WGS84 reference ellipsoid. In geofencing applications that use map projections, these paths are inverse images of straight lines in the projected plane. This projected plane may be a projection of a spherical earth model onto a tangent plane, called an orthographic projection. Alternatively, it may be a projection where the straight lines in the projected plane correspond to straight lines in the latitudelongitude coordinate system, also called a Plate Carr´ee projection. This paper estimates distances between different edge paths and an oracle path, which is a geodesic on either the spherical earth or the WGS84 ellipsoidal earth. This paper therefore estimates how far apart different edge paths can be rather than comparing their path lengths, which are not considered. Rather, the comparision is between the actual locations of the edges between vertices. For edges drawn using orthographic projections, this maximum distance increases as the distance from the polygon vertices to the projection point increases. For edges drawn using Plate Carr´ee projections, this maximum distance increases as the vertices become further from the equator. Distances between geodesics on a spherical earth and a WGS84 ellipsoidal earth are also analyzed, using the WGS84 ellipsoid as the oracle. Bounds on the 2D distance between a straight line and a great circle path, in an orthographically projected plane rather than on the surface of the earth, have been formally verified in the PVS theorem prover, meaning that they are mathematically correct in the absence of floating point errors.
The primate cingulate gyrus contains multiple cortical areas that can be distinguished by several neurochemical features, including the distribution of neurofilament protein-enriched pyramidal neurons. In addition, connectivity and functional properties indicate that there are multiple motor areas in the cortex lining the cingulate sulcus. These motor areas were targeted for analysis of potential interactions among regional specialization, connectivity, and cellular characteristics such as neurochemical profile and morphology. Specifically, intracortical injections of retrogradely transported dyes and intracellular injection were combined with immunocytochemistry to investigate neurons projecting from the cingulate motor areas to the putative forelimb region of the primary motor cortex, area M1. Two separate groups of neurons projecting to area M1 emanated from the cingulate sulcus, one anterior and one posterior, both of which furnished commissural and ipsilateral connections with area M1. The primary difference between the two populations was laminar origin, with the anterior projection originating largely in deep layers, and the posterior projection taking origin equally in superficial and deep layers. With regard to cellular morphology, the anterior projection exhibited more morphologic diversity than the posterior projection. Commissural projections from both anterior and posterior fields originated largely in layer VI. Neurofilament protein distribution was a reliable tool for localizing the two projections and for discriminating between them. Comparable proportions of the two sets of projection neurons contained neurofilament protein, although the density and distribution of the total population of neurofilament protein-enriched neurons was very different in the two subareas of origin. Within a projection, the participating neurons exhibited a high degree of morphologic heterogeneity, and no correlation was observed between somatodendritic morphology and neurofilament protein content. Thus, although the neurons that provide the anterior and posterior cingulate motor projections to area M1 differ morphologically and in laminar origin, their neurochemical profiles are similar with respect to neurofilament protein. This suggests that neurochemical phenotype may be a more important unifying feature for corticocortical projections than morphology.
Shared Voyage is about four remarkable projects: the Advanced Composition Explorer (NASA), the Joint Air-to-Surface Standoff Missile (U.S. Air Force), the Pathfinder Solar-Powered Airplane (NASA), and the Advanced Medium Range Air-to-Air Missile (U.S.Air Force). Each project is presented as a case study comprised of stories collected from key members of the project teams. The stories found in the book are included with the purpose of providing an effective learning source for project management, encouraging the unlearning of outdated project management concepts, and enhancing awareness of the contexts surrounding different projects. Significantly different from project concepts found in most project management literature, Shared Voyage highlights concepts like a will to win, a results-oriented focus, and collaboration through trust. All four project teams researched in this study applied similar concepts; however, they applied them differently, tailoring them to fit the context of their own particular projects. It is clear that the one best way approach which is still the prevailing paradigm in project management literature should be replaced by a new paradigm: Even though general project management principles exist, their successful application depends on the specifics of the situation.
The Guidance and Control Software (GCS) project was the last in a series of software reliability studies conducted at Langley Research Center between 1977 and 1994. The technical results of the GCS project were recorded after the experiment was completed. Some of the support documentation produced as part of the experiment, however, is serving an unexpected role far beyond its original project context. Some of the software used as part of the GCS project was developed to conform to the RTCA/DO-178B software standard, "Software Considerations in Airborne Systems and Equipment Certification," used in the civil aviation industry. That standard requires extensive documentation throughout the software development life cycle, including plans, software requirements, design and source code, verification cases and results, and configuration management and quality control data. The project documentation that includes this information is open for public scrutiny without the legal or safety implications associated with comparable data from an avionics manufacturer. This public availability has afforded an opportunity to use the GCS project documents for DO-178B training. This report provides a brief overview of the GCS project, describes the 4-volume set of documents and the role they are playing in training, and includes the planning documents from the GCS project. Volume 1 contains five appendices: A. Plan for Software Aspects of Certification for the Guidance and Control Software Project; B. Software Development Standards for the Guidance and Control Software Project; C. Software Verification Plan for the Guidance and Control Software Project; D. Software Configuration Management Plan for the Guidance and Control Software Project; and E. Software Quality Assurance Activities.
This slide presentation reviews the use of a Statistical Project Control Tool (SPCT) for managing engineering projects. A literature review pointed to a definition of project success, (i.e., A project is successful when the cost, schedule, technical performance, and quality satisfy the customer.) The literature review also pointed to project success factors, and traditional project control tools, and performance measures that are detailed in the report. The essential problem is that with resources becoming more limited, and an increasing number or projects, project failure is increasing, there is a limitation of existing methods and systematic methods are required. The objective of the work is to provide a new statistical project control tool for project managers. Graphs using the SPCT method plotting results of 3 successful projects and 3 failed projects are reviewed, with success and failure being defined by the owner.
My internship was in the IDC which consist of a machine shop and an array of design space. During my tour I worked on a wide variety of projects some of which included design, research, machining and fabrication. I gained further knowledge on some machines that I have had prior experience on such as the lathe and Hurco CNC machines. The first thing we did was complete our checkout in the machine shop which went pretty well, since I was already familiar with most of the machines. I also did a couple of practice parts on some of the machines, I made a name block on the CNC machine and I also used the vertical milling machine to complete this project. One of the other projects that I did was machine a hammer with my initials with the use of the lathe and CNC machine, this project took much longer since I had to set up a cylindrical piece on the CNC machine. The first project that I began work on was the Systems Engineering & Management Advancement Program (SEPMAP) Hexacopter project and helped them to assemble and modify one of their particle capture doors on their boxes. After a while we ended up helping them make a hinge and holes to reduce the weight of their design. We helped the NASA Extreme Environment Mission Operations (NEEMO) team a bit with some of their name tags and assembly of some of their underwater parts. One of the more challenging projects was a rail that came in with a rather weirdly drawn part. The biggest project that I worked on was the solar array project. Which consisted of a variety of machining and 3D printing and it took me about 3 different times of re-designing to come up with a final prototype. Along with this project I also had to complete a project in which I had to modify a thermos. This was rather simple since I just had to draw up a part and print it out on the 3D printer. I also learned how to use Pro E/Creo parametric to design a square block and print it on the 3D Printer. All of these projects increased my experience on all of the machines and equipment that I used. I also got to tweak my design skills and better understand how to modify my designs and how to improve those specific designs.
Since the mid 1980s, much of the marine science research effort in the United Kingdom has been focused into large scale collaborative projects involving public sector laboratories and university departments, termed Community Research Projects. Two of these, the Biogeochemical Ocean Flux Study (BOFS) and the North Sea Project incorporated large scale data collection to underpin multidisciplinary modeling efforts. The challenge of providing project data sets to support the science was met by a small team within the British Oceanographic Data Centre (BODC) operating as a topical data center. The role of the data center was to both work up the data from the ship's sensors and to combine these data with sample measurements into online databases. The working up of the data was achieved by a unique symbiosis between data center staff and project scientists. The project management, programming and data processing skills of the data center were combined with the oceanographic experience of the project communities to develop a system which has produced quality controlled, calibrated data sets from 49 research cruises in 3.5 years of operation. The data center resources required to achieve this were modest and far outweighed by the time liberated in the scientific community by the removal of the data processing burden. Two online project databases have been assembled containing a very high proportion of the data collected. As these are under the control of BODC their long term availability as part of the UK national data archive is assured. The success of the topical data center model for UK Community Research Project data management has been founded upon the strong working relationships forged between the data center and project scientists. These can only be established by frequent personal contact and hence the relatively small size of the UK has been a critical factor. However, projects covering a larger, even international scale could be successfully supported by a network of topical data centers managing online databases which are interconnected by object oriented distributed data management systems over wide area networks.
The ectostriatum is a major visual component of the avian telencephalon. The core region of the ectostriatum (Ec) receives visual input from the optic tectum through thalamic nuclei. In the present study, the efferent projections of the ectostriatum were investigated by using the anterograde tracers Phaseolus vulgaris leucoagglutinin and biotinylated dextran amine. Projection patterns resulting from these tracers were confirmed by the retrograde tracer cholera toxin subunit B. When anterograde tracers were injected in Ec, primary projections were seen traveling dorsolaterally to the belt region of the ectostriatum (Ep) and the neostriatal area immediately surrounding Ep (Ep2). Neurons in Ep sent projections primarily to the overlying Ep2. The efferents of Ep2 traveled dorsolaterally to terminate in three telencephalic regions, from anterior to posterior: (1) neostriatum frontale, pars lateralis (NFL), (2) area temporo-parieto-occipitalis (TPO), and (3) neostriatum intermedium, pars lateralis (NIL). A part of the archistriatum intermedium and the lateral part of the neostriatum caudale also received somewhat minor projections. In addition, some neurons in Ec were also the source of direct, but minor, projections to the NFL, TPO, NIL, and archistriatum intermedium. The topographical relationship among the primary (Ec), secondary (Ep and Ep2), and tertiary (NFL, TPO, NIL) areas indicate that the neural populations for visual processing are organized along the rostral-caudal axis. Thus, the anterior Ec sent efferents to the anterior Ep, which in turn sent projections to anterior Ep2. Neurons in the anterior Ep2 sent projections to NFL and the anterior TPO. Similarly, the intermediate and posterior Ec sent projections to corresponding parts of Ep, whose efferents projected to intermediate and posterior Ep2, respectively. The intermediate Ep2 gave rise to major projections to TPO, whereas posterior Ep2 neurons sent efferents primarily to NIL. The organization of this neural circuit is compared with those of other sensory circuits in the avian telencephalon, as well as the laminar arrangement of the mammalian isocortex.
Previous studies of the primate cerebral cortex have shown that neurofilament protein is present in pyramidal neuron subpopulations displaying specific regional and laminar distribution patterns. In order to characterize further the neurochemical phenotype of the neurons furnishing feedforward and feedback pathways in the visual cortex of the macaque monkey, we performed an analysis of the distribution of neurofilament protein in corticocortical projection neurons in areas V1, V2, V3, V3A, V4, and MT. Injections of the retrogradely transported dyes Fast Blue and Diamidino Yellow were placed within areas V4 and MT, or in areas V1 and V2, in 14 adult rhesus monkeys, and the brains of these animals were processed for immunohistochemistry with an antibody to nonphosphorylated epitopes of the medium and heavy molecular weight subunits of the neurofilament protein. Overall, there was a higher proportion of neurons projecting from areas V1, V2, V3, and V3A to area MT that were neurofilament protein-immunoreactive (57-100%), than to area V4 (25-36%). In contrast, feedback projections from areas MT, V4, and V3 exhibited a more consistent proportion of neurofilament protein-containing neurons (70-80%), regardless of their target areas (V1 or V2). In addition, the vast majority of feedback neurons projecting to areas V1 and V2 were located in layers V and VI in areas V4 and MT, while they were observed in both supragranular and infragranular layers in area V3. The laminar distribution of feedforward projecting neurons was heterogeneous. In area V1, Meynert and layer IVB cells were found to project to area MT, while neurons projecting to area V4 were particularly dense in layer III within the foveal representation. In area V2, almost all neurons projecting to areas MT or V4 were located in layer III, whereas they were found in both layers II-III and V-VI in areas V3 and V3A. These results suggest that neurofilament protein identifies particular subpopulations of corticocortically projecting neurons with distinct regional and laminar distribution in the monkey visual system. It is possible that the preferential distribution of neurofilament protein within feedforward connections to area MT and all feedback projections is related to other distinctive properties of these corticocortical projection neurons.