The Experts below are selected from a list of 206784 Experts worldwide ranked by ideXlab platform
Udo Lindemann - One of the best experts on this subject based on the ideXlab platform.
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Enhancing Collaboration between Design and Simulation Departments by Methods of Complexity Management
The Journal of Modern Project Management, 2017Co-Authors: Sebastian Schweigert, Udo Lindemann, Mesut ÇavuşoğluAbstract:The significance of CAD-CAE coupling has grown with the increasing use of simulations in development processes. With the focus on technical aspects like simulation data Management in literature, however, there is a lack of research on the implications on collaboration. This paper uses methods of structural Complexity Management to improve communication and collaboration between simulation and design departments. Design structure matrices and multiple domain matrices are derived from system graphs that come from interviews. A case study uses these methods to handle data to enhance collaboration between departments. The results are techniques to deal with lacking information and low degrees of connectivity in the matrices. After an overlay of different matrices, standard procedures like triangulation and clustering can be applied that would otherwise not have been sensible. This leads to knowledge clusters and sequences of documents and task that help to integrate simulations more smoothly into the product development process.
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Analyzing industrial clusters using measures of structural Complexity Management
Risk and change management in complex systems, 2015Co-Authors: Danilo Marcello Schmidt, Marc Haas, Daniel Kammerl, Julian Wilberg, Maximilian Kissel, Udo LindemannAbstract:Companies organize in industrial clusters to exchange knowledge, to identify new options for cooperation and to improve the regional competences for a special industry sector. For optimizing industrial clusters, it is necessary to assess factors influencing performance or effectivity of industrial clusters. This evaluation or analysis of cluster’s performance can reveal strengths and weaknesses of the cluster. Interpreting the weaknesses might detect activities for improving the performance of the industrial cluster. For this performance analysis, we use measures and metrics of structural Complexity Management to investigate the cluster’s inner structure, e.g. the cooperation and linkage between employees of companies, which are in the same cluster. We applied the measures at the MAI Carbon cluster and interpreted the results of the performance analysis. The user data of the cluster’s online platform serve as the basis for this analysis.
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Part I: DSM Methods and Complexity Management
2014Co-Authors: Mark Grice, Fatos Elezi, David Resch, Iris D Tommelein, Wolfgang Bauer, Udo Lindemann, Nick Kimball, Neeraj Sangal, Donald V StewardAbstract:DSM can be used as an autonomous organization. It can also be used to manage risks. Frustrated and irrational people cannot solve problems, and when people cannot solve the problems that adversely affect them, they become frustrated and irrational. To escape this trap, it is necessary to solve the problems that got people into this trap. The Explainer can be used to extend people’s limited capabilities to solve such complex problems. It can be used to find explanations for specific behaviors. And it can also be used to design systems to satisfy a given behavior by turning an explanation for the behavior into the design. Examples are shown for how the Explainer can be used to shed light on how to solve problems that befuddle Congress and cause such animosity and useless squabbles.
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a viable system model perspective on variant Management based on a structural Complexity Management approach
Risk and Change Management in Complex Systems#R##N#Proceedings of the 16th International DSM Conference Paris France July 2014, 2014Co-Authors: Fatos Elezi, David Resch, Maik Maurer, Iris D Tommelein, Wolfgang Bauer, Udo LindemannAbstract:This paper explores the applicability of Structural Complexity Management (StCM) on organizational design and diagnosis. As basic structural model for efficient Management of organizations the Viable System Model (VSM) is used. The VSM represents an alternative organization model based on Management Cybernetics (MC) theory that describes the structure of all viable systems. Companies operating in dynamic environments strive for viability, therefore incorporation of VSM into their structure is essential. However, VSM requires complex communication and control structures that are not so intuitive at first sight. A methodology that supports the identification and analysis of these structures is still missing, which is why the VSM has not gained wider popularity. This paper addresses a methodology based on StCM that can prove to be beneficial for this purpose. The methodology is applied to an industry case study, where first improvement suggestions based on the newly derived insights are shown.
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Improving Organizational Design and Diagnosis by Supporting Viable System Model Application with Structural Complexity Management
Reducing Risk in Innovation, 2013Co-Authors: Fatos Elezi, David Resch, Iris D Tommelein, Udo LindemannAbstract:This paper explores the applicability of Structural Complexity Management (StCM) on organizational design and diagnosis. As basic structural model for efficient Management of organizations the Viable System Model (VSM) is used. The VSM represents a structural model based on Management Cybernetics (MC) theory that describes the structure of all viable systems. Companies operating in dynamic environments strive for viability, therefore incorporation of VSM into their structure is essential. However, VSM requires more complex communication and control structures that are not so intuitive at first sight, and in addition, a methodology that supports the identification and analysis of these structures is missing. This paper addresses this issue by suggesting and adapting StCM approach for this purpose. StCM is a powerful and proven methodology for analysing structures and making them more transparent and manageable, hence it can support the design and diagnosis of organizational structures based on VSM.
Maik Maurer - One of the best experts on this subject based on the ideXlab platform.
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History of Complexity Management
Complexity Management in Engineering Design – a Primer, 2017Co-Authors: Maik MaurerAbstract:A historical classification shall provide a better understanding and explain the fundamentals of modern Complexity Management. Thinking about the phenomenon of Complexity and dealing with this challenge can be traced back to ancient Greece. From the seventeenth century on, exceptional mathematicians like Isaac Newton and in the nineteenth century philosophers like Emanuel Kant were dealing with Complexity and changed the view of the world. And scientific and methodical knowledge about modern Complexity Management has been aggregated over a time span of approximately 70 years. Significant challenges like those presented by the Second World War, the Cold War and the beginning of astronautics acted as catalysts for developments on Complexity Management. Thus, Complexity Management is not an invention made at the end of the twentieth century. It is based on long-term developments originating from different disciplines. This chapter will give a deeper insight into the historical development.
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A Complexity Management Framework
Complexity Management in Engineering Design – a Primer, 2017Co-Authors: Maik MaurerAbstract:This chapter presents a guideline for implementing an adequate method for managing a complex challenge. A systematic approach towards Complexity Management is crucial, because complex challenges are typically characterized by a lack of clarity. In addition, complex challenges often come with high urgency for action, which can mislead people to take quick measures without assuring their suitability to the situation. The guideline starts with the task of defining the system. This is required for identifying the origin of observed Complexity. Next the type of Complexity needs to be determined, e.g. if it is useful or useless in terms of the higher Management objective. Before selecting a specific Complexity Management method the strategy has to be determined. Once the method of Complexity Management is specified, the system can be modeled adequately for the final implementation. Examples clarifying the steps of this guideline have been chosen from the field of structural Complexity.
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Classification of Complexity Management Approaches in Engineering
Complexity Management in Engineering Design – a Primer, 2017Co-Authors: Maik MaurerAbstract:Whereas in engineering the relevance of complex system interactions is obvious, the meaning of Complexity and Complexity Management is still indistinct. In order to provide transparency over existing approaches and methods, this chapter presents a map of fields in systems engineering and Complexity Management topics and approaches within these fields. Selected works of authors who significantly contributed to and influenced their scientific fields are described. The map further depicts overlaps between the fields based on similarity in Complexity Management practices. This shall illustrate the evolution of different research fields and their interconnectivity regarding Complexity. And it shall facilitate the transfer of methods and procedures of Complexity Management, as the application of new methods is often inspired by transferring them from other fields.
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a viable system model perspective on variant Management based on a structural Complexity Management approach
Risk and Change Management in Complex Systems#R##N#Proceedings of the 16th International DSM Conference Paris France July 2014, 2014Co-Authors: Fatos Elezi, David Resch, Maik Maurer, Iris D Tommelein, Wolfgang Bauer, Udo LindemannAbstract:This paper explores the applicability of Structural Complexity Management (StCM) on organizational design and diagnosis. As basic structural model for efficient Management of organizations the Viable System Model (VSM) is used. The VSM represents an alternative organization model based on Management Cybernetics (MC) theory that describes the structure of all viable systems. Companies operating in dynamic environments strive for viability, therefore incorporation of VSM into their structure is essential. However, VSM requires complex communication and control structures that are not so intuitive at first sight. A methodology that supports the identification and analysis of these structures is still missing, which is why the VSM has not gained wider popularity. This paper addresses a methodology based on StCM that can prove to be beneficial for this purpose. The methodology is applied to an industry case study, where first improvement suggestions based on the newly derived insights are shown.
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SysCon - A survey on Complexity Management in systems engineering
2014 IEEE International Systems Conference Proceedings, 2014Co-Authors: Maik Maurer, Roland Schneller, Mayada OmerAbstract:In this contribution, we classify approaches towards Complexity Management in systems engineering. With the aid of an extensive survey of Complexity, we identify similarities, differences, overlaps and gaps of Complexity definitions and interactions between research areas. The overarching goal of this paper is to support a better understanding of Complexity. An extensive literature review revealed seven disciplines that have direct relevance to issues of Complexity. These disciplines are: Complex Systems, Product Development, Systems Engineering, Software Engineering, Management Science, Complexity Theory and Complex Networks. Additionally, the research unveiled overlaps between the various disciplines and in turn helped to expose voids or white spots between the disciplines. These white spots represent areas without any scientific contributions so far, showing need and possibilities of future research. The findings are implemented to a Venn Diagram for easy access.
Iain E. G. Richardson - One of the best experts on this subject based on the ideXlab platform.
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computational Complexity Management of a real time h 264 avc encoder
IEEE Transactions on Circuits and Systems for Video Technology, 2008Co-Authors: C.s. Kannangara, Iain E. G. Richardson, A.j. MillerAbstract:The H.264 video coding standard supports efficient coding of video at the expense of high computational Complexity. This work addresses the problem of maintaining acceptable video coding performance in a computation-constrained application scenario. A Complexity Management approach is proposed for an H.264 encoder running in a processor/power-constrained environment. We hypothesize that, in a power-constrained application such as mobile video telephony, good perceptual quality requires a balance between a high frame rate and acceptable image quality. Therefore, the objective of the Complexity Management approach is to maintain a smooth video frame rate whilst ensuring that the frame quality is not degraded unacceptably. A frame-level algorithm calculates a target coding time for each frame and drops frames when necessary to maintain acceptable image quality. A per-frame algorithm controls the coding Complexity of each frame in order to achieve the target coding time. The performance of the approach is evaluated by carrying out subjective tests and comparing the managed Complexity encoder with a reference encoder in a computation-constrained scenario. Subjective results show that the managed Complexity encoder consistently achieves superior perceptual video quality ratings compared to the reference encoder.
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Computational Complexity Management of a Real-Time H.264/AVC Encoder
IEEE Transactions on Circuits and Systems for Video Technology, 2008Co-Authors: C.s. Kannangara, Iain E. G. Richardson, A.j. MillerAbstract:The H.264 video coding standard supports efficient coding of video at the expense of high computational Complexity. This work addresses the problem of maintaining acceptable video coding performance in a computation-constrained application scenario. A Complexity Management approach is proposed for an H.264 encoder running in a processor/power-constrained environment. We hypothesize that, in a power-constrained application such as mobile video telephony, good perceptual quality requires a balance between a high frame rate and acceptable image quality. Therefore, the objective of the Complexity Management approach is to maintain a smooth video frame rate whilst ensuring that the frame quality is not degraded unacceptably. A frame-level algorithm calculates a target coding time for each frame and drops frames when necessary to maintain acceptable image quality. A per-frame algorithm controls the coding Complexity of each frame in order to achieve the target coding time. The performance of the approach is evaluated by carrying out subjective tests and comparing the managed Complexity encoder with a reference encoder in a computation-constrained scenario. Subjective results show that the managed Complexity encoder consistently achieves superior perceptual video quality ratings compared to the reference encoder.
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Macroblock classification for Complexity Management of video encoders
Signal Processing: Image Communication, 2003Co-Authors: Yafan Zhao, Iain E. G. RichardsonAbstract:Typically, many macroblocks (MBs) are skipped during encoding of H.263 or MPEG-4 SP video data, particularly at low bit-rates. In this paper, we describe an algorithm that predicts the occurrence of skipped MBs prior to encoding, making it possible to save significant computational effort by not coding these MBs. The algorithm estimates the energy of low-frequency quantized coefficients in order to classify each MB as ‘skipped’ or ‘not skipped’. Results show that the algorithm can deliver substantial computational savings at the expense of a small reduction in rate-distortion performance.
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ACM Multimedia - Complexity Management for video encoders
Proceedings of the tenth ACM international conference on Multimedia - MULTIMEDIA '02, 2002Co-Authors: Yafan Zhao, Iain E. G. RichardsonAbstract:Computational Complexity is an important performance constraint for software-only video CODECs. The aim of this research is to develop a video coding system with variable, controllable computational Complexity. Adaptive algorithms for DCT and motion estimation are proposed separately to reduce Complexity of each function and maintain it at target level. An integrated approach to video CODEC Complexity Management is also addressed. This work will have potential benefit for a wide range of computation-constrained or power-constrained multimedia applications.
A.j. Miller - One of the best experts on this subject based on the ideXlab platform.
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computational Complexity Management of a real time h 264 avc encoder
IEEE Transactions on Circuits and Systems for Video Technology, 2008Co-Authors: C.s. Kannangara, Iain E. G. Richardson, A.j. MillerAbstract:The H.264 video coding standard supports efficient coding of video at the expense of high computational Complexity. This work addresses the problem of maintaining acceptable video coding performance in a computation-constrained application scenario. A Complexity Management approach is proposed for an H.264 encoder running in a processor/power-constrained environment. We hypothesize that, in a power-constrained application such as mobile video telephony, good perceptual quality requires a balance between a high frame rate and acceptable image quality. Therefore, the objective of the Complexity Management approach is to maintain a smooth video frame rate whilst ensuring that the frame quality is not degraded unacceptably. A frame-level algorithm calculates a target coding time for each frame and drops frames when necessary to maintain acceptable image quality. A per-frame algorithm controls the coding Complexity of each frame in order to achieve the target coding time. The performance of the approach is evaluated by carrying out subjective tests and comparing the managed Complexity encoder with a reference encoder in a computation-constrained scenario. Subjective results show that the managed Complexity encoder consistently achieves superior perceptual video quality ratings compared to the reference encoder.
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Computational Complexity Management of a Real-Time H.264/AVC Encoder
IEEE Transactions on Circuits and Systems for Video Technology, 2008Co-Authors: C.s. Kannangara, Iain E. G. Richardson, A.j. MillerAbstract:The H.264 video coding standard supports efficient coding of video at the expense of high computational Complexity. This work addresses the problem of maintaining acceptable video coding performance in a computation-constrained application scenario. A Complexity Management approach is proposed for an H.264 encoder running in a processor/power-constrained environment. We hypothesize that, in a power-constrained application such as mobile video telephony, good perceptual quality requires a balance between a high frame rate and acceptable image quality. Therefore, the objective of the Complexity Management approach is to maintain a smooth video frame rate whilst ensuring that the frame quality is not degraded unacceptably. A frame-level algorithm calculates a target coding time for each frame and drops frames when necessary to maintain acceptable image quality. A per-frame algorithm controls the coding Complexity of each frame in order to achieve the target coding time. The performance of the approach is evaluated by carrying out subjective tests and comparing the managed Complexity encoder with a reference encoder in a computation-constrained scenario. Subjective results show that the managed Complexity encoder consistently achieves superior perceptual video quality ratings compared to the reference encoder.
C.s. Kannangara - One of the best experts on this subject based on the ideXlab platform.
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computational Complexity Management of a real time h 264 avc encoder
IEEE Transactions on Circuits and Systems for Video Technology, 2008Co-Authors: C.s. Kannangara, Iain E. G. Richardson, A.j. MillerAbstract:The H.264 video coding standard supports efficient coding of video at the expense of high computational Complexity. This work addresses the problem of maintaining acceptable video coding performance in a computation-constrained application scenario. A Complexity Management approach is proposed for an H.264 encoder running in a processor/power-constrained environment. We hypothesize that, in a power-constrained application such as mobile video telephony, good perceptual quality requires a balance between a high frame rate and acceptable image quality. Therefore, the objective of the Complexity Management approach is to maintain a smooth video frame rate whilst ensuring that the frame quality is not degraded unacceptably. A frame-level algorithm calculates a target coding time for each frame and drops frames when necessary to maintain acceptable image quality. A per-frame algorithm controls the coding Complexity of each frame in order to achieve the target coding time. The performance of the approach is evaluated by carrying out subjective tests and comparing the managed Complexity encoder with a reference encoder in a computation-constrained scenario. Subjective results show that the managed Complexity encoder consistently achieves superior perceptual video quality ratings compared to the reference encoder.
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Computational Complexity Management of a Real-Time H.264/AVC Encoder
IEEE Transactions on Circuits and Systems for Video Technology, 2008Co-Authors: C.s. Kannangara, Iain E. G. Richardson, A.j. MillerAbstract:The H.264 video coding standard supports efficient coding of video at the expense of high computational Complexity. This work addresses the problem of maintaining acceptable video coding performance in a computation-constrained application scenario. A Complexity Management approach is proposed for an H.264 encoder running in a processor/power-constrained environment. We hypothesize that, in a power-constrained application such as mobile video telephony, good perceptual quality requires a balance between a high frame rate and acceptable image quality. Therefore, the objective of the Complexity Management approach is to maintain a smooth video frame rate whilst ensuring that the frame quality is not degraded unacceptably. A frame-level algorithm calculates a target coding time for each frame and drops frames when necessary to maintain acceptable image quality. A per-frame algorithm controls the coding Complexity of each frame in order to achieve the target coding time. The performance of the approach is evaluated by carrying out subjective tests and comparing the managed Complexity encoder with a reference encoder in a computation-constrained scenario. Subjective results show that the managed Complexity encoder consistently achieves superior perceptual video quality ratings compared to the reference encoder.
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Complexity Management of H.264/AVC Video Compression
2006Co-Authors: C.s. KannangaraAbstract:The H. 264/AVC video coding standard offers significantly improved compression efficiency and flexibility compared to previous standards. However, the high computational Complexity of H. 264/AVC is a problem for codecs running on low-power hand held devices and general purpose computers. This thesis presents new techniques to reduce, control and manage the computational Complexity of an H. 264/AVC codec. A new Complexity reduction algorithm for H. 264/AVC is developed. This algorithm predicts "skipped" macroblocks prior to motion estimation by estimating a Lagrange ratedistortion cost function. Complexity savings are achieved by not processing the macroblocks that are predicted as "skipped". The Lagrange multiplier is adaptively modelled as a function of the quantisation parameter and video sequence statistics. Simulation results show that this algorithm achieves significant Complexity savings with a negligible loss in rate-distortion performance. The Complexity reduction algorithm is further developed to achieve Complexity-scalable control of the encoding process. The Lagrangian cost estimation is extended to incorporate computational Complexity. A target level of Complexity is maintained by using a feedback algorithm to update the Lagrange multiplier associated with Complexity. Results indicate that scalable Complexity control of the encoding process can be achieved whilst maintaining near optimal Complexity-rate-distortion performance. A Complexity Management framework is proposed for maximising the perceptual quality of coded video in a real-time processing-power constrained environment. A real-time frame-level control algorithm and a per-frame Complexity control algorithm are combined in order to manage the encoding process such that a high frame rate is maintained without significantly losing frame quality. Subjective evaluations show that the managed Complexity approach results in higher perceptual quality compared to a reference encoder that drops frames in computationally constrained situations. These novel algorithms are likely to be useful in implementing real-time H. 264/AVC standard encoders in computationally constrained environments such as low-power mobile devices and general purpose computers.