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Requirements Engineering in Business Analytics for Innovation and Product Lifecycle Management
(2014)
Considering Requirements Engineering (RE) in business analytics, involving market oriented management, computer science and statistics, may be valuable for managing innovation in Product Lifecycle Management (PLM). RE and business analytics can help maximize the value of corporate product information throughout the value chain starting with innovation management. Innovation and PLM must address 1) big data, 2) development of well-defined business goals and principles, 3) cost/benefit analysis, 4) continuous change management, and 5) statistical and report science. This paper is a positioning note that addresses some business case considerations for analytics project involving PLM data, patents, and innovations. We describe a number of research challenges in RE that addresses business analytics when high PLM data should be turned into a successful market oriented innovation management strategy. We provide a draft on how to address these research challenges.
In biomechanics laboratories the ground reaction force time histories of the foot-fall of persons are usually measured using a force plate. The accelerations of the floor, in which the force plate is embedded, have to be limited, as they may influence the accuracy of the force measurements. For the numerical simulation of vibrations induced by humans in biomechanical laboratories, loading scenarios are defined. They include continuous motions of persons (walking, running) as well as jumps, typical for biomechanical investigations on athletes. The modeling of floors has to take into account the influence of floor screed in case of portable force plates. Criteria for the assessment of the measuring error provoked by floor vibrations are given. As an example a floor designed to accommodate a force platform in a biomechanical laboratory of the University Hospital in Tübingen, Germany, has been investi-gated for footfall induced vibrations. The numerical simulation by a finite element analysis has been validated by field measurements. As a result, the measuring error of the force plate installed in the laboratory is obtained for diverse scenarios.
Konventionelle, von Dieselmotoren angetriebene Radlader beeinträchtigen die Lebensqualität der Menschen in ihrer unmittelbaren Umgebung mit Lärm- und Schadstoffemissionen. Das vom BMBF geförderte Forschungsvorhaben "Emissionsarmer Elektroradlader" verfolgt das Ziel, die lokalen Emissionen von Radladern deutlich herabzusetzen und die Effizienz des Fahrzeugs zu steigern. Im Rahmen des Vorhabens wurde ein konventioneller Radlader auf elektrische Antriebe umgerüstet. Als Energiespeicher dient eine LiFeYPO4-Batterie, die für eine Betriebsdauer von vier Stunden ausgelegt ist. In ersten praktischen Untersuchungen wurde die Energiebilanz des Emissionsarmen Elektro-Radladers mit der des konventionellen Serienfahrzeugs verglichen. Dazu wurde ein modifizierter Y-Arbeitszyklus entworfen, der sich an den üblichen Arbeitsaufgaben des Radladers orientiert und sich durch eine hohe Reproduzierbarkeit auszeichnet. Für die vollständige Bewertung wird die komplette Kette der Energieumwandlung betrachtet, beginnend mit der Energie im Kraftstoff bzw. der dem Stromnetz entnommenen Energie, bis zur mechanischen Arbeit, die das Gerät verrichtet. Daraus lassen sich Rückschlüsse auf die unterschiedlichen CO2-Emissionen beider Fahrzeuge ableiten.
The proposed approach applies current unsupervised clustering approaches in a different dynamic manner. Instead of taking all the data as input and finding clusters among them, the given approach clusters Holter ECG data (long-term electrocardiography data from a holter monitor) on a given interval which enables a dynamic clustering approach (DCA). Therefore advanced clustering techniques based on the well known Dynamic Time Warping algorithm are used. Having clusters e.g. on a daily basis, clusters can be compared by defining cluster shape properties. Doing this gives a measure for variation in unsupervised cluster shapes and may reveal unknown changes in healthiness. Embedding this approach into wearable devices offers advantages over the current techniques. On the one hand users get feedback if their ECG data characteristic changes unforeseeable over time which makes early detection possible. On the other hand cluster properties like biggest or smallest cluster may help a doctor in making diagnoses or observing several patients. Further, on found clusters known processing techniques like stress detection or arrhythmia classification may be applied.