Thesis (M.A.)--University of Wisconsin--Madison, 1964. / eContent provider-neutral record in process. Description based on print version record. Bibliography: l. 240-245.

Produktivitätsmodelle geben Auskunft darüber, welche Leistung mit einem bestimmten Verfahren unter konkreten Rahmenbedingungen zu erwarten ist. In der Forstwirtschaft sind sie für Planungen und Kalkulationen notwendig. In bisherigen forstlichen Produktivitätsmodellen für den Maschineneinsatz wurde der Faktor Mensch, welcher einen wichtigen, bisher jedoch weitestgehend unbekannten Einfluss auf die Leistung hat, vernachlässigt. Ziel der vorliegenden Untersuchung ist es daher, den menschlichen Einfluss auf die Leistung von Harvestersystemen quantitativ zu erfassen und ihn in Produktivitätsmodelle einzubauen. Die Untersuchungsgrundlage bilden Leistungsmessungen, die mit vier verschiedenen Datenerhebungsverfahren an bis zu 32 Fahrern erfolgten. Neben einer neu entwickelten, sensorgestützten, semiautomatischen Zeitstudienmessung fand die Auswertung von summarischen Bordcomputer-Bestandesdaten über einen Zeitraum von drei Jahren Anwendung. Weiterhin erfolgte die (Weiter-)Entwicklung eines standardisierten Parcourstests, welcher auf einer Freifläche durchgeführt wird sowie eine Beurteilung der Leistung der Harvesterfahrer durch Gutachter. Alle vier Verfahren wurden miteinander verglichen und auf ihre Treffsicherheit und Aussagefähigkeit hin überprüft. Dabei konnten signifikante Korrelationen zwischen allen vier Datenerhebungsverfahren nachgewiesen werden, was eine Umrechnung untereinander grundsätzlich zulässt. Im Rahmen dieser Untersuchungen konnte die Vermutung quantitativ bestätigt werden, dass große, signifikant unterschiedliche Leistungshergaben zwischen den verschiedenen Harvesterfahrern existieren. Dies gilt nicht nur für den Vergleich zwischen so genannten „unerfahrenen“ und „erfahrenen“ Maschinenführern. Auch zwischen schon lange auf den Erntemaschinen arbeitenden Fahrern ist ein Leistungsunterschied von bis zu 80% zu verzeichnen. Somit ist die Beachtung des Parameters „Mensch“ für präzise Produktivitätsmodelle zwingend erforderlich. Der ursprüngliche Lösungsansatz, ein allgemein gültiges Produktivitätsmodell zu entwickeln, in dem der Einfluss des Menschen berücksichtigt wird, wurde aufgrund von Informationsdefiziten sowie zu starken Unterschieden und Komplexität der vorhandenen Modelle verworfen. Mit dem neu gewählten Lösungsansatz, der die Bestimmung eines Leistungswertes für jeden Fahrer vorsieht, ist es nun möglich, ein beliebig erstelltes Produktivitätsmodell linear auf ein Basisniveau zu normieren. Die Multiplikation des normierten Modells mit dem Leistungswert eines Fahrers ermöglicht es, die wahrscheinlich von ihm zu erwartende Produktivität zu berechnen. Dieser Wert kann auf verschiedene Weisen erhoben werden. Als Ergebnis dieser Arbeit kann dafür der entwickelte, schnell und einfach durchzuführende Parcourstest empfohlen werden. Er erreicht eine ungefähre Treffgenauigkeit von ±10%. Die Untersuchungen wiesen des Weiteren aus, dass beim Harvestereinsatz unter einfachen Umwelt- und Geländebedingungen der Fahrereinfluss auf die Produktivität bei 37% liegt. Er stellt somit nach Baumvolumen des ausscheidenden Bestandes (46%) den zweitwichtigsten Einflussfaktor dar. Die Leistungshergabe des einzelnen Harvesterfahrers ist nicht immer gleich, sondern verändert sich über der Zeit (Lernkurve). Die gemessenen Lerngeschwindigkeiten variieren dabei zwischen den Harvesterfahrern sehr stark. Das Anlernen eines unerfahrenen Harvesterfahrers dauerte bei den untersuchten Probanden im Mittel neun Monate, was einer Minderleistung von ca. 24% über diesen Zeitraum entspricht. Die bisherigen angenommenen Modelle der Lernkurve konnten bestätigt werden. Darüber hinaus zeigten sich Tendenzen, dass die Leistung der Fahrer nach der Lernphase ein konstantes Leistungsniveau erreicht, allerdings bei einer hohen Fehlerabweichung. Durch die Beurteilung und Berücksichtigung der Leistungen der Fahrer steht der Forstwirtschaft ein Werkzeug zur Verfügung, mit dem man in Zukunft die erstellten Produktivitätsmodelle für den Harvestereinsatz genauer auf die jeweiligen individuellen Gegebenheiten des eigenen Betriebes anpassen kann. Die Übertragung des gewählten Lösungsansatzes auf weitere Forstmaschinen, die von Menschen bedient werden, sollte geprüft werden. / Productivity models provide information about the expected performance of a given procedure under specific conditions. In forestry, they are essential to planning and cost estimation. In hitherto existing productivity models of forest machinery, however, the human being as an important yet mostly unknown factor influencing productivity has been disregarded. Therefore, the objectives of this study are the quantification of the human impact on the performance of forest harvesting systems and its integration into productivity models. The study is based on performance measurements collected using four different methods of data acquisition to monitor up to 32 machine operators: (i) a newly developed sensor-based semi-automatic time study, (ii) the extraction of on-board computer data accumulated over a 3-year period, (iii) an improved standardized machinery test course conducted in an open area, and (iv) the expert evaluation of operator performance. The four data acquisition methods were compared and tested for data accuracy and informational value. All correlations between data yielded by each of the four methods were significant, thus in principle allowing for data conversion between data rendered by different acquisition methods. The results of this study quantitatively corroborate the presumption of major, significantly different performances between harvester operators. This holds true not only for the comparison of so-called ‘inexperienced’ and ‘experienced’ machine operators, but also when exclusively comparing experienced operators with each other. A performance difference of up to 80% has been observed between individuals featuring long-term experience in operating harvesting machinery. The integration of the parameter ‘human being’ is thus an imperative for precise productivity models. The original aim of developing a general productivity model accounting for human influence had to be abandoned due to an information deficit, substantial differences between the existing models as well as their inherent complexity. Instead, the new approach of determining a performance indicator for each individual machine operator now allows for the linear standardisation of any productivity model. By multiplying the standardised model with the performance indicator of a particular operator the probable performance to be expected of this operator can be calculated. This value can be determined in various ways. Based on this study, the test course value can be recommended, which was explicitly developed for fast and simple assessment of operator performance and achieves an accuracy of ±10%. The study results show that under favourable environmental conditions and on easy terrain the influence of the machine operator on overall performance amounts to 37%. The human factor is thus second only to the average volume of harvested trees (46%) with respect to influencing performance. The performance of an individual harvester operator is not constant, but changes over time (learning curve). The measured rate of learning largely differed between operators. The initial training of an inexperienced harvester operator took on average nine months, which corresponds to a performance deficiency of approx. 24% throughout this time period. Hitherto developed models of learning curves were corroborated by the study results. Performance levels following the initial training period tended to remain constant over time, but were characterised by large error margins. The evaluation and consideration of operator performance presents a forest management tool which allows for future customization of existing productivity models of harvesting machinery to the specific conditions and economics of any individual forest enterprise. The extrapolation of the presented approach towards quantification and integration of machine operator performance to other human-operated forest machinery needs to be investigated in subsequent studies.

Piezoelectric energy harvester has become a new powering choice for small electronic device. Due to its piezoelectric effect, electric energy can be obtained from ambient vibrations. This thesis is intending to build a frequency-adjustable piezoelectric energy harvester system. The system is structured with two piezoelectric bimorph beams, which are connected to each other by a spring. The feasibility of the frequency-adjustable piezoelectric energy harvester has been proved by investigating effects of the spring, loading mass and impedance on the operation frequencies.

The research concerns the design and implementation of mechatronic systems to assist in the operation and control of a sugar cane harvester. Two functions were chosen for attention, the primary separation system, and the ‘topper’ that discards the leafy crown. Although these operations are given low priority by the operator of the harvester, their optimisation is of particular significance to the industry. Optimum separation requires a fine balance between discarding ‘trash’ that would contaminate the quality of the cane billets and losing good sugar-bearing material through over cleaning. Poor control of the topper can create extra load for the separation system and cause it to operate at a low efficiency with high loss. Alternatively it can cause a length of sugar-bearing cane stalk to be lost before it even enters the harvester system at all. A variety of mechatronic techniques were explored, that addressed the problem of providing useful data directly from the harvester functions and the electronic instrumentation to allow the data to be collected in a useful form in real-time. Computer control issues were also investigated, to make best use of the data stream. Novel acoustic transducers were introduced to the sensory separation system to provide a signal that indicated material striking the fan blades. A rotary transformer was required to allow transmission of the signal, and a signal interface system was implemented to record the returned data. Many real-time time-series analyses were conducted, and from these a suitable algorithm to extract an impact signal was developed. This system was assessed under harvesting conditions with results that confirmed its ability to quantify the amount of cane lost from the harvest. An investigation was conducted to detect the optimum topping height on a sugar cane stalk. The techniques considered both the internal and external attributes of the stalk, and a method was selected to measure the sugar concentration with a chemical sensor. An important design parameter was that the sensor must operate on the harvester in real time. The novel refractometer worked well in laboratory conditions, yielding repeatable and accurate results. The field environment complicated the application of this system, however this was partly overcome with introduction of a custom sample-crushing mechanism. This device provided the necessary juice sample from a selection of the topped cane stalks. The complete sampling and measuring mechanism operated well on cane stalks, and returned encouraging results. Both sets of data returned useful information regarding the operation of the particular harvester operations. The control of either the separation system or the topper requires careful balancing, and novel control techniques that consider the ergonomics for the operator are discussed. These include visual indication devices through to automatic control algorithms. With the integration of mechatronic techniques into the functioning of the sugar cane harvester, the overall efficiency of many of its functions may be improved, and the operator’s task may be greatly simplified. The ultimate objective is to maximise the yield with an improved level of harvested and separated cane.

Wireless micro-sensors can enjoy popularity in biomedical drug-delivery treatments and tire-pressure monitoring systems because they offer in-situ, real-time, non-intrusive processing capabilities. However, miniaturized platforms severely limit the energy of onboard batteries and shorten the lifespan of electronic systems. Ambient energy is an attractive alternative because the energy from light, heat, radio-frequency (RF) radiation, and motion can potentially be used to continuously replenish an exhaustible reservoir. Of these sources, solar light produces the highest power density, except when supplied from indoor lighting, under which conditions the available power decreases drastically. Harnessing thermal energy is viable, but micro-scale dimensions severely limit temperature gradients, the fundamental mechanism from which thermo piles draw power. Mobile electronic devices today radiate plenty of RF energy, but still, the available power rapidly drops with distance. Harvesting kinetic energy may not compete with solar power, but in contrast to indoor lighting, thermal, and RF sources, moderate and consistent vibration power across a vast range of applications is typical. Although operating conditions ultimately determine which kinetic energy-harvesting method is optimal, piezoelectric transducers are relatively mature and produce comparatively more power than their counterparts such as electrostatic and electromagnetic kinetic energy transducers. The presented research objective is to develop, design, simulate, fabricate, prototype, test, and evaluate CMOS ICs that harvest ambient kinetic energy in periodic and non-periodic vibrations using a small piezoelectric transducer to continually replenish an energy-storage device like a capacitor or a rechargeable battery. Although vibrations in surrounding environment produce abundant energy over time, tiny transducers can harness only limited power from the energy sources, especially when mechanical stimulation is weak. To overcome this challenge, the presented piezoelectric harvesters eliminate the need for a rectifier which necessarily imposes threshold limits and additional losses in the system. More fundamentally, the presented harvesting circuits condition the transducer to convert more electrical energy for a given mechanical input by increasing the electromechanical damping force of the piezoelectric transducer. The overall aim is to acquire more power by widening the input range and improving the efficiency of the IC as well as the transducer. The presented technique in essence augments the energy density of micro-scale electronic systems by scavenging the ambient kinetic energy and extends their operational lifetime. This dissertation reports the findings acquired throughout the investigation. The first chapter introduces the applications and challenges of micro-scale energy harvesting and also reviews the fundamental mechanisms and recent developments of various energy-converting transducers that can harness ambient energy in light, heat, RF radiation, and vibrations. Chapter 2 examines various existing piezoelectric harvesting circuits, which mostly adopt bridge rectifiers as their core. Chapter 3 then introduces a bridge-free piezoelectric harvester circuit that employs a switched-inductor power stage to eliminate the need for a bridge rectifier and its drawbacks. More importantly, the harvester strengthens the electrical damping force of the piezoelectric device and increases the output power of the harvester. The chapter also presents the details of the integrated-circuit (IC) implementation and the experimental results of the prototyped harvester to corroborate and clarify the bridge-free harvester operation. One of the major discoveries from the first harvester prototype is the fact that the harvester circuit can condition the piezoelectric transducer to strengthen its electrical damping force and increase the output power of the harvester. As such, Chapter 4 discusses various energy-investment strategies that increase the electrical damping force of the transducer. The chapter presents, evaluates, and compares several switched-inductor harvester circuits against each other. Based on the investigation in Chapter 4, an energy-investing piezoelectric harvester was designed and experimentally evaluated to confirm the effectiveness of the investing scheme. Chapter 5 explains the details of the IC design and the measurement results of the prototyped energy-investing piezoelectric harvester. Finally, Chapter 6 concludes the dissertation by revisiting the challenges of miniaturized piezoelectric energy harvesters and by summarizing the fundamental contributions of the research. With the same importance as with the achievements of the investigation, the last chapter lists the technological limits that bound the performance of the proposed harvesters and briefly presents perspectives from the other side of the research boundary for future investigations of micro-scale piezoelectric energy harvesting.

Small and efficient energy harvesters, as a renewable power supply, draw lots of attention in last few years. This thesis presents a planar rotary electromagnetic generator with copper coils fabricated by printed circuit board (PCB) as inductance and Nd-Fe-B magnets as magnetic member. Coils are fabricated on PCB, which is presumably cost-effective and promising methods. 28-pole Nd-Fe-B magnets with outer diameter of 50 mm and thickness of 2 mm was sintered and magnetized, which can provide magnetic field of 1.4 Tesla. This harvester consists of planar multilayer with multi-pole coils and multi-pole permanent magnet, and the volume of this harvester is about 50x50x2.5 mm3. Finite element analysis is used to design energy harvesting system, and simulation model of the energy harvester is established. In order to confirm the simulation, experiment data are compared with simulation result. The PCB energy harvester prototype can generate induced voltage 1.11 V and 26.54mW output power at rotary speed of 4,000 rpm, and the efficiency of this energy harvester is 31.5%.

Wireless micro-sensors can enjoy popularity in biomedical drug-delivery treatments and tire-pressure monitoring systems because they offer in-situ, real-time, non-intrusive processing capabilities. However, miniaturized platforms severely limit the energy of onboard batteries and shorten the lifespan of electronic systems. Ambient energy is an attractive alternative because the energy from light, heat, radio-frequency (RF) radiation, and motion can potentially be used to continuously replenish an exhaustible reservoir. Of these sources, solar light produces the highest power density, except when supplied from indoor lighting, under which conditions the available power decreases drastically. Harnessing thermal energy is viable, but micro-scale dimensions severely limit temperature gradients, the fundamental mechanism from which thermo piles draw power. Mobile electronic devices today radiate plenty of RF energy, but still, the available power rapidly drops with distance. Harvesting kinetic energy may not compete with solar power, but in contrast to indoor lighting, thermal, and RF sources, moderate and consistent vibration power across a vast range of applications is typical. Although operating conditions ultimately determine which kinetic energy-harvesting method is optimal, piezoelectric transducers are relatively mature and produce comparatively more power than their counterparts such as electrostatic and electromagnetic kinetic energy transducers. The presented research objective is to develop, design, simulate, fabricate, prototype, test, and evaluate CMOS ICs that harvest ambient kinetic energy in periodic and non-periodic vibrations using a small piezoelectric transducer to continually replenish an energy-storage device like a capacitor or a rechargeable battery. Although vibrations in surrounding environment produce abundant energy over time, tiny transducers can harness only limited power from the energy sources, especially when mechanical stimulation is weak. To overcome this challenge, the presented piezoelectric harvesters eliminate the need for a rectifier which necessarily imposes threshold limits and additional losses in the system. More fundamentally, the presented harvesting circuits condition the transducer to convert more electrical energy for a given mechanical input by increasing the electromechanical damping force of the piezoelectric transducer. The overall aim is to acquire more power by widening the input range and improving the efficiency of the IC as well as the transducer. The presented technique in essence augments the energy density of micro-scale electronic systems by scavenging the ambient kinetic energy and extends their operational lifetime. This dissertation reports the findings acquired throughout the investigation. The first chapter introduces the applications and challenges of micro-scale energy harvesting and also reviews the fundamental mechanisms and recent developments of various energy-converting transducers that can harness ambient energy in light, heat, RF radiation, and vibrations. Chapter 2 examines various existing piezoelectric harvesting circuits, which mostly adopt bridge rectifiers as their core. Chapter 3 then introduces a bridge-free piezoelectric harvester circuit that employs a switched-inductor power stage to eliminate the need for a bridge rectifier and its drawbacks. More importantly, the harvester strengthens the electrical damping force of the piezoelectric device and increases the output power of the harvester. The chapter also presents the details of the integrated-circuit (IC) implementation and the experimental results of the prototyped harvester to corroborate and clarify the bridge-free harvester operation. One of the major discoveries from the first harvester prototype is the fact that the harvester circuit can condition the piezoelectric transducer to strengthen its electrical damping force and increase the output power of the harvester. As such, Chapter 4 discusses various energy-investment strategies that increase the electrical damping force of the transducer. The chapter presents, evaluates, and compares several switched-inductor harvester circuits against each other. Based on the investigation in Chapter 4, an energy-investing piezoelectric harvester was designed and experimentally evaluated to confirm the effectiveness of the investing scheme. Chapter 5 explains the details of the IC design and the measurement results of the prototyped energy-investing piezoelectric harvester. Finally, Chapter 6 concludes the dissertation by revisiting the challenges of miniaturized piezoelectric energy harvesters and by summarizing the fundamental contributions of the research. With the same importance as with the achievements of the investigation, the last chapter lists the technological limits that bound the performance of the proposed harvesters and briefly presents perspectives from the other side of the research boundary for future investigations of micro-scale piezoelectric energy harvesting.

Produktivitätsmodelle geben Auskunft darüber, welche Leistung mit einem bestimmten Verfahren unter konkreten Rahmenbedingungen zu erwarten ist. In der Forstwirtschaft sind sie für Planungen und Kalkulationen notwendig. In bisherigen forstlichen Produktivitätsmodellen für den Maschineneinsatz wurde der Faktor Mensch, welcher einen wichtigen, bisher jedoch weitestgehend unbekannten Einfluss auf die Leistung hat, vernachlässigt. Ziel der vorliegenden Untersuchung ist es daher, den menschlichen Einfluss auf die Leistung von Harvestersystemen quantitativ zu erfassen und ihn in Produktivitätsmodelle einzubauen. Die Untersuchungsgrundlage bilden Leistungsmessungen, die mit vier verschiedenen Datenerhebungsverfahren an bis zu 32 Fahrern erfolgten. Neben einer neu entwickelten, sensorgestützten, semiautomatischen Zeitstudienmessung fand die Auswertung von summarischen Bordcomputer-Bestandesdaten über einen Zeitraum von drei Jahren Anwendung. Weiterhin erfolgte die (Weiter-)Entwicklung eines standardisierten Parcourstests, welcher auf einer Freifläche durchgeführt wird sowie eine Beurteilung der Leistung der Harvesterfahrer durch Gutachter. Alle vier Verfahren wurden miteinander verglichen und auf ihre Treffsicherheit und Aussagefähigkeit hin überprüft. Dabei konnten signifikante Korrelationen zwischen allen vier Datenerhebungsverfahren nachgewiesen werden, was eine Umrechnung untereinander grundsätzlich zulässt. Im Rahmen dieser Untersuchungen konnte die Vermutung quantitativ bestätigt werden, dass große, signifikant unterschiedliche Leistungshergaben zwischen den verschiedenen Harvesterfahrern existieren. Dies gilt nicht nur für den Vergleich zwischen so genannten „unerfahrenen“ und „erfahrenen“ Maschinenführern. Auch zwischen schon lange auf den Erntemaschinen arbeitenden Fahrern ist ein Leistungsunterschied von bis zu 80% zu verzeichnen. Somit ist die Beachtung des Parameters „Mensch“ für präzise Produktivitätsmodelle zwingend erforderlich. Der ursprüngliche Lösungsansatz, ein allgemein gültiges Produktivitätsmodell zu entwickeln, in dem der Einfluss des Menschen berücksichtigt wird, wurde aufgrund von Informationsdefiziten sowie zu starken Unterschieden und Komplexität der vorhandenen Modelle verworfen. Mit dem neu gewählten Lösungsansatz, der die Bestimmung eines Leistungswertes für jeden Fahrer vorsieht, ist es nun möglich, ein beliebig erstelltes Produktivitätsmodell linear auf ein Basisniveau zu normieren. Die Multiplikation des normierten Modells mit dem Leistungswert eines Fahrers ermöglicht es, die wahrscheinlich von ihm zu erwartende Produktivität zu berechnen. Dieser Wert kann auf verschiedene Weisen erhoben werden. Als Ergebnis dieser Arbeit kann dafür der entwickelte, schnell und einfach durchzuführende Parcourstest empfohlen werden. Er erreicht eine ungefähre Treffgenauigkeit von ±10%. Die Untersuchungen wiesen des Weiteren aus, dass beim Harvestereinsatz unter einfachen Umwelt- und Geländebedingungen der Fahrereinfluss auf die Produktivität bei 37% liegt. Er stellt somit nach Baumvolumen des ausscheidenden Bestandes (46%) den zweitwichtigsten Einflussfaktor dar. Die Leistungshergabe des einzelnen Harvesterfahrers ist nicht immer gleich, sondern verändert sich über der Zeit (Lernkurve). Die gemessenen Lerngeschwindigkeiten variieren dabei zwischen den Harvesterfahrern sehr stark. Das Anlernen eines unerfahrenen Harvesterfahrers dauerte bei den untersuchten Probanden im Mittel neun Monate, was einer Minderleistung von ca. 24% über diesen Zeitraum entspricht. Die bisherigen angenommenen Modelle der Lernkurve konnten bestätigt werden. Darüber hinaus zeigten sich Tendenzen, dass die Leistung der Fahrer nach der Lernphase ein konstantes Leistungsniveau erreicht, allerdings bei einer hohen Fehlerabweichung. Durch die Beurteilung und Berücksichtigung der Leistungen der Fahrer steht der Forstwirtschaft ein Werkzeug zur Verfügung, mit dem man in Zukunft die erstellten Produktivitätsmodelle für den Harvestereinsatz genauer auf die jeweiligen individuellen Gegebenheiten des eigenen Betriebes anpassen kann. Die Übertragung des gewählten Lösungsansatzes auf weitere Forstmaschinen, die von Menschen bedient werden, sollte geprüft werden. / Productivity models provide information about the expected performance of a given procedure under specific conditions. In forestry, they are essential to planning and cost estimation. In hitherto existing productivity models of forest machinery, however, the human being as an important yet mostly unknown factor influencing productivity has been disregarded. Therefore, the objectives of this study are the quantification of the human impact on the performance of forest harvesting systems and its integration into productivity models. The study is based on performance measurements collected using four different methods of data acquisition to monitor up to 32 machine operators: (i) a newly developed sensor-based semi-automatic time study, (ii) the extraction of on-board computer data accumulated over a 3-year period, (iii) an improved standardized machinery test course conducted in an open area, and (iv) the expert evaluation of operator performance. The four data acquisition methods were compared and tested for data accuracy and informational value. All correlations between data yielded by each of the four methods were significant, thus in principle allowing for data conversion between data rendered by different acquisition methods. The results of this study quantitatively corroborate the presumption of major, significantly different performances between harvester operators. This holds true not only for the comparison of so-called ‘inexperienced’ and ‘experienced’ machine operators, but also when exclusively comparing experienced operators with each other. A performance difference of up to 80% has been observed between individuals featuring long-term experience in operating harvesting machinery. The integration of the parameter ‘human being’ is thus an imperative for precise productivity models. The original aim of developing a general productivity model accounting for human influence had to be abandoned due to an information deficit, substantial differences between the existing models as well as their inherent complexity. Instead, the new approach of determining a performance indicator for each individual machine operator now allows for the linear standardisation of any productivity model. By multiplying the standardised model with the performance indicator of a particular operator the probable performance to be expected of this operator can be calculated. This value can be determined in various ways. Based on this study, the test course value can be recommended, which was explicitly developed for fast and simple assessment of operator performance and achieves an accuracy of ±10%. The study results show that under favourable environmental conditions and on easy terrain the influence of the machine operator on overall performance amounts to 37%. The human factor is thus second only to the average volume of harvested trees (46%) with respect to influencing performance. The performance of an individual harvester operator is not constant, but changes over time (learning curve). The measured rate of learning largely differed between operators. The initial training of an inexperienced harvester operator took on average nine months, which corresponds to a performance deficiency of approx. 24% throughout this time period. Hitherto developed models of learning curves were corroborated by the study results. Performance levels following the initial training period tended to remain constant over time, but were characterised by large error margins. The evaluation and consideration of operator performance presents a forest management tool which allows for future customization of existing productivity models of harvesting machinery to the specific conditions and economics of any individual forest enterprise. The extrapolation of the presented approach towards quantification and integration of machine operator performance to other human-operated forest machinery needs to be investigated in subsequent studies.

Made available in DSpace on 2014-06-11T19:24:43Z (GMT). No. of bitstreams: 0 Previous issue date: 2010-08-24Bitstream added on 2014-06-13T19:11:19Z : No. of bitstreams: 1 bertin_vas_me_botfca.pdf: 1651764 bytes, checksum: 2034af146a88f8f514f9c86d1c3d50e9 (MD5) / A mecanização das operações tem proporcionado aumento da capacidade operacional nas diversas atividades florestais. A caracterização desse potencial técnico para a produção da madeira ao longo da cadeia produtiva é importante. Todo sistema de colheita florestal seja ele mecanizado ou não, requer uma avaliação de rendimentos para uma análise eficiente das viabilidades técnicas e econômicas. A comparação e o estudo dos rendimentos nos processos de colheita florestal são importantes para a determinação de um sistema rentável, de maior eficiência e minimização dos custos, otimizando a produção e privilegiando a qualidade final do produto através de um sistema cada vez mais mecanizado, automatizado e auto-suficiente. O objetivo deste estudo foi comparar dois modais de sistemas de colheita florestal mecanizada, Harvester e Feller-Buncher + Processador Florestal, de modo a apresentar qual destes possui maior eficiência quanto a produtividade. Conclui-se que trabalhando nas mesmas condições, o conjunto Feller-Buncher + Processador Florestal demonstrou rendimento operacional efetivo superior, sendo desta forma o mais indicado para colheita florestal do eucalipto em primeira rotação / The mechanization of the operations has provided increased operational capacity in the various forest activities. The characterization of this technical potential for timber manufacturing throughout the productive chain is of great importance. All forest harvesting systems, mechanized or not, requires a performance assessment for an efficient analysis of the technical and economical viabilities. The comparison and the study of the performance in the forest harvesting processes are extremely important in order to determine a profitable system, with more efficiency and minimizing the costs, optimizing the production and privileging the product final quality through a system even more mechanized, automated and self-sufficient. The aim of this study was to compare two modes of mechanical harvesting systems, Harvester and Feller-Buncher + Processor Forest, to display which of these has greater efficiency for yield. The conclusion is that working under the same conditions, the modal Feller-Buncher + Processor Forest proved superior effective operational performance, therefore, the most indicated for first rotation harvesting of eucalyptus

Sufficient power supply to run GPS machinery and transmit data on a long-term basis remains to be the key challenge for wildlife tracking technology. Traditional ways of replacing battery periodically is not only time and money consuming but also dangerous to live-trapping wild animals. In this paper, an innovative wildlife tracking collar with multi-source energy harvester with advantage of high efficiency and reliability is proposed. This multi-source energy harvester entails a solar energy harvester and an innovative rotational electromagnetic energy harvester is mounted on the "wildlife tracking collar" which will extend the duration of wild life tracking by 20% time as was estimated. A feedforward and feedback control of DC-DC converter circuit is adopted to passively realize the Maximum Power Point Tracking (MPPT) logic for the solar energy harvester. A novel electromagnetic pendulum energy harvester with motion regulator is proposed which can mechanically rectify the irregular bidirectional swing motion of the pendulum into unidirectional rotational motion of the motor. No electrical rectifier is needed and voltage drops from diodes can be avoided, the EM pendulum energy harvester can provide 200~300 mW under the 0.4g base excitation of 4.5 Hz. The nonlinearity of the disengage mechanism in the pendulum energy harvester will lead to a broad bandwidth frequency response. Simulation results shows the broadband advantage of the proposed energy harvester and experiment results verified that at some frequencies over the natural frequency the efficiency is increased. / Master of Science