1,721,033 research outputs found
Power Systems & Smart Energies Advances in systems, signals and devices ;, 11./ edited by Faouzi Derbel, Nabil Derbel, Olfa Kanoun
No full textThe book presents selected, extended and peer reviewed papers from the International Multiconference on System, Automation and Control held Leipzig in 2016. These are complemented with solicited contributions by international experts. This volume is devoted to power electronics in renewable energy systems as well as to hybrid renewable energy systemsLazim, M.T. / Al-Faoury, A. -- Ponce, E. / Benadero, L. / El Aroudi, A. -- Ayed, R. Ben -- Alfaoury, A. / Agha, A. -- Al-Faoury, A. / Lazim, M.T. / Al-Khashashna, H. -- Loukil, J. / Masmoudi, F. / Derbel, N. -- Sdiri, F. / Bensalem, Y. / Trabelsi, H. / Abdelkrim, M. -- Mars, R. / Bouzidi, B. / El Badsi, B. / Yangui, A. Frontmatter -- Contents -- Multi-phase Current Source Cycloconverter Using Amplitude Modulation Technique / Bifurcation Analysis in a Self-Oscillating Series Resonant Converter / Optimal design targeting the environmental footprint reduction of an electrical railway subsystem / Long -- Term Energy Losses Analyses For Jordanian Power system (Commercial Losses) / A Novel Dynamics Approximation method for Analysis and Design of DC Drive Systems / Third order Model and Identification of Lead Acid Batteries Using Meta-Heuristic Algorithms and Experimental Measurements / A short circuit fault estimation in PMSM: A comparative study of an adaptive observer and an unknown inputs observer / DCC and DTC of Three-Level Inverter Fed Brushless DC Motor Drives with Torque Ripple Reduction /1 online resourc
Accuracy Improvement of Predictive Neural Networks for Managing Energy in Solar Powered Wireless Sensor Nodes
Get PDFDas drahtlose Sensornetzwerk (WSN) ist eine Technologie, die Umgebungsbedingungen oder physikalische Parameter misst, weiterleitet und per Fernüberwachung zur Verfügung stellt. Normalerweise werden die Sensorknoten, die diese Netzwerke bilden, von Batterien gespeist. Diese sollen aus verschiedenen Gründen nicht mehr verwendet werden, sondern es wird auf eine eigenständige Stromversorgung gesetzt. Dies soll den aufwendigen Austausch und die Wartung minimieren. Energy Harvesting kann mit den Knoten verwendet werden, um die Batterien zu unterstützen und die Lebensdauer der Netzwerke zu verlängern.
Aufgrund der hohen Leistungsdichte der Solarenergie im Vergleich zu verschiedenen anderen Umweltenergien sind Solarzellen die am häufigsten eingesetzten Wandler, allerdings stellt die schwankende und intermittierende Natur der Solarenergie eine Herausforderung dar, einen funktionalen und zuverlässigen Sensorknoten zu versorgen.
Um den Sensorknoten effektiv zu betreiben, sollte sein Energieverbrauch sinnvoll gesteuert werden. Ein interessanter Ansatz zu diesem Zweck ist die Steuerung der Aktivitäten des Knotens in Abhängigkeit von der zukünftig verfügbaren Energie. Dies erfordert eine Vorhersage der wandelbaren Sonnenenergie für die kommenden Betriebszeiten einschließlich der freien Zeiten der Sonne. Einige Vorhersagealgorithmen wurden mit stochastischen und statistischen Prinzipien sowie mit Methoden der künstlichen Intelligenz (KI) erstellt. Durch diese Algorithmen bleibt ein erheblicher Vorhersagefehler von 5-70%, der den zuverlässigen Betrieb der Knoten beeinträchtigt. Beispielsweise verwenden die stochastischen Methoden einen diskreten Energiezustand, der meist nicht zu den tatsächlichen Messwerten passt. Die statistischen Methoden verwenden einen Gewichtungsfaktor für die zuvor registrierten Messwerte. Daher sind sie nur geeignet, um Energieprofile bei konstanten Wetterbedingungen vorherzusagen. KI-Methoden erfordern große Beobachtungen im Trainingsprozess, die den benötigten Speicherplatz erhöhen. Dementsprechend ist die Leistung hinsichtlich der Vorhersagegenauigkeit dieser Algorithmen nicht ausreichend.
In dieser Arbeit wird ein Vorhersagealgorithmus mit einem neuronalen Netzwerk entwickelt und eingebunden in einen Mikrocontroller, um die Verwaltung des Energieverbrauchs von solarzellengesteuerten Sensorknoten zu optimieren. Das verwendete neuronale Netzwerk wurde mit einer Kombination aus meteorologischen und statistischen Eingangsparametern realisiert. Dies hat zum Ziel, die erforderlichen Designkriterien für Sensorknoten zu erfüllen und eine Leistung zu erreichen, die in ihrer Genauigkeit die Leistung der oben genannten traditionellen Algorithmen übersteigt. Die Vorhersagegenauigkeit die durch den Korrelationskoeffizienten repräsentiert wird, wurde für das entwickelte neuronale Netzwerk auf 0,992 bestimmt. Das genaueste traditionelle Netzwerk erreicht nur einen Wert von 0,963.
Das entwickelte neuronale Netzwerk wurde in einen Prototyp eines Sensorknotens integriert, um die Betriebszustände oder -modi über einen Simulationszeitraum von einer Woche anzupassen. Während dieser Zeit hat der Sensorknoten 6 Stunden zusätzlich im Normalbetrieb gearbeitet. Dies trug dazu bei, eine effektive Nutzung der verfügbaren Energie um ca. 3,6% besser zu erfüllen als das genaueste traditionelle Netz. Dadurch wird eine längere Lebensdauer und Zuverlässigkeit des Sensorknotens erreicht.Wireless Sensor Network (WSN) is a technology that measures an environmental or physical parameters in order to use them by decision makers with a possibility of remote monitoring. Normally, sensor nodes that compose these networks are powered by batteries which are no longer feasible, especially when they used as fixed and standalone power source. This is due to the costly replacement and maintenance. Ambient energy harvesting systems can be used with these nodes to support the batteries and to prolong the lifetime of these networks.
Due to the high power density of solar energy in comparison with different environmental energies, solar cells are the most utilized harvesting systems. Although that, the fluctuating and intermittent nature of solar energy causes a real challenge against fulfilling a functional and reliable sensor node.
In order to operate the sensor node effectively, its energy consumption should be well managed. One interesting approach for this purpose is to control the future node’s activities according to the prospective energy available. This requires performing a prior prediction of the harvestable solar energy for the upcoming operation periods including the sun’s free times. A few prediction algorithms have been created using stochastic and statistical principles as well as artificial intelligence (AI) methods. A considerable prediction error of 5-70% is realized by these algorithms affecting the reliable operation of the nodes. For example, the stochastic ones use a discrete energy states which are mostly do not fit the actual readings. The statistical methods use a weighting factors for the previous registered readings. Thus, they are convenient only to predict energy profiles under consistent weather conditions. AI methods require large observations to be used in the training process which increase the memory space needed. Accordingly, the performance concerning the prediction accuracy of these algorithms is not sufficient.
In this thesis, a prediction algorithm using a neural network has been proposed and implemented in a microcontroller for managing energy consumption of solar cell driven sensor nodes. The utilized neural network has been developed using a combination of meteorological and statistical input parameters. This is to meet a required design criteria for the sensor nodes and to fulfill a performance exceeds in its accuracy the performance of aforementioned traditional algorithms. The prediction accuracy represented by the correlation coefficient has been registered for the developed neural network to be 0.992, which increases the most accurate traditional network which has a value 0.963.
The developed neural network has been embedded into a sensor node prototype to adjust the operating states or modes over a simulation period of one week. During this period, the sensor node has worked 6 hours more towards normal operation mode. This in its role helped to fulfill an effective use of available energy approximately 3.6% better than the most accurate traditional network. Thus, longer lifetime and more reliable sensor node
In-Vitro Biological Tissue State Monitoring based on Impedance Spectroscopy
Get PDFThe relationship between post-mortem state and changes of biological tissue impedance has been investigated to serve as a basis for developing an in-vitro measurement method for monitoring the freshness of meat. The main challenges thereby are the reproducible measurement of the impedance of biological tissues and the classification method of their type and state.
In order to realize reproducible tissue bio-impedance measurements, a suitable sensor taking into account the anisotropy of the biological tissue has been developed. It consists of cylindrical penetrating multi electrodes realizing good contacts between electrodes and the tissue. Experimental measurements have been carried out with different tissues and for a long period of time in order to monitor the state degradation with time. Measured results have been evaluated by means of the modified Fricke-Cole-Cole model. Results are reproducible and correspond to the expected behavior due to aging. An appropriate method for feature extraction and classification has been proposed using model parameters as features as input for classification using neural networks and fuzzy logic.
A Multilayer Perceptron neural network (MLP) has been proposed for muscle type computing and the age computing and respectively freshness state of the meat. The designed neural network is able to generalize and to correctly classify new testing data with a high performance index of recognition.
It reaches successful results of test equal to 100% for 972 created inputs for each muscle. An investigation of the influence of noise on the classification algorithm shows, that the MLP neural network has the ability to correctly classify the noisy testing inputs especially when the parameter noise is less than 0.6%. The success of classification is 100% for the muscles Longissimus Dorsi (LD) of beef, Semi-Membraneous (SM) of beef and Longissimus Dorsi (LD) of veal and 92.3% for the muscle Rectus Abdominis (RA) of veal.
Fuzzy logic provides a successful alternative for easy classification. Using the Gaussian membership functions for the muscle type detection and trapezoidal member function for the classifiers related to the freshness detection, fuzzy logic realized an easy method of classification and generalizes correctly the inputs to the corresponding classes with a high level of recognition equal to 100% for meat type detection and with high accuracy for freshness computing equal to 84.62% for the muscle LD beef, 92.31 % for the muscle RA beef, 100 % for the muscle SM veal and 61.54% for the muscle LD veal.
Auf der Basis von Impedanzspektroskopie wurde ein neuartiges in-vitro-Messverfahren zur Überwachung der Frische von biologischem Gewebe entwickelt. Die wichtigsten Herausforderungen stellen dabei die Reproduzierbarkeit der Impedanzmessung und die Klassifizierung der Gewebeart sowie dessen Zustands dar. Für die Reproduzierbarkeit von Impedanzmessungen an biologischen Geweben, wurde ein zylindrischer Multielektrodensensor realisiert, der die 2D-Anisotropie des Gewebes berücksichtigt und einen guten Kontakt zum Gewebe realisiert. Experimentelle Untersuchungen wurden an verschiedenen Geweben über einen längeren Zeitraum durchgeführt und mittels eines modifizierten Fricke-Cole-Cole-Modells analysiert. Die Ergebnisse sind reproduzierbar und entsprechen dem physikalisch-basierten erwarteten Verhalten. Als Merkmale für die Klassifikation wurden die Modellparameter genutzt
Investigation of Current Excitation for Personal Health and Biological Tissues Monitoring
Get PDFBioimpedance spectroscopy is very useful in biomedical field as a safe and non-invasive technique. A stable and safe excitation current below than 0.5 mA for load impedances changing from 100 Ω to 10 kΩ in the full β-dispersion range from kHz up to 1 MHz is a big challenge for the design of the current source addressed by this thesis. For a good stability and high accuracy, the source should have a high output impedance.
Different current source types in “current-mode approach” and “voltage-mode approach” were investigated and compared for usability in bioimpedance measurement systems. The “voltage-mode approach” with grounded load was proven to be more suitable and stable for biomedical measurements. Thereby the Tietze and the Howland circuit in dual configuration with negative feedback have shown the lowest error of the output current and the highest output impedance, where the improved Howland circuit in dual configuration with negative feedback is preferred because it has a simple structure, high accuracy and good stability.
We suggest to improve the stability of the Howland circuit in dual configuration with negative feedback by introducing compensated operational amplifiers and to reduce stray capacitances at higher frequencies by adding gain compensation capacitor. We reach thereby an accuracy of 0.5% at low frequency and 0.9% at 1 MHz. With the realized accuracy of the designed voltage controlled current source, one decisive prerequisite for portable bioimpedance measurement system is achieved.
In order to select the appropriate excitation signals for short measurement time, a comparative study of signals and their parameters was carried out. It leads to the selection of binary chirp signal as a suitable excitation signal due to its short measurement time about 100 μs, low crest factor lower than 2.8 and an energy efficiency higher than 54% in a very noisy signal. Simulation results show that the designed enhanced Howland current source excited by the binary chirp signal has low error and flatness in the whole range.Die Bioimpedanzspektroskopie gewinnt aufgrund ihrer besonderen Eigenschaften als nicht-invasive, schonende Messmethode zunehmend an Bedeutung im biomedizinischen Bereich. Dabei ergeben sich besondere erausforderungen für den Entwurf der Stromquelle zur Realisierung eines stabilen und sicheren Anregungsstroms. Gefordert ist eine hohe Genauigkeit bis zu einem Maximalstrom von 0.5 mA in einem Frequenzbereich, der der β-Dispersion entspricht, von wenigen kHz bis hin zu 1 MHz. Die Stabilität muss bei variablen Lastimpedanzen im Bereich von 100 Ω bis 10 kΩ gewährleistet sein. Dafür muss die Stromquelle eine hohe Ausgangsimpedanz aufweisen.
Diese Arbeit fokussiert auf den Entwurf von spannungsgesteuerten Stromquellen. Verschiedene Arten von Stromquellen wurden untersucht und verglichen. Der 'Voltage-Modus-Ansatz' mit Masse-referenzierter Last hat sich als besser geeignet und stabiler für biomedizinische Messungen erwiesen. Die Tietze-Schaltung und diese Howland-Schaltung zeigen dabei die niedrigsten Fehler des Ausgangsstroms und die höchste Ausgangsimpedanz. Im direkten Vergleich besitzt die verbesserte Howland-Schaltung doch eine einfachere Struktur, höhere Genauigkeit und bessere Stabilität und wird daher gegenüber der Tietze-Schaltung bevorzugt.
Um weitere Stabilitätsverbesserungen bei der Howland-Schaltung zu erreichen, werden zwei Maβnahmen vorgeschlagen. Zum einen werden kompensierte Operationsverstärker eingeführt und zum anderen wird der Einfluss von Streukapazitäten bei hohen Frequenzen minimiert indem die Verstärkung mit Kondensatoren kompensiert wird. Durch diese Maβnahmen wird eine Genauigkeit von 0.5% bei niedrigen Frequenzen und 0.9% bei 1 MHz ermöglicht. Mit dem neuen Entwurf der spannungsgesteuerten Stromquelle ist ein entscheidender Meilenstein für die Realisierung tragbarer Messsysteme der Bioimpedanz erreicht.
Um eine kurze Messzeit zu realisieren wurde eine vergleichende Studie von Anregungssignalen und deren Signalparameter durchgeführt. Die Ergebnisse zeigen, dass binäre Chirp-Signale aufgrund der reduzierten Messzeit, des niedrigen Crest-Faktors unter 2.8 und hohe Energieeffizienz von mehr als 54% bei hohem Rauschlevel besonders geeignet sind. Simulationsergebnisse zeigen, dass die entwickelte Howland-Stromquelle zusammen mit einem binären Mehrfrequenzsignal den geringsten Amplitudenfehler im gesamten Frequenzbereich realisiert
Hybrid Hand Sign Recognition for Real-Time Wearable Systems with Ambiguity Reduction
No full textHand sign recognition (HSR) has emerged as a significant field of research and development in the context of wearable systems and human machine interaction. The aim of this research is to investigate the potential of forearm-attached sensors to recognize hand signs and to propose a novel measurement approach for real-time HSR with reduced ambiguities. Three measurement methods are deeply investigated: Force Myography (FMG), Electrical Impedance Tomography (EIT), and surface Electromyography (EMG). The potential of these methods is evaluated in the context of American Sign Language (ASL). For a comprehensive comparative study, it is important to realize same conditions in the data collection. Therefore, a parallel data acquisition interface has been designed for simultaneous data collection. To assess the methods' capacity to distinguish between different hand signs independent of the classification algorithms, we propose a novel method for evaluating the ambiguities between different hand signs directly from the collected data. The application of this method to the collected data for all subjects shows, that EIT and FMG can better differentiate hand signs. Therefore, an FMG-EIT hybrid HSR method is proposed fusing the classification results of both methods based on their complementarity in solving ambiguous cases. The proposed method is able to achieve an average of real time accuracy of 94.16%, 82.5%, and 71.36% for the proposed fusion method, FMG and EIT respectively.:1 Introduction
2 Theoretical background on hand sign recognition
3 State of the art of hand sign recognition systems
4 Design of hand sign recognition measurement systems
5 Investigation of measurements methods
6 Hybrid FMG-EIT method for hand sign recognition
7 Conclusio
Hybrid Hand Sign Recognition for Real-Time Wearable Systems with Ambiguity Reduction
No full textHand sign recognition (HSR) has emerged as a significant field of research and development in the context of wearable systems and human machine interaction. The aim of this research is to investigate the potential of forearm-attached sensors to recognize hand signs and to propose a novel measurement approach for real-time HSR with reduced ambiguities. Three measurement methods are deeply investigated: Force Myography (FMG), Electrical Impedance Tomography (EIT), and surface Electromyography (EMG). The potential of these methods is evaluated in the context of American Sign Language (ASL). For a comprehensive comparative study, it is important to realize same conditions in the data collection. Therefore, a parallel data acquisition interface has been designed for simultaneous data collection. To assess the methods' capacity to distinguish between different hand signs independent of the classification algorithms, we propose a novel method for evaluating the ambiguities between different hand signs directly from the collected data. The application of this method to the collected data for all subjects shows, that EIT and FMG can better differentiate hand signs. Therefore, an FMG-EIT hybrid HSR method is proposed fusing the classification results of both methods based on their complementarity in solving ambiguous cases. The proposed method is able to achieve an average of real time accuracy of 94.16%, 82.5%, and 71.36% for the proposed fusion method, FMG and EIT respectively.:1 Introduction
2 Theoretical background on hand sign recognition
3 State of the art of hand sign recognition systems
4 Design of hand sign recognition measurement systems
5 Investigation of measurements methods
6 Hybrid FMG-EIT method for hand sign recognition
7 Conclusio
Hybrid Hand Sign Recognition for Real-Time Wearable Systems with Ambiguity Reduction
No full textHand sign recognition (HSR) has emerged as a significant field of research and development in the context of wearable systems and human machine interaction. The aim of this research is to investigate the potential of forearm-attached sensors to recognize hand signs and to propose a novel measurement approach for real-time HSR with reduced ambiguities. Three measurement methods are deeply investigated: Force Myography (FMG), Electrical Impedance Tomography (EIT), and surface Electromyography (EMG). The potential of these methods is evaluated in the context of American Sign Language (ASL). For a comprehensive comparative study, it is important to realize same conditions in the data collection. Therefore, a parallel data acquisition interface has been designed for simultaneous data collection. To assess the methods' capacity to distinguish between different hand signs independent of the classification algorithms, we propose a novel method for evaluating the ambiguities between different hand signs directly from the collected data. The application of this method to the collected data for all subjects shows, that EIT and FMG can better differentiate hand signs. Therefore, an FMG-EIT hybrid HSR method is proposed fusing the classification results of both methods based on their complementarity in solving ambiguous cases. The proposed method is able to achieve an average of real time accuracy of 94.16%, 82.5%, and 71.36% for the proposed fusion method, FMG and EIT respectively.:1 Introduction
2 Theoretical background on hand sign recognition
3 State of the art of hand sign recognition systems
4 Design of hand sign recognition measurement systems
5 Investigation of measurements methods
6 Hybrid FMG-EIT method for hand sign recognition
7 Conclusio
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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