Study on wearable device users’ willingness to continue using—ECM-IS based on the expansion model

Based on the Expectation Confirmation Model of Information System(ECM-IS), three personal characteristic factors of self-efficacy, privacy concerns, and innovation as well as two external environmental factors of subjective reference and switching costs were introduced to construct a model of factors affecting users’ continuance intention of wearable devices from the perspective of “technology-individual-environment”. 356 valid samples were collected through the questionnaire for empirical analysis. The results of the study show that self-efficacy, switching costs, and perceived usefulness in the ECM-IS model have a significant effect on users’ continuance intention at p<0.001 level while innovativeness and subjective references affect users’ continuance intention at p<0.05, but privacy concerns have no effect on continuance intention

Motor Noise and Vibration Test Research

Some factors, such as friction, vibration, and so on, can result in the fault and abnormal noise in the motor. Based on the detection and analysis of noise and vibration, we can identify and eliminate the faults of the motor. This is helpful not only to ensure the completion of production tasks, but also to prevent accidents. In this paper, we briefly introduce the motor noise generation principle. A laptop computer and LabVIEW software are used to design the experiment system to detect and analysis the noise and vibration of motor. External microphone and computer with sound card constitute noise detection system hardware. Vibration sensor and the data acquisition card constitute vibration detection system hardware. LabVIEW software combined with FFT analysis is used to realize the noise signal acquisition, recording and spectral analysis. Detecting and analyzing the noise of the permanent magnet DC motor and three-phase asynchronous motor proves that the motor noise and vibration detecting experimental platform is fully meet the requirements of motor test and research. This detection and analysis system has a good man-machine interface and strong operability

Remarkable Effect of PEG-1000-based Dicationic Ionic Liquid for N-hydroxyphthalimide-catalyzed Aerobic Selective Oxidation of Alkylaromatics

PEG 1000–based functional dicationic acidic ionic liquid (PEG1000 –DAIL) was used for the first time as the reaction solvent for the N-Hydroxyphthalimide (NHPI)-cobalt acetate(Co(OAc)2) catalyzed aerobic oxidations of alkylaromatics to the corresponding acids. It enhanced the efficient catalytic ability of NHPI: 99.9 % conversion of toluene with 99.5 % selectivity for benzoic acid could be obtained at 80 °C in 10 h and ethylbenzene was selectively oxidized to benzoic acid. Several alkylaromatics were efficiently oxidized to their corresponding acids under mild conditions. For substituted toluene, the conversions of substrates and the selectivity of products was affected by the position and kind of substituted groups, respectively. Both the catalyst and PEG1000 –DAIL could be reused at least eight times without significantly decreasing the catalytic activity.(doi: 10.5562/cca2051

Pattern search for the visualization of scalar, vector, and line fields

The main topic of this thesis is pattern search in data sets for the purpose of visual data analysis. By giving a reference pattern, pattern search aims to discover similar occurrences in a data set with invariance to translation, rotation and scaling. To address this problem, we developed algorithms dealing with different types of data: scalar fields, vector fields, and line fields. For scalar fields, we use the SIFT algorithm (Scale-Invariant Feature Transform) to find a sparse sampling of prominent features in the data with invariance to translation, rotation, and scaling. Then, the user can define a pattern as a set of SIFT features by e.g. brushing a region of interest. Finally, we locate and rank matching patterns in the entire data set. Due to the sparsity and accuracy of SIFT features, we achieve fast and memory-saving pattern query in large scale scalar fields. For vector fields, we propose a hashing strategy in scale space to accelerate the convolution-based pattern query. We encode the local flow behavior in scale space using a sequence of hierarchical base descriptors, which are pre-computed and hashed into a number of hash tables. This ensures a fast fetching of similar occurrences in the flow and requires only a constant number of table lookups. For line fields, we present a stream line segmentation algorithm to split long stream lines into globally-consistent segments, which provides similar segmentations for similar flow structures. It gives the benefit of isolating a pattern from long and dense stream lines, so that our patterns can be defined sparsely and have a significant extent, i.e., they are integration-based and not local. This allows for a greater flexibility in defining features of interest. For user-defined patterns of curve segments, our algorithm finds similar ones that are invariant to similarity transformations. Additionally, we present a method for shape recovery from multiple views. This semi-automatic method fits a template mesh to high-resolution normal data. In contrast to existing 3D reconstruction approaches, we accelerate the data acquisition time by omitting the structured light scanning step of obtaining low frequency 3D information.Das Hauptthema dieser Arbeit ist die Mustersuche in Datensätzen zur visuellen Datenanalyse. Durch die Vorgabe eines Referenzmusters versucht die Mustersuche ähnliche Vorkommen in einem Datensatz mit Translations-, Rotations- und Skalierungsinvarianz zu entdecken. In diesem Zusammenhang haben wir Algorithmen entwickelt, die sich mit verschiedenen Arten von Daten befassen: Skalarfelder, Vektorfelder und Linienfelder. Bei Skalarfeldern benutzen wir den SIFT-Algorithmus (Scale-Invariant Feature Transform), um ein spärliches Abtasten von markanten Merkmalen in Daten mit Translations-, Rotations- und Skalierungsinvarianz zu finden. Danach kann der Benutzer ein Muster als Menge von SIFT-Merkmalspunkten definieren, zum Beispiel durch Markieren einer interessierenden Region. Schließlich lokalisieren wir passende Muster im gesamten Datensatz und stufen sie ein. Aufgrund der spärlichen Verteilung und der Genauigkeit von SIFT-Merkmalspunkten erreichen wir eine schnelle und speichersparende Musterabfrage in großen Skalarfeldern. Für Vektorfelder schlagen wir eine Hashing-Strategie zur Beschleunigung der faltungsbasierten Musterabfrage im Skalenraum vor. Wir kodieren das lokale Flussverhalten im Skalenraum durch eine Sequenz von hierarchischen Basisdeskriptoren, welche vorberechnet und als Zahlen in einer Hashtabelle gespeichert sind. Dies stellt eine schnelle Abfrage von ähnlichen Vorkommen im Fluss sicher und benötigt lediglich eine konstante Anzahl von Nachschlageoperationen in der Tabelle. Für Linienfelder präsentieren wir einen Algorithmus zur Segmentierung von Stromlinien, um lange Stromlinen in global konsistente Segmente aufzuteilen. Dies erlaubt eine größere Flexibilität bei der Definition von Mustern. Für vom Benutzer definierte Muster von Kurvensegmenten findet unser Algorithmus ähnliche Kurvensegmente, die unter Ähnlichkeitstransformationen invariant sind. Zusätzlich präsentieren wir eine Methode zur Rekonstruktion von Formen aus mehreren Ansichten. Diese halbautomatische Methode passt ein Template an hochauflösendeNormalendatenan. Im Gegensatz zu existierenden 3D-Rekonstruktionsverfahren beschleunigen wir die Datenaufnahme, indem wir auf die Streifenprojektion verzichten, um niederfrequente 3D Informationen zu gewinnen