2,963 research outputs found
Applying the superior identification group linguistic variable to construct kano model oriented quality function deployment
This study attempts to manipulate 2-tuple linguistic variables rather than pure linguistic variables in quality function deployment (QFD) in order to significantly improve the identification of the QFD model. The Kano model, a two-dimensional quality technique, is also integrated to recognize the degree of urgency in terms of enhancing and prioritizing quality-related requirements of customers via a fuzzy linguistic quantifier with a soft majority concept to fit the optimal aggregation weights. This study also retains the goodness on the usage of multi-granularity linguistic approach to facilitate the implementation of a group decision. Simultaneously, two-dimensional analysis is performed to explain the results synthetically between relationship matrix and correlation matrix from a management perspective, capable of providing comprehensive information for the decision process. Owing to the integration of several quality and management methods, results of this study demonstrate the capability of TRIZ
What do they eat? A survey of eat-out habit of university students in Taiwan
[EN] Main purpose of this research is trying to understand food likeliness of
Taiwan college students, and probe whether these food are healthy. Three
survey steps are taken as: step 1, market survey for what kind of foods are
selling around the campuses; step 2, questionnaire investigation for students
food preference; step 3, analyzing whether these favorite foods are healthy or
not. The result shows: major consideration for students food selection are
“taste” and “price”; 63% of students are taking food or snacks late at night
at least once a week. Top three most favorite foods are: Taiwanese fries (yan
su ji), carbon grilled chicken and fried fish steaks. Quantities of these foods
are small, prices are low, and easy access from roadside food stands.
Problems of them are high calories, easy to accumulate free radical in
human body, plus insanitary food processing environment. They are harmful
to student health. We suggest Taiwan government take it seriouslyShih, K.; Wang, M.; Shih, H.; Lee, S.; Lin, T. (2020). What do they eat? A survey of eat-out habit of university students in Taiwan. Editorial Universitat Politècnica de València. 421-430. https://doi.org/10.4995/INN2019.2019.10562OCS42143
RS2G: Data-Driven Scene-Graph Extraction and Embedding for Robust Autonomous Perception and Scenario Understanding
Human drivers naturally reason about interactions between road users to
understand and safely navigate through traffic. Thus, developing autonomous
vehicles necessitates the ability to mimic such knowledge and model
interactions between road users to understand and navigate unpredictable,
dynamic environments. However, since real-world scenarios often differ from
training datasets, effectively modeling the behavior of various road users in
an environment remains a significant research challenge. This reality
necessitates models that generalize to a broad range of domains and explicitly
model interactions between road users and the environment to improve scenario
understanding. Graph learning methods address this problem by modeling
interactions using graph representations of scenarios. However, existing
methods cannot effectively transfer knowledge gained from the training domain
to real-world scenarios. This constraint is caused by the domain-specific rules
used for graph extraction that can vary in effectiveness across domains,
limiting generalization ability. To address these limitations, we propose
RoadScene2Graph (RS2G): a data-driven graph extraction and modeling approach
that learns to extract the best graph representation of a road scene for
solving autonomous scene understanding tasks. We show that RS2G enables better
performance at subjective risk assessment than rule-based graph extraction
methods and deep-learning-based models. RS2G also improves generalization and
Sim2Real transfer learning, which denotes the ability to transfer knowledge
gained from simulation datasets to unseen real-world scenarios. We also present
ablation studies showing how RS2G produces a more useful graph representation
for downstream classifiers. Finally, we show how RS2G can identify the relative
importance of rule-based graph edges and enables intelligent graph sparsity
tuning
Preparing random state for quantum financing with quantum walks
In recent years, there has been an emerging trend of combining two
innovations in computer science and physics to achieve better computation
capability. Exploring the potential of quantum computation to achieve highly
efficient performance in various tasks is a vital development in engineering
and a valuable question in sciences, as it has a significant potential to
provide exponential speedups for technologically complex problems that are
specifically advantageous to quantum computers. However, one key issue in
unleashing this potential is constructing an efficient approach to load
classical data into quantum states that can be executed by quantum computers or
quantum simulators on classical hardware. Therefore, the split-step quantum
walks (SSQW) algorithm was proposed to address this limitation. We facilitate
SSQW to design parameterized quantum circuits (PQC) that can generate
probability distributions and optimize the parameters to achieve the desired
distribution using a variational solver. A practical example of implementing
SSQW using Qiskit has been released as open-source software. Showing its
potential as a promising method for generating desired probability amplitude
distributions highlights the potential application of SSQW in option pricing
through quantum simulation.Comment: 11 pages, 7 figure
High efficiency silicon nanodisk laser based on colloidal CdSe/ZnS QDs
Using colloidal CdSe/ZnS quantum dots in the submicron-sized silicon disk cavity, we have developed a visible wavelength nanodisk laser that operates under extremely low threshold power at room temperature. Time-resolved photoluminescence (PL) of QDs; nanodisk by e-beam lithography. Observation of lasing action at 594 nm wavelength for quantum dots on a nanodisk (750 nm in diameter) cavity and an ultra-low threshold of 2.8 µW. From QD concentration dependence studies we achieved nearly sevenfold increase in spontaneous emission (SE) rate. We have achieved high efficient and high SE coupling rate in such a QD nanodisk laser
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