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