Theoretical Framework for Design for Dynamic User Preferences

2020 ◽  
Author(s):  
Mojtaba Arezoomand ◽  
Elliott Rouse ◽  
Jesse Austin-Breneman
Keyword(s):  
New Product Development ◽  
User Satisfaction ◽  
Variable Stiffness ◽  
User Preferences ◽  
Illustrative Case ◽  
Design Strategies ◽  
Initial Information ◽  
Upper Confidence Bound ◽  
Process Length ◽  
Dynamic User Preferences

Abstract A key assumption of new product development is that user requirements and related preferences do not vary on time scales of the process length. However, prior work has identified cases in which user preferences for product attributes can vary with time. This study proposes a method, Design for Dynamic User Preferences, which adapts reinforcement learning (RL) algorithms for designing physical systems whose functionality changes with user feedback. An illustrative case comprised of the design of a variable stiffness prosthetic ankle is presented to evaluate the potential usefulness of the framework. Lifetime user satisfaction for static and dynamic design strategies are compared over simulated user preferences under a number of conditions. Results suggest RL-based strategies outperform static strategies for cases with dynamic user preferences despite significantly less initial information. Within RL methods, upper-confidence bound policies led to higher user satisfaction on average. This study suggests that further investigation into RL-based design strategies is warranted for situations with possibly dynamic preferences.

scholarly journals Assessing a Multi-Objective Genetic Algorithm with a Simulated Environment for Energy-Saving of Air Conditioning Systems with User Preferences

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 344
Author(s):  
Alejandro Humberto García Ruiz ◽  
Salvador Ibarra Martínez ◽  
José Antonio Castán Rocha ◽  
Jesús David Terán Villanueva ◽  
Julio Laria Menchaca ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Energy Saving ◽  
User Satisfaction ◽  
Air Conditioning ◽  
State Of The Art ◽  
User Preferences ◽  
The State ◽  
Nsga Ii ◽  
Air Conditioning System

Electricity is one of the most important resources for the growth and sustainability of the population. This paper assesses the energy consumption and user satisfaction of a simulated air conditioning system controlled with two different optimization algorithms. The algorithms are a genetic algorithm (GA), implemented from the state of the art, and a non-dominated sorting genetic algorithm II (NSGA II) proposed in this paper; these algorithms control an air conditioning system considering user preferences. It is worth noting that we made several modifications to the objective function’s definition to make it more robust. The energy-saving optimization is essential to reduce CO2 emissions and economic costs; on the other hand, it is desirable for the user to feel comfortable, yet it will entail a higher energy consumption. Thus, we integrate user preferences with energy-saving on a single weighted function and a Pareto bi-objective problem to increase user satisfaction and decrease electrical energy consumption. To assess the experimentation, we constructed a simulator by training a backpropagation neural network with real data from a laboratory’s air conditioning system. According to the results, we conclude that NSGA II provides better results than the state of the art (GA) regarding user preferences and energy-saving.

Theoretical Framework for Design for Dynamic User Preferences

2021 ◽  
Author(s):  
Mojtaba Arezoomand ◽  
Elliott Rouse ◽  
Jesse Austin-Breneman
Keyword(s):  
Theoretical Framework ◽  
User Preferences ◽  
Dynamic User Preferences

OPNET Simulation Setup for QoE Based Network Selection

2012 ◽  
pp. 100-139
Author(s):  
Umar Toseef ◽  
Manzoor Ahmed Khan
Keyword(s):  
User Satisfaction ◽  
Discrete Event ◽  
Mobile Terminal ◽  
Research Literature ◽  
User Preferences ◽  
Transport Infrastructure ◽  
Network Simulator ◽  
Component Development ◽  
Efficient Resource

In its most generic sense, the user-centric view in telecommunications considers that the users are free from subscription to any one network operator and can instead dynamically choose the most suitable transport infrastructure from the available network providers for their terminal and application requirements. In this approach, the decision of interface selection is delegated to the mobile terminal enabling end users to exploit the best available characteristics of different network technologies and network providers, with the objective of increased satisfaction. In order to more accurately express the user satisfaction in telecommunications, a more subjective and application-specific measure, namely, the Quality-of-Experience (QoE) is introduced. QoE is the core requirement in future wireless networks and provisions. It is a framework that optimizes the global system of networks and users in terms of efficient resource utilization and meeting user preferences (guaranteeing certain Quality-of-Service [QoS] requirements). A number of solution frameworks to address the mentioned problems using different theoretical approaches are proposed in the research literature. Such scholarly approaches need to be evaluated using simulation platforms (e.g., OPNET, NS2, OMNET++, etc.). This chapter focuses on developing the simulation using a standard discrete event network simulator, OPNET. It outlines the general development procedures of different components in simulation and details the following important aspects: Long Term Evolution (LTE) network component development, impairment entity development, implementing IPv6 flow management, developing an integrated heterogeneous scenario with LTE and WLAN, implementing an example scenario, and generating and analyzing the results.

Music Retrieval and Recommendation Scheme Based on Varying Mood Sequences

2012 ◽  
pp. 257-273 ◽  
Author(s):  
Sanghoon Jun ◽  
Seungmin Rho ◽  
Eenjun Hwang
Keyword(s):  
User Satisfaction ◽  
Clustering Algorithm ◽  
Sequence Similarity ◽  
User Interaction ◽  
User Preferences ◽  
New Music ◽  
Music Recommendation ◽  
Unsatisfactory Result ◽  
Music Retrieval ◽  
Music Clip

A typical music clip consists of one or more segments with different moods and such mood information could be a crucial clue for determining the similarity between music clips. One representative mood has been selected for music clip for retrieval, recommendation or classification purposes, which often gives unsatisfactory result. In this paper, the authors propose a new music retrieval and recommendation scheme based on the mood sequence of music clips. The authors first divide each music clip into segments through beat structure analysis, then, apply the k-medoids clustering algorithm for grouping all the segments into clusters with similar features. By assigning a unique mood symbol for each cluster, one can transform each music clip into a musical mood sequence. For music retrieval, the authors use the Smith-Waterman (SW) algorithm to measure the similarity between mood sequences. However, for music recommendation, user preferences are retrieved from a recent music playlist or user interaction through the interface, which generates a music recommendation list based on the mood sequence similarity. The authors demonstrate that the proposed scheme achieves excellent performance in terms of retrieval accuracy and user satisfaction in music recommendation.

A Survey on Recent Recommendation Systems for the Tourism Industry

2020 ◽  
pp. 205-237
Author(s):  
S. Ranjith ◽  
P. Victer Paul
Keyword(s):  
User Satisfaction ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Tourism Industry ◽  
User Preferences ◽  
Reference List ◽  
Current State ◽  
Recent Recommendation ◽  
Important Field ◽  
Preference Criteria

Data mining is an important field that derives insights from the data and recommendation systems. Recommendation systems have become common in recent years in the field of tourism. These are widely used as a tool that can input various selection criteria and user preferences and yields travel recommendations to tourists. User's style and preferences should be constructed accurately so as to supply most relevant suggestions. Researchers proposed various types of tourism recommendation systems (TRS) in order to improve the accuracy and user satisfaction. In this chapter, the authors studied the current state of tourism recommendation system models and discussed their preference criteria. As a part of that, the authors studied various important preference factors in TRS and categorized them based on their likeness. This chapter reports TRS model future directions and compiles a comprehensive reference list to assist researchers.

On Quality of Experience in Remote Visualization on Mobile Devices

2010 ◽  
Vol 2 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Gianluca Paravati ◽  
Andrea Sanna ◽  
Fabrizio Lamberti ◽  
Luigi Ciminiera
Keyword(s):  
Mobile Devices ◽  
User Satisfaction ◽  
Network Performance ◽  
Quality Of Experience ◽  
Experimental Tests ◽  
User Preferences ◽  
Frame Rate ◽  
Remote Visualization ◽  
Server Side

Quality of Experience (QoE) is a relatively new concept which represents a way of measuring user satisfaction in the use of a certain kind of service. This work investigates issues related to the QoE in manipulating 3D scenes on mobile devices, by focusing on scenarios based on the remote visualization paradigm where a remote server is in charge of computing a flow of compressed images to be delivered to client devices. A novel approach able to dynamically set the encoding parameters at the server side is presented; the considered parameters are frame resolution, frame rate and image quality. The proposed solution is able to tune the above parameters according to both user preferences and network performance. Experimental tests are exploited to assess the relationship between the involved parameters and the QoE. Results obtained by considering low resource hardware (e.g. mobile devices) and unreliable connections (e.g. wireless networks) are presented. User feedback proves the effectiveness of the proposed approach.

scholarly journals Learning Personalized End-to-End Goal-Oriented Dialog

2019 ◽  
Vol 33 ◽  
pp. 6794-6801 ◽  
Author(s):  
Liangchen Luo ◽  
Wenhao Huang ◽  
Qi Zeng ◽  
Zaiqing Nie ◽  
Xu Sun
Keyword(s):  
User Satisfaction ◽  
State Of The Art ◽  
User Preferences ◽  
Task Completion ◽  
Dialog Systems ◽  
Performance Improvements ◽  
Human Evaluation ◽  
Proposed Model ◽  
End To End ◽  
Qualitative Performance

Most existing works on dialog systems only consider conversation content while neglecting the personality of the user the bot is interacting with, which begets several unsolved issues. In this paper, we present a personalized end-to-end model in an attempt to leverage personalization in goal-oriented dialogs. We first introduce a PROFILE MODEL which encodes user profiles into distributed embeddings and refers to conversation history from other similar users. Then a PREFERENCE MODEL captures user preferences over knowledge base entities to handle the ambiguity in user requests. The two models are combined into the PERSONALIZED MEMN2N. Experiments show that the proposed model achieves qualitative performance improvements over state-of-the-art methods. As for human evaluation, it also outperforms other approaches in terms of task completion rate and user satisfaction.

A COMPARATIVE STUDY OF PERFORMANCE OF THE DESIGN STRATEGIC GROUPS AND DESIGN STRATEGIES

2004 ◽  
Vol 01 (04) ◽  
pp. 373-392 ◽  
Author(s):  
WEN-CHIH CHANG ◽  
YEN HSU
Keyword(s):  
New Product Development ◽  
Design Strategy ◽  
Aggressive Response ◽  
Strategic Groups ◽  
Design Strategies ◽  
Passive Response ◽  
Home Appliance ◽  
And Performance ◽  
And Cluster Analysis ◽  
Response Group

The results generated from a questionnaire survey conducted in the period of 2002 to 2003 have shown that Taiwanese home appliance firms' product design strategy can be classified into passive response, aggressive response, and R&D focus groups according to characteristics classified by factor analysis and cluster analysis. Differences in issues related to the design strategy adopted by each group are highlighted from case studies. Performance in new product development differs among strategic groups. Overall, the aggressive response group performs the best, followed by the R&D focus group, and finally the passive response group. Some relationships between the design strategy related issues adopted by each strategic group and performance have been found after comparisons between them.

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