scholarly journals APPLYING FUZZY QFD MCDM TO EVALUATE MUSICAL INSTRUMENTS

2020 ◽  
Author(s):  
Peter Poon Chong ◽  
Terrence Lalla
Keyword(s):  
Quality Function Deployment ◽  
Model Performance ◽  
Musical Instruments ◽  
Physical Parameters ◽  
Analytic Hierarchy ◽  
Data Set ◽  
Linguistic Data ◽  
Order Preference ◽  
Physical Features ◽  
Stakeholder Needs

This paper exhibits a method to improve the quality of musical instruments with the application of two Multi-Criteria Decision Making models, Technique of Order Preference by Similarity to Ideal Solution (TOPSIS) and Analytic Hierarchy Process (AHP) in a Quality Function Deployment (QFD) Environment. A fuzzy analysis approach was also included to accommodate qualitative data in music. The QFD was constructed with literature based on optimizing the manufacture of musical instruments. At this phase of the research, the paper focused on the physical parameters and perceived qualities of musical instruments. The proposed modified QFD was developed to identify the product features chosen by the market and aid the manufacture of musical instruments. A standard QFD recognized and scored factors to develop and manufacture musical instruments. It accommodated some core engineering variables for the musical instruments but overlooked some feature stakeholder needs. For example, the musician may not have 100% gratification while playing the instrument as the manufacturer fails to capture acoustic features to psychologically satisfy the musician’s audience. Using fuzzy logic, QFD and MCDM increased the model performance by expanding the data set. It offered the manufacturer of musical instruments a mode to capture and analyse behavioural linguistic data covering more customer requirements. Hence, the approach increased the range to correlate the physical features and psychological behaviours of musical instruments. It allowed non-technical persons to provide an improved form of reliable information. This modified QFD can also be applied to develop other products involving linguistic data.

scholarly journals Influence of Random Forest Hyperparameterization on Short-Term Runoff Forecasting in an Andean Mountain Catchment

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 238
Author(s):  
Pablo Contreras ◽  
Johanna Orellana-Alvear ◽  
Paul Muñoz ◽  
Jörg Bendix ◽  
Rolando Célleri
Keyword(s):  
Random Forest ◽  
Model Performance ◽  
Early Warning Systems ◽  
Point Of View ◽  
Lead Times ◽  
Physical Parameters ◽  
Runoff Forecasting ◽  
Spatio Temporal ◽  
Improved Model ◽  
Search Approach

The Random Forest (RF) algorithm, a decision-tree-based technique, has become a promising approach for applications addressing runoff forecasting in remote areas. This machine learning approach can overcome the limitations of scarce spatio-temporal data and physical parameters needed for process-based hydrological models. However, the influence of RF hyperparameters is still uncertain and needs to be explored. Therefore, the aim of this study is to analyze the sensitivity of RF runoff forecasting models of varying lead time to the hyperparameters of the algorithm. For this, models were trained by using (a) default and (b) extensive hyperparameter combinations through a grid-search approach that allow reaching the optimal set. Model performances were assessed based on the R2, %Bias, and RMSE metrics. We found that: (i) The most influencing hyperparameter is the number of trees in the forest, however the combination of the depth of the tree and the number of features hyperparameters produced the highest variability-instability on the models. (ii) Hyperparameter optimization significantly improved model performance for higher lead times (12- and 24-h). For instance, the performance of the 12-h forecasting model under default RF hyperparameters improved to R2 = 0.41 after optimization (gain of 0.17). However, for short lead times (4-h) there was no significant model improvement (0.69 < R2 < 0.70). (iii) There is a range of values for each hyperparameter in which the performance of the model is not significantly affected but remains close to the optimal. Thus, a compromise between hyperparameter interactions (i.e., their values) can produce similar high model performances. Model improvements after optimization can be explained from a hydrological point of view, the generalization ability for lead times larger than the concentration time of the catchment tend to rely more on hyperparameterization than in what they can learn from the input data. This insight can help in the development of operational early warning systems.

scholarly journals Data Augmentation Using Generative Adversarial Network for Automatic Machine Fault Detection Based on Vibration Signals

2021 ◽  
Vol 11 (5) ◽  
pp. 2166
Author(s):  
Van Bui ◽  
Tung Lam Pham ◽  
Huy Nguyen ◽  
Yeong Min Jang
Keyword(s):  
Fault Detection ◽  
Data Augmentation ◽  
Model Performance ◽  
Original Data ◽  
Fault Classification ◽  
Training Process ◽  
Generative Adversarial Network ◽  
Data Set ◽  
Adversarial Network ◽  
Machine Fault

In the last decade, predictive maintenance has attracted a lot of attention in industrial factories because of its wide use of the Internet of Things and artificial intelligence algorithms for data management. However, in the early phases where the abnormal and faulty machines rarely appeared in factories, there were limited sets of machine fault samples. With limited fault samples, it is difficult to perform a training process for fault classification due to the imbalance of input data. Therefore, data augmentation was required to increase the accuracy of the learning model. However, there were limited methods to generate and evaluate the data applied for data analysis. In this paper, we introduce a method of using the generative adversarial network as the fault signal augmentation method to enrich the dataset. The enhanced data set could increase the accuracy of the machine fault detection model in the training process. We also performed fault detection using a variety of preprocessing approaches and classified the models to evaluate the similarities between the generated data and authentic data. The generated fault data has high similarity with the original data and it significantly improves the accuracy of the model. The accuracy of fault machine detection reaches 99.41% with 20% original fault machine data set and 93.1% with 0% original fault machine data set (only use generate data only). Based on this, we concluded that the generated data could be used to mix with original data and improve the model performance.

scholarly journals Statistical analysis of Stromboli VLP tremor in the band [0.1–0.5] Hz: some consequences for vibrating structures

2006 ◽  
Vol 13 (4) ◽  
pp. 393-400 ◽  
Author(s):  
E. De Lauro ◽  
S. De Martino ◽  
M. Falanga ◽  
M. Palo
Keyword(s):  
Frequency Band ◽  
High Frequency ◽  
Noise Source ◽  
Volcanic Tremor ◽  
Large Data ◽  
Musical Instruments ◽  
Data Set ◽  
Long Period ◽  
Vibrating Structures ◽  
Analyze Time Series

Abstract. We analyze time series of Strombolian volcanic tremor, focusing our attention on the frequency band [0.1–0.5] Hz (very long period (VLP) tremor). Although this frequency band is largely affected by noise, we evidence two significant components by using Independent Component Analysis with the frequencies, respectively, of ~0.2 and ~0.4 Hz. We show that these components display wavefield features similar to those of the high frequency Strombolian signals (>0.5 Hz). In fact, they are radially polarised and located within the crater area. This characterization is lost when an enhancement of energy appears. In this case, the presence of microseismic noise becomes relevant. Investigating the entire large data set available, we determine how microseismic noise influences the signals. We ascribe the microseismic noise source to Scirocco wind. Moreover, our analysis allows one to evidence that the Strombolian conduit vibrates like the asymmetric cavity associated with musical instruments generating self-sustained tones.

scholarly journals Hospital acquired pressure injury prediction in surgical critical care patients

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jenny Alderden ◽  
Kathryn P. Drake ◽  
Andrew Wilson ◽  
Jonathan Dimas ◽  
Mollie R. Cummins ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Model Performance ◽  
Routine Care ◽  
Predictor Variables ◽  
Classification Algorithms ◽  
Data Set ◽  
Icu Patients ◽  
Pressure Injury ◽  
Surgical Icu ◽  
Hospital Acquired

Abstract Background Hospital-acquired pressure injuries (HAPrIs) are areas of damage to the skin occurring among 5–10% of surgical intensive care unit (ICU) patients. HAPrIs are mostly preventable; however, prevention may require measures not feasible for every patient because of the cost or intensity of nursing care. Therefore, recommended standards of practice include HAPrI risk assessment at routine intervals. However, no HAPrI risk-prediction tools demonstrate adequate predictive validity in the ICU population. The purpose of the current study was to develop and compare models predicting HAPrIs among surgical ICU patients using electronic health record (EHR) data. Methods In this retrospective cohort study, we obtained data for patients admitted to the surgical ICU or cardiovascular surgical ICU between 2014 and 2018 via query of our institution's EHR. We developed predictive models utilizing three sets of variables: (1) variables obtained during routine care + the Braden Scale (a pressure-injury risk-assessment scale); (2) routine care only; and (3) a parsimonious set of five routine-care variables chosen based on availability from an EHR and data warehouse perspective. Aiming to select the best model for predicting HAPrIs, we split each data set into standard 80:20 train:test sets and applied five classification algorithms. We performed this process on each of the three data sets, evaluating model performance based on continuous performance on the receiver operating characteristic curve and the F1 score. Results Among 5,101 patients included in analysis, 333 (6.5%) developed a HAPrI. F1 scores of the five classification algorithms proved to be a valuable evaluation metric for model performance considering the class imbalance. Models developed with the parsimonious data set had comparable F1 scores to those developed with the larger set of predictor variables. Conclusions Results from this study show the feasibility of using EHR data for accurately predicting HAPrIs and that good performance can be found with a small group of easily accessible predictor variables. Future study is needed to test the models in an external sample.

scholarly journals An Empirical Investigation into Physician Preferences in Drug Prescription: An Integrated Methodology of AHP and QFD

2016 ◽  
Vol 8 (5) ◽  
pp. 81
Author(s):  
Farzana Elahi ◽  
Shamsad Ahmed ◽  
Mahbubul Haque ◽  
Nazreen Chowdhury
Keyword(s):  
Quality Function Deployment ◽  
Drug Prescription ◽  
Future Research ◽  
Relationship Matrix ◽  
Analytic Hierarchy ◽  
Expert Choice ◽  
Integrated Methodology ◽  
Hierarchy Process ◽  
The Relationship

<p class="Default">In order to sustain in a competitive market like pharmaceutical in Bangladesh, it is important to get an insight into physicians’ preferences in prescribing the drugs. The aim of this work is to investigate and address the physician requirements through an integrated methodology of Analytic Hierarchy Process (AHP) and Quality Function Deployment (QFD). In this research, an expert panel has been interviewed to recognize the criteria affecting physicians’ decisions. The results from AHP derived through Expert Choice software demonstrate that from the viewpoint of physicians, out of the five criteria, quality of product offering is ranked highest in prescribing the drugs followed by the reputation of the company, relationship enjoyed with the company, etc. As for the technical aspects, derived from the relationship matrix of AHP and QFD, out of the sixteen, brand image is ranked first followed by the quality of raw and packaging materials, skilled production personnel etc. The contribution of this research is expected to enable the managers in the pharmaceutical companies to recognize the factors that influence physicians in prescribing drugs for the patients and help them find out challenging items with preeminent alternatives. Few suggestions for future research are also put forward. <strong></strong></p>

scholarly journals Development of a semi-parametric PAR (Photosynthetically Active Radiation) partitioning model for the United States, version 1.0

2014 ◽  
Vol 7 (5) ◽  
pp. 2477-2484 ◽  
Author(s):  
J. C. Kathilankal ◽  
T. L. O'Halloran ◽  
A. Schmidt ◽  
C. V. Hanson ◽  
B. E. Law
Keyword(s):  
United States ◽  
Elevation Angle ◽  
Model Performance ◽  
Diffuse Radiation ◽  
The United States ◽  
Polynomial Model ◽  
Coefficient Of Determination ◽  
Data Set ◽  
Cubic Polynomial ◽  
Solar Elevation Angle

Abstract. A semi-parametric PAR diffuse radiation model was developed using commonly measured climatic variables from 108 site-years of data from 17 AmeriFlux sites. The model has a logistic form and improves upon previous efforts using a larger data set and physically viable climate variables as predictors, including relative humidity, clearness index, surface albedo and solar elevation angle. Model performance was evaluated by comparison with a simple cubic polynomial model developed for the PAR spectral range. The logistic model outperformed the polynomial model with an improved coefficient of determination and slope relative to measured data (logistic: R2 = 0.76; slope = 0.76; cubic: R2 = 0.73; slope = 0.72), making this the most robust PAR-partitioning model for the United States currently available.

Evaluating a model for predicting active crown fire rate of spread using wildfire observations

2006 ◽  
Vol 36 (11) ◽  
pp. 3015-3028 ◽  
Author(s):  
Martin E Alexander ◽  
Miguel G Cruz
Keyword(s):  
Fire Management ◽  
Model Performance ◽  
The United States ◽  
Fire Research ◽  
Management Decision ◽  
Crown Fire ◽  
Predictive Capacity ◽  
Data Set ◽  
Rate Of Spread ◽  
Spread Model

We evaluated the predictive capacity of a rate of spread model for active crown fires (M.G. Cruz, M.E. Alexander, and R.H. Wakimoto. 2005. Can. J. For. Res. 35: 1626–1639) using a relatively large (n = 57) independent data set originating from wildfire observations undertaken in Canada and the United States. The assembled wildfire data were characterized by more severe burning conditions and fire behavior in terms of rate of spread and the degree of crowning activity than the data set used to parameterize the crown fire rate of spread model. The statistics used to evaluate model adequacy showed good fit and a level of uncertainty considered acceptable for a wide variety of fire management and fire research applications. The crown fire rate of spread model predicted 42% of the data with an error lower then ±25%. Mean absolute percent errors of 51% and 60% were obtained for Canadian and American wildfires, respectively. The characteristics of the data set did not allow us to determine where model performance was weaker and consequently identify its shortcomings and areas of future improvement. The level of uncertainty observed suggests that the model can be readily utilized in support of operational fire management decision making and for simulations in fire research studies.

Sistem Pendukung Keputusan Pemilihan Bibit Padi Berkualitas Dengan Metode AHP Dan Topsis

2021 ◽  
Vol 10 (2) ◽  
pp. 162-169
Author(s):  
Istna Mar`atul Khusna ◽  
Novita Mariana
Keyword(s):  
Analytic Hierarchy Process ◽  
Analytical Hierarchy Process ◽  
Ideal Solution ◽  
Analytic Hierarchy ◽  
Order Preference ◽  
Hierarchy Process

Abstrak— Bibit merupakan salah satu penentu dalam keberhasilan budidaya tanaman padi. Budidaya tanaman padi dimulai dari memilih bibit tanaman yang berkualitas karena bibit termasuk objek utama yang dikembangkan pada budidaya selanjutnya. Bibit sebagai pembawa gen dari induknya yang akan menentukan sifat dari tanaman setelah berproduksi dan untuk mendapatkan bibit padi yang berkualitas dapat diperoleh dari memilih dan menentukan bibit yang berasal dari induk berkualitas. Kualitas bibit merupakan kunci keberhasilan dalam budidaya padi. Bibit yang berkualitas mampu beradaptasi, memiliki pertumbuhan yang cepat serta seragam, tumbuh lebih cepat, tahan hama dan tinggi nilai produktivitasnya. Untuk mendapatkan bibit padi berkualitas, petani sering mengalami kesulitan. Berdasarkan kesulitan yang dialami petani, maka akan dibangun sebuah sistem pendukung keputusan untuk membantu petani memutuskan bibit yang akan ditanam sesuai dengan kondisi lingkungan tanam dengan mempertimbangkan beberapa aspek kriteria. Dalam mengatasi masalah pemilihan bibit padi tersebut dibuat sebuah program sistem pendukung keputusan agar memudahkan informasi dan rekomendasi kepada petani padi tentang bibit yang berkualitas. Dengan menggunakan dua metode yaitu Analytical Hierarchy Process (AHP) dan Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Penentuan bobot kriteria dilakukan dengan menggunakan metode Analytic Hierarchy Process (AHP), sedangkan untuk tahap perankingan dikerjakan dengan menggunakan metode TOPSIS. Hasil yang didapatkan dari penelitian ini adalah padi berkualitas dari lima alternatif yang sudah ditetapkan, yaitu: Sunggal, Inpari32, Ciherang, IR64, Situbagendit. Sistem menghasilkan nilai preferensi tertinggi yaitu 0,858 pada padi Sunggal di urutan pertama dan  0,767 pada padi Inpari32 diurutan kedua. Jadi dari hasil penelitian ini, peneliti merekomendasikan bibit padi berkualitas yang cocok ditanam di di desa sambongbangi yaitu Sunggal dan Inpari32..Kata Kunci : Bibit Padi, DSS, TOPSIS, AHP, Kualitas Bibit Padi

Artificial Intelligence Coreflooding Simulator for Special Core Data Analysis

2021 ◽  
pp. 1-29
Author(s):  
Eric Sonny Mathew ◽  
Moussa Tembely ◽  
Waleed AlAmeri ◽  
Emad W. Al-Shalabi ◽  
Abdul Ravoof Shaik
Keyword(s):  
Artificial Intelligence ◽  
Steady State ◽  
History Matching ◽  
Water Saturation ◽  
Flow Behavior ◽  
Model Performance ◽  
Analysis Data ◽  
Coefficient Of Determination ◽  
Water Drainage ◽  
Data Set

Two of the most critical properties for multiphase flow in a reservoir are relative permeability (Kr) and capillary pressure (Pc). To determine these parameters, careful interpretation of coreflooding and centrifuge experiments is necessary. In this work, a machine learning (ML) technique was incorporated to assist in the determination of these parameters quickly and synchronously for steady-state drainage coreflooding experiments. A state-of-the-art framework was developed in which a large database of Kr and Pc curves was generated based on existing mathematical models. This database was used to perform thousands of coreflood simulation runs representing oil-water drainage steady-state experiments. The results obtained from the corefloods including pressure drop and water saturation profile, along with other conventional core analysis data, were fed as features into the ML model. The entire data set was split into 70% for training, 15% for validation, and the remaining 15% for the blind testing of the model. The 70% of the data set for training teaches the model to capture fluid flow behavior inside the core, and then 15% of the data set was used to validate the trained model and to optimize the hyperparameters of the ML algorithm. The remaining 15% of the data set was used for testing the model and assessing the model performance scores. In addition, K-fold split technique was used to split the 15% testing data set to provide an unbiased estimate of the final model performance. The trained/tested model was thereby used to estimate Kr and Pc curves based on available experimental results. The values of the coefficient of determination (R2) were used to assess the accuracy and efficiency of the developed model. The respective crossplots indicate that the model is capable of making accurate predictions with an error percentage of less than 2% on history matching experimental data. This implies that the artificial-intelligence- (AI-) based model is capable of determining Kr and Pc curves. The present work could be an alternative approach to existing methods for interpreting Kr and Pc curves. In addition, the ML model can be adapted to produce results that include multiple options for Kr and Pc curves from which the best solution can be determined using engineering judgment. This is unlike solutions from some of the existing commercial codes, which usually provide only a single solution. The model currently focuses on the prediction of Kr and Pc curves for drainage steady-state experiments; however, the work can be extended to capture the imbibition cycle as well.

scholarly journals Technology Roadmap for Eco-Friendly Building Materials Industry

Energies ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 804 ◽  
Author(s):  
Hyunsook Shim ◽  
Taeyeon Kim ◽  
Gyunghyun Choi
Keyword(s):  
Quality Function Deployment ◽  
High Performance ◽  
Building Materials ◽  
Free Market ◽  
Process Analysis ◽  
Low Carbon ◽  
Design Development ◽  
Analytic Hierarchy ◽  
Technology Roadmap ◽  
Save Energy

As quality of life has improved, the need for high-performance building materials that meet specific technological requirements has increased. Residential environments have also changed owing to climate change. A technology roadmap could define and systematically reflect a timeline for the development of future core technologies. The purpose of this research is to build a technology roadmap that could be utilized for the development of technology in the eco-friendly building material industry. This research is composed of multiple analysis processes—patent analysis, Delphi, and analytic hierarchy process analysis—that minimize the uncertainty caused by the lack of information in the eco-friendly construction industry by securing objective future forecast data. Subsequently, the quality function deployment test is implemented to verify the feasibility of the technology roadmap that is constructed. The design of various types of functional, low-carbon building materials could reduce carbon emissions and save energy by ensuring a hazardous-material-free market in the future. This design development roadmap is required to complement this technology roadmap.

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