An improved Kansei engineering method based on the mining of online product reviews

Emotional design is one of the most important methods in modern product design. It was highlighted after Donald A. Norman published his book Emotional design: why we love (or hate) everyday things. Emotional design addresses people’s needs and hopes which is in the center of the development of product design and product technology. Due to the lack of strong theoretical basis and quantitative analysis, many products can’t reach the goal of their designers with Emotional feelings, nor can consumer’s emotional understandings be similar to the designers’ emotional expectations. This paper analyzes the concept of emotional design and some difficulties which designers must to be faced in their emotional designing works, discusses on product shape design according to emotional design. On this basis, the Kansei engineering and emotional design methods are described in details.

Temperature control is the key of Ruhrstahl-Heraeus (RH) process in steelmaking plant. The accuracy of RH control model greatly affects the molten steel temperature fluctuation. To obtain RH control model with higher accuracy, an improved case-based reasoning (CBR) model based on attribute weights optimized by genetic algorithm (GA) was proposed. The fitness function in GA was determined according to the prediction accuracy of end temperature of molten steel in RH; then, GA is used to optimize all the attribute weights based on known case base. An improved CBR model that contains the optimized attribute weights was applied to predict end temperature of molten steel in RH, and the prediction accuracy was calculated. Four methods, CBR based on attribute weights optimized by GA (CBR–GA), ordinary CBR, back propagation neural network (BPNN) and multiple linear regression (MLR) method were employed for comparison. The results show that in the error range of [− 3 °C, 3 °C], [− 5 °C, 5 °C], [− 7 °C, 7 °C] and [− 10 °C, 10 °C], the prediction accuracy of CBR–GA was improved by 19.99%, 28.19%, 27.11% and 16.3%, respectively, than that of MLR. Compared with BPNN, the prediction accuracy increased by 3.22%, 7.44%, 5.29% and 2.40%, respectively. Compared with ordinary CBR, the accuracy increased by 5.43%, 5.80%, 4.66% and 2.27%, respectively.

Online assessment of pear firmness by acoustic vibration analysis

Feature extraction is important to obtain information in digital images, where feature extraction results are used in the classification process. The success of a study to classify digital images is highly dependent on the selection of the feature extraction method used, from several studies providing a combination of feature extraction solutions to produce a more accurate classification. Classifying the types of marine fish is done by identifying fish based on special characteristics, and it can be through a description of the shape, fish body pattern, color, or other characteristics. This study aimed to classify coral reef fish species based on the characteristics contained in fish images using Backpropagation Neural Network (BPNN) method. Data used in this research was collected directly from Bunaken National Marine Park (BNMP) in Indonesia. The first stage was to extract shape features using the Geometric Invariant Moment (GIM) method, texture features using Gray Level Co-occurrence Matrix (GLCM) method, and color feature extraction using Hue Saturation Value (HSV) method. The third value of feature extraction was used as input for the next stage, namely the classification process using the BPNN method. The test results using 5-fold cross-validation found that the lowest test accuracy was 85%, the highest was 100%, and the average was 96%. This means that the intelligent model derived from the combination of the three feature extraction methods implemented in the BPNN training algorithm is very good for classifying coral reef fish.

Regression prediction of hydrogen enriched compressed natural gas (HCNG) engine performance based on improved particle swarm optimization back propagation neural network method (IMPSO-BPNN)

Sap flow is widely used to estimate the transpiration and water consumption of canopies and to manage water resources. In this paper, an improved time series prediction model was proposed by integrating three basic networks—CNN, GRU and BiLSTM—to assess sap flow with historical environment variables. A dataset with 17,569 records of each, including 9 environment variables and 1 sap flow, was applied from a public database of SAPFLUXNET. After normalization, the environment variables were analyzed and composed with the factor analysis method. After the CNN-GRU-BiLSTM structure was designed, N records of three main factors were computed from environment variables, which were measured at N previous moments, and the sap flow was measured at the current moment, and they were applied for each training, validation, and testing cycle. To improve and compare the CNN-GRU-BiLSTM-based model, nine other models, using the methods of multiple linear regression, support vector regression, random forest, LSTM, GRU, BiLSTM, CNN-GRU, CNN-BiLSTM, and CNN-GRU-LSTM, were constructed in this study, respectively. Results show that the performance of the CNN-GRU-BiLSTM-based model has more accuracy than the other nine models we built in this paper, with the mean absolute error, mean squared error, mean absolute percentage error, and coefficient of determination (R2) being 0.0410, 0.0029, 0.2708 and 0.9329, respectively. Furthermore, for a comparison of the descending dimension method of factor analysis, principal component analysis (PCA) and singular value decomposition (SVD) methods were applied and compared, respectively. Results show that the performance of the factor analysis-based model is better than the PCA- or SVD-based model, with the R2 results of the factor analysis-based model being higher than the PCA- and SVD-based models by 5.06% and 10.63%, respectively. This study indicates that the CNN-GRU-BiLSTM-based sap flow prediction model established with a factor analysis of historical environmental variables has optimistic applications for analyzing the transpiration of trees and evaluating water consumption.

Quantitative identification of the saline soil salinity content is a necessary precondition for the reasonable improvement and utilization of saline land, the article aimed at comparing the different quantitative analysis methods and achieving fast estimation of the saline soil salt content in the Yellow River Delta based on the visible-near infrared spectroscopy. Kenli County in Shandong Province was selected as the experimental area, firstly, the representative soil samples were selected, hyperspectral reflectance of the soil samples were measured in situ and transformed to the first deviation. Secondly the correlate spectra, the characteristic spectra and indices were firstly filtered using correlation analysis. Finally, the estimation models of soil salinity content were built using the multiple linear regression (MLR), back propagation neural network (BPNN) and support vector machine (SVM) respectively. The results indicated that the characteristic wave bands of soil salinity were 684 nm and 2058 nm. On the condition of the same input variables, the prediction precision of the SVM models was the highest, followed by the BPNN, the MLR was the lowest. The SVM model based on the first deviation of the reflectance at 684 and 2058nm had the highest precision, with the calibration R2 of 0.91 and RMSE as 0.11%, the validation R2 of 0.93, RMSE as 0.26% and RPD as 2.61, which had very good prediction accuracy of soil salt content, and was very stable and reliable. Different input variables had a great impact on the model accuracy, among of the MLR models, only the precision of the model based on characteristic spectral indices was slightly higher and could be used to estimate salt content, among of the BPNN and SVM models, the precision of the models based on characteristic spectra and indices was more high and stable significantly than the models on the correlate spectra. Therefore, for the three modeling methods of multiple linear regression, back propagation neural network and support vector machine, building the estimation model of saline soil salinity content based on characteristic spectra indices was effective.

In today's competitive market, successful product development hinges on understanding user needs and experiences. With the proliferation of expanding technologies like the Internet of Things, developers can gather vast amounts of data about user interactions. To leverage this wealth of information effectively, a user-centred approach to product development is imperative. This paper explores the convergence of usability and emotional design in product development, examining their interplay and impact on user experience. Through a review of existing literature, it investigates influential factors shaping the prioritisation of usability and emotional design elements. Key questions addressed include identifying these influential factors, assessing their impact on usability and emotional design, and elucidating their interrelationships. By shedding light on these dynamics, this systematic review aims to inform product developers and designers, facilitating the creation of products that resonate with users on both usableand emotional levels.

Tomatoes are among the world’s most significant vegetables, both in terms of production and consumption. Harvesting takes place in tomato production when the important quality attribute of total soluble solids content reaches its maximum possible level. Tomato total soluble solids content (TSS) is among the most crucial attribute parameters for assessing tomato quality and for tomato commercialization. Determination of total soluble solids content by conventional measurement methods is both destructive and time-consuming. Therefore, the tomato processing industry needs a rapid identification method to measure total soluble solids content (TSS). In this study, we aimed to estimate how much soluble solids there are in beef tomato fruit by Artificial Neural Networks (ANN) and Multiple Linear Regression (MLR) methods. The models were assessed using the Coefficient of Determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) metrics. The training data set results of the MLR model established to estimate the amount of brix in tomato fruit, calculated as MAE: 0.2349, RMSE: 0.3048, R2: 0.8441, and MAPE: 5.5368, while, according to the ANN model, MAE: 0.0250, RMSE: 0.031, R2: 0.9982 and MAPE: 0.5814. According to the metric outcomes, the ANN-based model performed better in both the training and testing parts.

Assessment of calibration methods for nitrogen estimation in wet and dry soil samples with different wavelength ranges using near-infrared spectroscopy

This research was made in order to see which method performance is better between the KNN (K-Nearest Neighbor) regression method and the multiple linear regression method on Boston Housing data. The method performace referred here is MAE, RMSE, MAPE, and R2. The KNN method is a method to predict something based on the closest training examples of an object. Meanwhile, multiple linear regression is a forecasting technique involving more than one independent variable. The comparison of the two methods is based on the results of the Mean Absolute Percent Error (MAPE). In this research the definitions of distance used are Euclidean distance and Minkowski distance. The K value in the KNN method defines the number of nearest neighbors to be examined to determine the value of a dependent variable, in this research we use K values from 1 to 10 for each test data and definition of distance. In this research, the percentage of test data used was 20%, 30%, and 40% for both methods. The best MAPE value obtained by the KNN regression method was 12,89% at K = 3 for Euclidean distance and 13,22% at K = 3 for Minkowski distance. Meanwhile the best MAPE value for the multiple linear regression method is 17,17%. The best method between the two methods is the KNN regression method as seen from the MAPE value of the KNN regression method which is smaller than the MAPE value of the multiple linear regression method.

The forecasting of evapotranspiration (ET) in some water-stressed regions remains a major challenge due to the lack of reliable and sufficient historical datasets. For efficient water balance, ET remains the major component and its proper forecasting and quantifying is of the utmost importance. This study utilises the 18-year (2001 to 2018) MODIS ET obtained from a drought-affected irrigation scheme in the Eastern Cape Province of South Africa. This study conducts a teleconnection evaluation between the satellite-derived evapotranspiration (ET) time series and other related remotely sensed parameters such as the Normalised Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI), Normalised Difference Drought Index (NDDI), and precipitation (P). This comparative analysis was performed by adopting the Mann–Kendall (MK) test, Sequential Mann–Kendall (SQ-MK) test, and Multiple Linear Regression methods. Additionally, the ET detailed time-series analysis with the Keiskamma River streamflow (SF) and monthly volumes of the Sandile Dam, which are water supply sources close to the study area, was performed using the Wavelet Analysis, Breaks for Additive Seasonal and Trend (BFAST), Theil–Sen statistic, and Correlation statistics. The MODIS-obtained ET was then forecasted using the Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANNs) for a period of 5 years and four modelling performance evaluations such as the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and the Pearson Correlation Coefficient (R) were used to evaluate the model performances. The results of this study proved that ET could be forecasted using these two time-series modeling tools; however, the ARIMA modelling technique achieved lesser values according to the four statistical modelling techniques employed with the RMSE for the ARIMA = 37.58, over the ANN = 44.18; the MAE for the ARIMA = 32.37, over the ANN = 35.88; the MAPE for the ARIMA = 17.26, over the ANN = 24.26; and for the R ARIMA = 0.94 with the ANN = 0.86. These results are interesting as they give hope to water managers at the irrigation scheme and equally serve as a tool to effectively manage the irrigation scheme.

With the increasingly serious issue of global population ageing, meeting the emotional needs of elderly users when interacting with the center consoles of new energy vehicles (NEVs) has become significant. At present, the design of NEV center consoles presents difficulties for elderly users in terms of both operation and usability. To ensure driving safety and comfort, console design must take into account the operational convenience and psychological experience of elderly users. Therefore, to design a center console that meets the emotional needs of elderly users, this study proposes a comprehensive methodological framework based on Kansei Engineering (KE), the entropy-weighted technique for order of preference by similarity to an ideal solution (TOPSIS), and Backpropagation Neural Networks (BPNN), focusing on the elderly-oriented design of NEV center consoles. First, within the framework of KE, emotional evaluation indicators were collected and analysed based on the usage habits and Kansei requirements of elderly users. Second, entropy-weighted TOPSIS was applied to rank the morphological features of center consoles and to extract the key design features. Finally, a BPNN-based mapping model was constructed to link elderly users' Kansei intentions with the critical design features of the center console. The experimental results show that, among the morphological configurations, the combination of center control screen Type II, co-driver entertainment screen Type II, control panel Type II, and center console outline Type II best satisfies the emotional needs of elderly users. By integrating KE, entropy-weighted TOPSIS, and BPNN, this study provides a more effective means of understanding and predicting user emotions, thereby supporting the design of NEV center consoles that better meet the emotional requirements of elderly users.

Carbon dioxide (CO2) flooding is a mature technology in oil industry that finds broad attention in oil production during tertiary oil recovery (EOR). After about five decades of developments, there have been many successful reports for CO2 miscible flooding. However, operators recognized after considering the safety and economics that achieving miscible phases is one of big challenge in fields with extremely high minimum miscible pressure (MMP). Compared with CO2 miscible flooding, immiscible flooding of CO2 demonstrates the great potential under varying reservoir/fluid conditions. A comprehensive and high-quality data set for CO2 immiscible flooding is built in this study. Valuable guidelines have been concluded, and production prediction models are established to further assist the applicability of new projects for the first time. Results show that along with the current method in literature to find applicability guidelines, prediction models involved with important operation and production parameters help to increase the accuracy of CO2 immiscible applicabilities. Data involved in this study are checked for independence for feature selection before utilization. We also find that support vector machine could predict the enhanced oil production rate and CO2 injection efficiency better than multiple linear regression method based on the data set. Furthermore, the multiple linear regression method build an excellent model for the prediction of enhanced oil recovery with an accuracy of almost 100%. Introduction A prediction model is a tool for decision making and problem solving that has been applied in variety of fields (e.g., medical science [1-3], meteorology [4], transportation [5, 6], business [7, 8], biology [9, 10], and chemistry [11, 12]) for further applicability evaluation. Eagle et al. built a prediction model to accurately estimate the risk of six month mortality after patients have been hospitalized for acute coronary syndrome (ACS), which provides guidance of the intensity of therapy to clinicians in clinical medicine [13]. Gendt et al. established a numerical weather prediction model to help people to make plans for many activities (e.g., farmers to find the best time for harvest; pilots to schedule the safest path, etc. [14]). In a prediction model, prediction accuracy mainly depends on the methodology of prediction and the quality of data that fed into the model, which is one of the crucial indicator to evaluate the effectiveness of models that researchers spare no efforts to pursue as high of an accuracy as possible.

Customer’s emotional requirements get more and more attention in the product development. While this has been addressed by technologies, like kansei engineering, the difficulty remains in handling the differences of an individual kansei. This cognitive dissonance commonly occurs in interactions between design domain and consumer domain. Correct understanding of customer feelings and subsequently relating them to product design elements is helpful for improving design quality. This article proposes a fuzzy cognitive model to establish synesthesia between customer and designer. The novel model can improve the accuracy of requirement transmission and assist design activities. The feasibility of this method is verified by an example of mobile phone. It will find application in product conceptual design and evaluation.