A Fuzzy Approach for In-Car Sound Quality Prediction

Abstract

Numerous methods exist to characterize product quality. Nowadays, in the case of road vehicles, one of the most important issues is the acoustic comfort of the interior. However, the detection of the traffic environment is a further key question. In the case of minor vehicle collisions, the perceptibility is to analyze. Within the framework of the current study, the results of airborne noise measurements are presented. Experimental data were used to design predictive fuzzy models to estimate cabin noise level, which is in connection with the audibility of outer sourcing sounds. Two concepts of inference systems were investigated by examining accuracy, conformity and 0 residuals: Mamdani and Sugeno type ones. It was finally concluded that for estimating interior noise, Sugeno type fuzzy model is the better choice, as the accuracy and conformity are higher. In addition, the range of residuals is a magnitude lower: Mamdani type FIS provided-2.30 ~ 2.30 dB (-3.84 ~ 3.30%), Sugeno type one resulted-0.40 ~ 0.20 dB (-0.57 ~ 0.33%). Furthermore, the residuals follow a Gaussian distribution, in the case of the Sugeno predictive fuzzy model.

Multisensory Design of Electric Shavers Based on Kansei Engineering and Artificial Neural Networks

“…In his research, the detail of products in conveying unisensory versus multisensory imagery were compared and analyzed by Schiferstein to confrm that Kansei imagery can be enhanced or weakened in diferent sensory associations. For example, car engine sound helps create a sense of security [25] and quality sound of hairdryer enhances power perception [26]. Past research has demonstrated that humans can judge the imagery conveyed by a product through multiple channels of senses simultaneously, because human senses can support each other through synesthesia processes [27].…”

Analisis Perbandingan Akurasi Metode Fuzzy Tsukamoto dan Fuzzy Sugeno Dalam Prediksi Penentuan Harga Mobil Bekas

“…Himpunan fuzzy tidak memiliki batas yang tegas. Namun, metode ini secara matematis tepat dan mewakili pengetahuan yang tersedia dengan akurasi yang cukup baik (Lukács, 2020;Sutara & Kuswanto, 2019). Logika Fuzzy sering digunakan pada sistem cerdas dalam memilah proses untuk mendeteksi cacat dalam penerapannya (Saputra, 2019).…”

Conflicts of Automated Driving With Conventional Traffic Infrastructure

“…Systems (traffic sign recognition, pedestrian recognition) must be able to perceive and sometimes replace relevant human's parameters, such as perception, visibility, and audibility. [19] The Kalman filter applied to the sensors can be a good choice for making the perfect perception in a highly automated or autonomous vehicle. [18] But sometimes the automated vehicles excessive rule-compliance causes an accident.…”