Surrogate modeling for fast experimental assessment of specific absorption rate

Experimental dosimetry of electromagnetic fields (EMFs) in biological tissue is important for validating numerical techniques, designing electromagnetic exposure systems, and compliance testing of wireless devices. Compliance standards specify a two-step procedure to determine the peak spatial-averaged SAR in 1 g or 10 g of tissue in which the measurement locations lie on a rectilinear grid (selected up-front). In this chapter, we show the potential of surrogate modeling techniques to significantly reduce the duration of experimental dosimetry of EMF by using a sequential design. A sequential design or adaptive sampling differs from a traditional design of experiments as data and models from previous iterations are used to optimally select new samples resulting in a more efficient distribution of samples as compared with the traditional design of experiments. Based on a data set of about 100 dosimetric measurements, we show that the adaptive sampling of surrogate modeling is suitable to speed up the determination of the peak SAR location in an area scan by up to 43 and 64% compared with the standardized area scan on a rectilinear grid (IEC 622090, IEEE Std 1528:2013) for the LOLA-Voronoi-error and the LOLA-max surrogate model, respectively.Experimental dosimetry of electromagnetic fields (EMFs) in biological tissue is important for validating numerical techniques, designing electromagnetic exposure systems, and compliance testing of wireless devices. Compliance standards specify a two-step procedure to determine the peak spatial-averaged SAR in 1 g or 10 g of tissue in which the measurement locations lie on a rectilinear grid (selected up-front). In this chapter, we show the potential of surrogate modeling techniques to significantly reduce the duration of experimental dosimetry of EMF by using a sequential design. A sequential design or adaptive sampling differs from a traditional design of experiments as data and models from previous iterations are used to optimally select new samples resulting in a more efficient distribution of samples as compared with the traditional design of experiments. Based on a data set of about 100 dosimetric measurements, we show that the adaptive sampling of surrogate modeling is suitable to speed up the determination of the peak SAR location in an area scan by up to 43 and 64% compared with the standardized area scan on a rectilinear grid (IEC 622090, IEEE Std 1528:2013) for the LOLA-Voronoi-error and the LOLA-max surrogate model, respectively.P

Classifying urban public spaces according to their soundscape

Cities are composed of many types of outdoor spaces, each with their distinct soundscape. Some of these soundscapes can be extraordinary, others are often less memorable. However, most locations in a city are not visited with the purpose of experiencing the soundscape. Consequently, the soundscape will not necessarily attract attention. Existing methods based on the circumplex model of affect classify soundscapes according to the pleasure and arousal they evoke, but do not fully take into account the goals and expectations of the listener. Therefore, in earlier work, a top-level hierarchical classification method was developed, which distinguishes between spaces based on the degree to which the soundscape creates awareness of the acoustical environment, matches expectations and arouses the listener. This paper presents the results of an immersive laboratory experiment, designed to validate this classification method. The experiment involved 40 participants and 50 audiovisual recordings drawn from the Urban Soundscapes of the World database. It is shown that the proposed classification method results in clearly distinct classes, and that membership to these classes can be explained well by physical parameters, extracted from the acoustical environment as well as the visual scene.Cities are composed of many types of outdoor spaces, each with their distinct soundscape. Some of these soundscapes can be extraordinary, others are often less memorable. However, most locations in a city are not visited with the purpose of experiencing the soundscape. Consequently, the soundscape will not necessarily attract attention. Existing methods based on the circumplex model of affect classify soundscapes according to the pleasure and arousal they evoke, but do not fully take into account the goals and expectations of the listener. Therefore, in earlier work, a top-level hierarchical classification method was developed, which distinguishes between spaces based on the degree to which the soundscape creates awareness of the acoustical environment, matches expectations and arouses the listener. This paper presents the results of an immersive laboratory experiment, designed to validate this classification method. The experiment involved 40 participants and 50 audiovisual recordings drawn from the Urban Soundscapes of the World database. It is shown that the proposed classification method results in clearly distinct classes, and that membership to these classes can be explained well by physical parameters, extracted from the acoustical environment as well as the visual scene.C