List of the term of monosyllables [8].
\r\n\t
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Resilience, as articulated by the Presidential Policy Directive 21 [1], refers to “the ability to withstand and recover from deliberate attacks, accidents, or naturally occurring threats or incidents.” In order to approach the
According to the U.S. Department of Energy, a microgrid is “a group of interconnected loads and distributed energy resources (DERs) with clearly defined electrical boundaries that acts as a single controllable entity with respect to the electric utility grid.” A microgrid can be connected to the grid, in a grid-connected mode, or independent from the grid, in an islanded mode. Operation and control of microgrids have been given genuine attention in the literature. Each of the individual resources and controllable loads needs a controller. These controllers are referred to as local or resource controllers. In addition, a microgrid central controller (MGCC) is needed to act as a coordinator/supervisor to the local controllers (LCs).
\nThe speed and bandwidth requirements significantly vary between LCs and MGCCs. LCs need to be faster since they have to deal with current and voltage commands and measurements, whereas MGCCs take a supervisory role in managing the power flow of the assets and loads within the microgrid and between the microgrid and the main grid. In addition, MGCCs fix any errors, for example, frequency deviation that may result from primary control [2, 3]. Tertiary control of microgrids refers the layer of control that extends beyond the boundaries of a single microgrid. It coordinates the interaction between various microgrids in close vicinity and the main grid. A virtual power plant whether owned by the utility company or a third party, aggregating some microgrids in a given geographic area, can be considered an example of tertiary control. This layer of control is typically considered as a part of the main grid’s control [2, 4] and will be out of the scope of this chapter. However, by the end of the chapter, the interaction of the MGCC with the main grid will be highlighted.
\nDC microgrids offer several advantages over AC microgrids [5, 6]. Electronic devices, such as computers, routers, and electronic lights (either fluorescent or LED), represent a high percent of the electric load in many buildings today. Moreover, variable speed drives (VFD) are increasingly used for electric motors. A DC environment is found to be a more convenient way to deliver power to these loads to assure reliability and redundancy. DC networks do not need AC to DC conversion for every electronic device, which has a significant impact on the efficiency. DC can reduce the losses associated with switch-mode supplies and uninterruptible power supplies. Furthermore, incorporating DC microgrids has the benefit of superior compatibility of the DC power with renewable energy generators, for example, photovoltaics (PV), electric vehicles, and energy storage systems (ESSs) [7–10].
\nIn this chapter, we will focus on the design of primary and secondary control techniques for DC microgrids. Computer simulations and hardware testing will be used to verify the presented techniques. The simulation results were obtained using MATLAB/Simulink and the SimPowerSystems sublibrary.
\nThe DC microgrid under study is assumed to be dependent mainly on the sustainable energy sources, as shown in Figure 1. The microgrid is connected to the main grid, so that it can operate in a grid-connected mode. Moreover, it includes an ESS, so that it can operate in an islanded mode during blackout/brownout conditions. During the grid-connected mode, power can be drawn either from the main grid or from the ESS in case the locally generated renewable energy is not enough to satisfy the load demand.
\nDC microgrid architecture and control hierarchy.
Since the microgrid is based on renewable energy, certain features had to be maintained to assure efficient integration of the renewable resources, such as efficient and reliable load-feeding capability and full controllability of voltage and power flow among the various buses in the system. The connectivity of the DC microgrid to the main grid should allow voltage regulation on the DC side. Furthermore, it should allow bidirectional power flow between AC and DC sides, depending on the desired mode of operation.
\nSpecifically, a fully controlled rectifier was used to tie the DC network to the AC grid while working at unity power factor. This rectifier is dedicated to regulating the voltage on the DC bus in the grid-connected mode. Therefore, it enables unidirectional power flow from the main grid to the DC microgrid. Alternatively, one of the other resource converters, for example, the bidirectional battery charger, must be responsible for regulating the DC bus voltage. A fully controlled bidirectional AC–DC converter was used to control the active/reactive power exchange with the main grid. It employs a vector decoupling control technique, which enables independent control of the active and reactive power in both directions. Each converter is controlled via a LC. A MGCC communicates with the LCs and coordinates their operation.
\nThe ESS and PV will be linked to the common DC bus of the microgrid through DC–DC converters. Boost converter is commonly used to interface renewable energy sources yielding DC voltage to the DC microgrid. In case of PV systems, a controlled boost converter shall serve two functionalities: (1) it steps up the output voltage of the PV system to be compatible with the DC bus voltage and (2) it regulates the output voltage or power, for example, corresponding to a predefined maximum power point tracking algorithm. The boost converter topology may be slightly modified when used as an interface for PV systems in a DC microgrid. For instance, since the DC bus may already possess high capacitance (i.e., owing to the other converters connected to the same bus), the boost converter capacitor can be omitted, resulting in a discontinuous instantaneous output current. A solution to maintain continuous output current is to synchronize multiple DC–DC converters (i.e., interleaved converters) [11, 12].
\nThe proposed inductively coupled boost converter topology for fuel cells integration into a DC ZEDS.
The ON and OFF states of the DC–DC converter with output L-filter: (a) (0 <
Another approach to solve the problem of discontinuity in the output current is by adding an L-filter to the output side of the converter, as shown in Figure 2. The added inductance assures continuous conduction of the output current. The configurations of the circuit during the ON and OFF states of the switches are shown in Figure 3, where the parameter
The small-signal mathematical model of this converter can be obtained using the state-space averaging technique. The state-space model during the interval (0 <
Using the state-space averaging technique,\n
If we consider a small-signal perturbation, the large-signal state-space equations will be\n
Bode plots of the developed controller.
A prototype system was designed and implemented in hardware to examine the performance of the inductively coupled boost converter. The set of equations described in Section 3.1 was used to design a closed-loop proportional-Integral (PI) controller. The digital signal processing board dSPACE 1104 was used to control the converter in real time. The switching frequency for the converter was 5 kHz. The parameters of the implemented converter prototype were as follows:
Results for the inductively coupled boost converter are shown in Figure 5. The output current reference changed from 1 to 3 A after 5 s. It can be seen that the output current is continuous, and the ripple is as small as 2%, which means a high power quality injected to the DC microgrid.
\nResults for (inductively coupled boost converter): (a) simulation results and (b) experimental results (the same scale: 1 A/division).
A fully controlled three-phase rectifier will be used for coupling the DC network to the AC grid. In our case study, we designed and implemented the rectifier such that it regulates the voltage of the DC bus, while being able to operate at unity power factor. This was achieved through a vector decoupling control technique and sinusoidal pulse width modulation (SPWM).
\nVector decoupling SPWM control is based on converting the voltages and currents from the three-phase abc frame of references to the d–q frame of references. Even though mathematical models for the system have been derived, PI controllers were implemented to control the rectifier rather than model-based control due to its relative simplicity and effectiveness. However, the mathematical models play an important role in decoupling the vectors, which is essential to achieve independent
The implemented three-phase SPWM rectifier: (a) circuit diagram and (b) single-phase equivalent.
The voltage equation is\n
where
\nThe rectifier should instantaneously draw enough input power to satisfy the sum of the load demand and the charging rate of the capacitor energy, to maintain fast voltage control. Neglecting the thermal and switching device losses, the power balance between the AC input and the DC output is as follows:\n
On the DC output side,\n
Inspecting Eq. (12), we can see that the system is nonlinear with regard to
Two nested loops including three PI controllers were implemented to achieve DC voltage as well as input current control. The outer loop is for controlling the DC bus voltage, while the inner loop is for current control. Due to the vector transformation from abc to d–q frame of references, the controller deals with three DC signals, which help eliminate steady-state errors in the developed PI controllers.
\nIn order to completely decouple the d and q components and achieve independent
In Figure 7, a layout for the developed controller is shown.
\nA block diagram of the vector decoupling control implemented on the controlled rectifier.
The controller has the capability to control the active and reactive power independently, and hence, it can be easily set up to operate at unity power factor by adjusting the desired
DC microgrids may draw or inject power to the grid, depending on the local generation/demand ratio. Therefore, a bidirectional converter must be put in place to enable such energy transfer. For instance, during times of surplus energy, that is, when power from the PV system is greater than the local load, the power can be injected to the grid if the price for electricity is high and/or the battery is fully charged. On the other hand, power may be drawn by the DC microgrid to cover load deficiencies. The vector decoupling control technique discussed earlier in this chapter was utilized here to enable independent active and reactive power control. For the bidirectional converter, the topology is modified by adding an L-filter (
Circuit diagram of the implemented three-phase bidirectional AC–DC/DC–AC converter.
A block diagram of the vector decoupling control implemented on the bidirectional converter.
This controller may be looked at as a means to control the voltage across the L-filter inductor. Corresponding to any given
Both the unidirectional converter (i.e., the controlled rectifier used for DC bus voltage regulation) and the bidirectional converter were implemented in hardware and simulated in MATLAB/Simulink. The switching frequency for both the converters was 8.04 kHz, and the sampling time was 0.3 ms. Voltage and current sensors were deployed to receive feedback from the various nodes of the system [14]. Several experiments were conducted to test the response of the converters under steady state as well as transient operating conditions. The hardware and simulation results will be presented for the various case studies.
\nThe first experiment aimed at testing the steady-state response of the rectifier while operating at unity power factor. Results of this experiment are shown in Figure 10. As can be seen in the figure, the current and voltage waveforms are in phase, and the DC bus voltage is regulated at 300 V. The DC current is positive, which according to our notation, means that the power is flowing from the main grid to the DC microgrid.
\nUnity power factor operation of the controlled rectifier: (a) experimental results and (b) simulation results (AC current factorized by 10).
Two experiments were conducted to examine the performance of the developed rectifier under transient operating conditions, namely (1) a step change in the load demand and (2) a step change in the DC microgrid voltage. Figures 11 and 12 depict the results for these two experiments, respectively.
\nControlled rectifier’s response to a load step change: (a) experimental results and (b) simulation results.
Controlled rectifier’s response to a change in the output voltage: (a) experimental results and (b) simulation results.
For the first experiment, whose results are shown in Figure 11, the DC load was suddenly changed from 0.72 to 1.5 kW. The results show that the converter is capable of responding to the change in the load by increasing the value of
In the second experiment,
The bidirectional converter was implemented using an L-filter value of 24 mH. This filter, which encompasses 0.9 ohm internal resistance, was used to improve the overall total harmonic distortion of the converter and achieve smooth current control. Several experiments were conducted to test the response of the converter under several step changes in the desired power and its direction.
\nControlled bidirectional response to DC current reference change from 1 to 3 A: (a) experimental results and (b) simulation results (AC current factorized by 10).
Controlled bidirectional converter response to DC current reference change from 3 to 1 A: (a) experimental results and (b) simulation results (AC current factorized by 10).
The first experiment (see Figure 13) involved a step change in the reference current from 1 to 3 A, while operating in the rectifier mode (i.e., the power flows from the main grid to the DC microgrid). Inspecting Figure 13, it can be seen that the converter succeeds in corresponding to the step change in the current reference within a few cycles. The results of a reverse experiment, in which the current reference was decreased from 3 to 1 A, are shown in Figure 14. Both experiments verify the applicability of the developed converter. It is worth mentioning that the experimental results match the simulation results. This assures the credibility of the simulation model, which can be used for analyses that may not be easily performed experimentally, for example, fault analysis.
\nControlled bidirectional converter response to DC current reference change (−3 to −1 A): (a) experimental results and (b) simulation results (AC current factorized by 10).
Controlled bidirectional response to DC current reference change (−3 to 3 A): (a) experimental results and (b) simulation results (AC current factorized by 10).
To examine the converter in the inverter mode, the current reference was changed from −3 to −1 A. The negative sign refers to the inverter mode of operation. Figure 15 shows the results for this experiment. It can be seen that the converter achieves the desired output current, and that the AC current and voltage waveforms are 180° out-of-phase corresponding to the negative active power direction and zero reactive power.
\nIn the developed bidirectional converter, the power can flow in both directions, that is, from AC to DC or from DC to AC. Two experiments were conducted to test the ability of the converter to change the direction of the power instantaneously, while maintaining unity power factor operation. Figure 16 shows the results of an experiment in which the current reference was changed from −3 to 3 A. This means that the current was flowing from the DC microgrid to the main grid and suddenly reversed its direction. The simulation and experimental results show that the converter was able to control the current and achieve the required step change. Figure 17 shows the case when the current reference was changed from 3 to −3 A. The results of both experiments show that the converter can smoothly change its mode of operation, from the rectifier to the inverter mode, and vice versa.
\nControlled bidirectional converter response to DC current reference change (3 to −3 A): (a) experimental results and (b) simulation results (AC current factorized by 10).
The MGCC communicates not only with the LCs to coordinate their operation but also with the controller of the main grid. The main function of the LC is local voltage and current control of the converter that they are associated with. The functions of the secondary controller are optimal microgrid control, for example, energy cost minimization, broadcasting active and reactive power set points, and islanding detection and operation [2, 15, 16].
\nIn order to examine the operation of MGCC, an experiment was conducted. Four AC generators were used to form an AC network. The generators were interconnected through transmission line models. The DC microgrid described earlier within this chapter was connected to the main AC grid at the AC point of common coupling (PCC), as shown in Figure 18. The DC microgrid has the ability to draw or inject
MGCC interaction with the main grid’s controller.
Response of the DC microgrid in an integrated hybrid AC/DC system corresponding to step changes in the load demand reference: (a) the load, DC, and AC active power share; (b) the load, DC, and AC reactive power share; (c) the frequency of the AC bus; and (d) the voltage of the AC and DC buses.
When a reactive load of 0.45 kVARs with lagging power factor is added to the PCC after 43 s, the voltage drops to around 0.95 pu. The DC microgrid is then requested by the main grid to inject 0.3 kVARs. Therefore, the voltage at the PCC increases to 0.98 pu. The DC bus voltage is hardly affected by this change in its reactive power reference. A maximum of 0.2 Hz frequency deviation was reported, as shown in Figure 19. This experiment highlights the coordination that can be achieved between the main grid’s controller and the MGCC to enhance the overall performance of the whole system [18].
\nIn human speech cognition, speech intelligibility integrates short-term memory and cerebral feedback [1]. However, important factors constituting the spatial impressions of sound also include certain related evaluation indicators, such as the listener’s judgment of sound source direction (sense of direction) and distance (sense of proximity), apparent source width (ASW), and lateral envelopment (LEV). As suggested by Ando [2] and Beranek [3], the composition of such spatial impressions mainly depends on fluctuations of the magnitude of the interaural cross-correlation (IACC) and is especially affected by the degree of subjective diffusion of the sound field. However, listeners differ in their needs and perceptions regarding subjective diffusion and ASW.
\nWith regard to neuron-psychology, Sperry [4] discovered the phenomenon of hemispheric disconnect. The cerebral specialization theory distinguishes between “speech functions” and “non-speech functions.” Certain symbols in architectural design belong to non-speech functions. For instance, the range of non-speech functions includes aesthetic perception and the feeling of balance. In particular, many non-speech symbols can be observed in environmental design. Earlier research on audio and cerebral correlations found that such common medical problems as aphasia and disturbances in tone judgment originate in the left cerebral hemisphere. Therefore, this study suggested that cerebral responses to speech and non-speech symbol in the physical environment effectively substitute for the semantic differences (SD) caused by age-related and cultural differences. Cerebral responses to communication stimuli are a direct cross-cultural and cross-age reference indicator, which is similar to the principle behind polygraph tests performed by police to examine physiological responses.
\nThis study suggested that cerebral responses can be used to clearly and consistently examine responses to change in “speech functions” of the physical environment, or speech intelligibility, when designing a sound field. Ando [2] considered “speech functions” to be an important temporal factor and the result of autocorrelation function (ACF) evaluations in the brain. Therefore, the environmental effects of temporal factors were examined in this study based on the influence of speech intelligibility on the correlation between “subjective perceptions” and cerebral responses, which served as the basis for the objective design of an acoustic environment. Akita et al. [5] indicated that when the sensory information received by listeners is analyzed by brainwaves, this does not represent their direct experience of changes in the environment, but rather the interaction between physiology and the environment. This phenomenon is common in daily life. The intensity of cerebral evoked responses is the optimal evaluation tool [6]. Soeta et al. [7] studied the effects of sound source features on subjective psychological responses and cerebral responses measured by magnetoencephalography (MEG) and reported that at different delay times of reflection sounds (Δ
The first reflection delay (Δ
The IACCE3 was changed to change subjective ASW. Changes in the waveforms of auditory evoked potentials (AEPs) during listeners’ perceptions of spatial ASW were analyzed.
This study used monosyllabic speech sound articulation and IACCE3 to quantify changes in two subjective experiences, namely, speech intelligibility and ASW. With regard to speech intelligibility, the fifth group of common Chinese monosyllabic speech sounds used in Taiwan [8] (female voice, Table 1) was used. Test results related to this group of monosyllabic sounds are characterized by the largest disparity in error rates because most related sounds belong to “fricative sounds” (i.e., apical vowels, such as “zh,” “ch,” “sh,” “r,” “z,” “ci,” and “si” in Bopomofo system). The amounts of fricative and non- fricative rhymes are balance (eight versus ten, respectively). The sound structure of Mandarin differs from that of other languages. In Mandarin, each character is pronounced as a monosyllable with one of five tones (i.e., types of pitch contour). Each of these tones (0–4), when used with a given monosyllable, causes the monosyllable to convey a meaning distinct from those conveyed when the monosyllable is used with the other four tones. Utterance lengths in the experiment were set to 400–500 ms. Monosyllabic presents were separated by 2.5 s. The experiment was arranged according to the arrangement used in the study by Chen et al. [9].
\nChinese monosyllable | \n|||||
---|---|---|---|---|---|
1 | \nshy0 | \n7 | \nyu2 | \n13 | \nching3 | \n
2 | \niur1 | \n8 | \nleau3 | \n14 | \ntzuen1 | \n
3 | \nli0 | \n9 | \nshoou3 | \n15 | \ncha2 | \n
4 | \nmeei3 | \n10 | \nian1 | \n16 | \nshuo4 | \n
5 | \ntsae3 | \n11 | \ntsong1 | \n17 | \nhe4 | \n
6 | \nru2 | \n12 | \nguang1 | \n18 | \nchye2 | \n
List of the term of monosyllables [8].
Note: The pronunciation of each syllable depends on the tone (one of five pitch contours) used, which is indicated by a number attached to the end of the syllable. For example, 0 denotes monosyllables pronounced with a soft puff of air.
The experiment was conducted in front of two overlapping loudspeakers in a semi-anechoic room (4 × 3 and 4 m in height) at Chaoyang University of Technology. The loudspeakers (Fostex NF-1A) were located at 1.5 m right front of the center of a listener’s head. The first reflected sound was given off by the upper loudspeaker (
The setup of the instrumental diagram (audio arrangement and EEG recordings).
Item | \nConditions of experiments | \n
---|---|
Δ | \nDelay gap: 0 ms, 35 ms, 100 ms, 150 ms, 200 ms | \n
SPL of individual loudspeakers | \nDirect sound: 60 dB(A); first reflection, Δ | \n
Reverberation times | \n
The setting of the physical parameters in subjective articulation test of monosyllables.
The percentage syllabic articulation of monosyllable functioning initial time delay of a sound field.
The paired-comparison method [11] was used in the psychological quantification test of subjective ASW. The experiment was conducted in the same venue as the first experiment. Three loudspeakers (one for direct sounds and two for reflected sounds) were located at 1.5 m from the center of a listener’s head; the incidence combinations (
IACCE3 (setup values) | \nAmplitude of direct sound (A0) | \nI-1, SPL/dB(A) | \nAmplitude of first reflection (A1) | \n
---|---|---|---|
0.35 | \n1 | \n62.6 | \n0.8 | \n
0.57 | \n1 | \n62.6 | \n0.8 | \n
0.68 | \n1 | \n55.4 | \n0.4 | \n
0.81 | \n1 | \n64.0 | \n0.2 | \n
59.4 | \n0.8 | \n59.4 | \n65 | \n
59.4 | \n0.8 | \n59.4 | \n65 | \n
53.4 | \n0.4 | \n55.4 | \n65 | \n
53.4 | \n0.2 | \n53.4 | \n65 | \n
The parameters of subjective source apparent width (ASW) test arranged by 2 kHz pure tone burst.
Note: I-1, I-2 and I-3 denote the sound intensity of direct sound and 1st and 2nd reflections sound measured at the location of the head top of the participants. a denotes the amplitude of the direct sound, 1st reflective and 2nd reflective sound by A0, A1 and A2.
The scale values of subjective ASW test functioning IACCE3.
After the fast Fourier transform (FFT) was applied to the brainwaves, ACF of CBW calculations were performed for the α-waves (8–13 Hz) and β-waves (13–30 Hz) of the left and right hemispheres. In the earlier study by Chen and Ando [15], 100 Hz α-waves and 500 Hz β-waves were sampled according to the sampling frequency laws and, after A/D conversion (16 bits), input into a computer to calculate the effective duration (
The ACF curve of α-wave (left) and β-wave (right) recorded in relation to the monosyllable “tzuen1” were announcing.
To explore the changes in subjective perceptions of ASW, AEPs of nine participants were induced, recorded and analyzed as in the psychological intelligibility experiment. However, a spatial impression of a sound signal is a short-term memory phenomenon. Therefore, waveforms induced by the brain AEPs are normally used to observe changes in responses to weak brainwave signals (about 10–100 μV in amplitude when measured from the scalp). Clear consistent brain waveforms are usually obtained by applying the signal averaging method [18] to responses that occur within 500 ms after auditory stimulation (Figure 5). In this study, 180 times of averaging process was applied here since the wave form of slow vertex responses (SVR) were clearly obtained. The movements (latency) of waveform peaks and troughs in the wave relative amplitude can reflect the activation of different parts of auditory nerves [19, 20]. As shown in Figure 5, this study changed ASW perceptions by changing the sound arrival orientation and energy while fixed reflection delay and echo times (Δ
The diagram illustrates brain waves in different time domains and their index at the peak or trough by Ichikawa [
With regard to brainwave recording, eight participants (and other nine in the AEPs experiments) sat on comfortable office chairs in the semi-anechoic room at Chaoyang University of Technology and their brainwaves were induced and recorded. The room temperature was maintained at 22 ± 2°C. All subjects were prohibited from drinking any alcohol for a period of 3 days before the brainwave recordings were conducted, and they refrained from smoking for 1 h before both experiments. They were instructed to concentrate on listening to the signals during the presentation. The participating subjects were eight male students (plus another nine male students in the AEPs experiments) aged 22–24 years old with normal hearing ability, as confirmed by an audiometry test and right-handed test (self-administered). The audiometry test detects sensorineural hearing loss (damage to the nerve or cochlea) and conductive hearing loss (damage to the eardrum or the tiny ossicle bones). Pure-tone subjective audiometry, in which air conduction hearing thresholds in decibels (dB) for a frequency range of 250–8000 Hz are plotted on an audiogram for each ear independently, was applied. All of the subjects had to be qualified as normal with a pure-tone audiogram (less than 25 dB) for both ears prior to the brainwave experiments and questionnaires.
\nThis procedure has been applied in many studies, such as those by Chen et al. [9], Ando et al. [19], and Ando et al. [20], among others.
\nElectrodes used to explore brainwaves were positioned at the participants’ T3 and T4 head points according to the international 10–20 system [21]. Electric potentials were examined using eardrops on the left and right sides. Unipolar induction of continuous brainwaves in the left and right hemispheres was performed. The G2 electrode was attached between the eyebrows for eye movement reference. The electrode system was grounded each time the brainwaves were recorded in order to avoid external electric interference. The settings of the simulated sound field were similar to that in the aforementioned psychological experiment [16]. The collected brainwave data was analyzed and processed by NI LabVIEW software. The setup of the instrumental diagram is shown in Figure 1. During the brainwave experiments, the subjects had to be relaxed while paying close attention to the sound stimuli. Brainwaves are extremely sensitive to any incoming stimuli or stress. For the purpose of this study, a relaxed state but one also focused on environmental variations was considered the best condition for the subjects during the brainwave recording process. For the recordings, periods of blinking had to be disregarded. Thus, a monitor was set up in the anechoic chamber to identify these periods, and these sections were later removed from the recordings.
\nMonosyllabic speech sounds had a major effect on both α-waves (
Relationship between τe of ACF, α-wave and Δt1 of sound field.
Relationship between τe of ACF, β-wave and Δt1 of sound field.
Figure 7 shows changes in β-waves. Consistent results were obtained with regard to the influence of the delay time of reflection on the left hemisphere (
The findings related to SVR to evoked potentials of nine participants are shown in Figure 8. With regard to the left hemisphere, SVR relative amplitude were consistently and inversely related to quantified psychological scale values (
Relationship between potential, SVR and ASW of sound field.
Latency changes in the left and right hemispheres indicated the presence of a significant difference between ASW (0.03) and ASW (0.45) only at N2 in the left hemisphere (
Relationship between latency, SVR and ASW of sound field.
The arrangements and results of the aforementioned brainwave experiments indicated that when simple physical changes in a sound field and complex psychological feedbacks affect cerebral brainwave reactions, the correspondence of the cerebral specialization theory with the results becomes very complicated. In general, in this study, the left hemisphere tended to be activated in both temporal and spatial aspects based on the sound field. When the participants’ brainwaves were recorded during the judgment task, the brain activation in the right hemisphere tended to reflect the discriminated object more closely. When CBW were observed during research on speech intelligibility, the left hemisphere showed clear reactions to the first reflection delay time of sound field (Figure 7). However, the degree of speech intelligibility is a reflection of the complex thinking process that occurs in the right hemisphere (cerebral feedback). This phenomenon was supported by the subjective ASW experiment. With regard to changes in spatial factors, the left hemisphere received information about sound field changes when the IACCE3 value changed. ASW changes between (ASW (0.03) and ASW (0.45)), which were more evident in the right hemisphere, affected both right and left hemispheres. They are coherent while the N2 latency of SVR significantly prolonged in both left and right hemispheres under changes of subjective diffuseness in IACCE3 found by Ando et al. [20]. Different sites are activated by brainwaves during focused and ambient use of the brain.
\nCerebral specialization has been reported to be determined by focused conscious decisions. For instance, Floel et al. [23] conducted a spatial—visual focus experiment and used a Doppler ultrasound system and magnetic resonance imaging (MRI) equipment to observe the brain reactions of right-handed participants; the researchers found that both spatial recognition and speech functions were activated in the right hemisphere, which corresponded to clinical experiment results.
\nNevertheless, for CBW researches, we conclude that α-waves (8–12 Hz) mainly responds to the emotional reactions; β-waves (13–30 Hz) reacts to the auditory matter drift (Figures 6 and 7). But the left hemisphere leads focus or attention on the varying of situational conditions (Figures 8 and 9), and the right one blends with imaginable feeling and experience. Hemispheric specialization has to pay attention to the conditioned response, conscientious and careful detail to setup each brainwaves’ experiment.
\nI would like to express my thanks to the graduated students, Yong-Shang Chen and Qi-Wen Lin who have, in brainwaves’ experiments, helped me in the course of preparing this study. In particular, I wish to thank Professor Em. Yoichi Ando, who kindly gave me directions on my brain research and data analysis methodology. I am also indebted to the Ministry of Science and Technology Taiwan, for their 2 years period (2007 and 2012) of financial support to complete this research. Meanwhile, the application conformed to academic ethics for the conduct of research. Moreover, during the brainwave experiments, the subjects were assured of their safety and told the procedure was non-invasive. Special thanks are due to my many colleagues for their participation in the experiments involving the subjective judgments and the brainwaves’ recordings.
\n\n apparent source width, a sound perception of the subjective diffuseness occurred from beginning to 80 ms of stimulus delay gap between direct and first reflection in a defuse sound field binaural initial (<80 ms) interaural cross-correlation function autocorrelation function effective delay of autocorrelation function (ACF) continuous brainwave, a term to distinguish from an evoked potential (EP) or evoked response within EEG slow vertex response, an evoked potential is a direct result after a specific sensory stimulus in the period of 10–500 ms listener envelopment, a sound perception of the subjective diffuseness occurred after 80 ms of stimulus semantic differences, a method of questionnaire employed the scale of responses caused by a psychological affection example of a monosyllable in Taiwanese’s life speech vertical angles at a median plane, 0° started from the front of head at ear height angles at clockwise horizontal plane, 0° started from the front of head at ear height sound pressure level measured by a sound level meter in a fast time-weighting mode auditory evoked potential percentage syllable articulation
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