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New AI technology to Measure the Noisiness of Upstairs Neighbors
Source: NewswiseURL: https://www.newswise.com/articles/new-ai-technology-to-measure-the-noisiness-of-upstairs-neighborsNew AI technology to Measure the Noisiness of Upstairs NeighborsA new method for predicting footstep sounds of upstairs residents using a CNN model
14-Dec-2022 7:00 AM EST, by National Research Council of Science and Technology1favorite_borderCredit: Korea Institute of Civil Engineering and Building TechnologyApartment floor plan and experimental setup for measuring footstep vibration and soundsPreviousNextNewswise — Some people can’t sleep well because of the noise from above: Noisy upstairs neighbors. In South Korea, these sleepless nights happen in many places because of the noise from upstairs neighbors. Living in the apartment units means dealing with a level of noise from the neighborhood on a daily basis.The Korea Institute of Civil Engineering and Building Technology (KICT, President Kim, Byung-Suk) has announced a new approach for predicting the footstep sounds of upstairs residents using a convolutional neural network(CNN) model based on vibration signals. The CNN models are widely applied in computer vision tasks. The vibration sensors are designed to be installed on the wall and floor slab of a residential building to monitor footstep-induced vibration in real–timeUpstairs floor noise causes stress to occupants and leads to conflicts between neighbors. According to a survey conducted by Korea Environment Corporation in 2022, the most common noise complaints sources in apartment units are footsteps, accounting for 67.2%. Furthermore, hammering rated 10.6%, and furniture dragging sound showed 5.5%. The biggest sources of neighbor noise in apartment units, which are mostly box-frame reinforced-concrete structures, are heavy-weight impact sound. However, there isn’t any method to obtain objective sound information.Numerical methods, such as statistical energy analysis and finite element analysis can be considered for predicting heavy-weight impact sounds. However, it is difficult to predict the sound when the material properties of the structure are complex or the constraint conditions are diversified. Furthermore, physics-based models require several hours for computation. The presented algorithm is a method for predicting the actual impact sound, especially footsteps in the rooms of buildings. A dataset was experimentally collected and its performance was compared according to the location of the vibration sensors and the resolution of the short-time Fourier transform (STFT) feature, which represents footstep-induced vibrations. The sound level for 2 s was predicted with 0.99 dB as the mean absolute error. When complaints about inter-floor noise are raised, there is insufficient evidence of sound level or which house occupants made the sound. Therefore, third parties who mediate disputes about the inter-floor noise disturbances between the neighbors, have to make decisions based on the subjective opinions of the person who filed the complaint and the neighbors who were suspected of making the sounds. However, in the future, an ‘impact monitoring system' that predicts sound based on vibration could be beneficial to alter the behavior of the neighbors causing the excessive sound. Or, it is possible that the stored data can be used by mediators in case of disputes to identify the sound source household and assess the disturbance. Main researcher Shin noted that it is more important to accumulate occupants’ perceived quality of inter-floor noise and indoor sound environments under actual living conditions. It can be used as basic data for identifying the sound perceived as ‘noisy’ by the neighbors. Shin said that “To reduce problems caused by noise between floors, it is important to quantify the noise exposure to occupants. This AI-based technology will make effective monitoring of the inter-floor noise so people will less suffer from neighbors’ noise in the future.”
Regdate 2022/12/14
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Predicting future landscape of a river
Source: EurekAlart!URL: https://www.eurekalert.org/news-releases/973487NEWS RELEASE 8-DEC-2022Predicting future landscape of a riverA eco-morphodynamic modelling was performed to predict the future landscape evolution of an actual sandy, monsoon-driven riverReports and ProceedingsNATIONAL RESEARCH COUNCIL OF SCIENCE & TECHNOLOGYPrintEmail App IMAGE: VEGETATION DYNAMICS IN 2016 view more CREDIT: KOREA INSTITUTE OF CIVIL ENGINEERING AND BUILDING TECHNOLOGYClimate change is changing the environmental condition of rivers; hence, it is no longer possible to manage modern rivers with methods that have been practiced under the past environmental conditions. A joint research team, Korea Institute of Civil Engineering and Building Technology(KICT) and Deltares of the Netherlands, conducted a research on prediction of the future changes in river landscapes using an eco-morphodynamic model applied to an actual river. According to the study result, the vegetation cover increases continuously until 2031, and the area covered by willow trees occupies up to 20% of the river area. Using this modeling, efficiency in river management can be achieved by planning management practices in advance. Eco-morphodynamic model developed by Deltares is a model that combines a vegetation model with Delft3D software, which is widely used in the field of river hydraulics. The Delft3D computes flow velocity, water depth and elevation of a riverbed. Then the vegetation model simulate the germination, settlement, growth and mortality of vegetation based on the Delft3D computation. Simultaneously, vegetation properties are converted to flow resistance and fed back into Delft3D. KICT and Deltares applied the eco-morphodynamic model to Naeseongcheon Stream in Korea which belongs to a temperate monsoon climate region with large seasonal hydrological fluctuations. Most of the Naeseongcheon Stream has characteristics as a natural river. As its riverbed is mainly composed of sand, the movement due to hydrological fluctuations and consequently, the vegetation dynamics are active. KICT has been conducting long-term monitoring including LiDAR and hydrological surveys and vegetation map production since 2012, before significant vegetation establishment in Naeseongcheon Stream began. These monitoring data were used to build and verify the eco-morphodynamic modelling. The modelling area is approximately 5 km long curved reach, located in the middle-lower section of the Naeseongcheon Stream. The width is approximately 300 m, and the grid of the model was constructed considering the actual vegetation distribution which had occurred narrowly along the shoreline. After conducting a modelling for the past data(2012-2019 period), the results were compared with the observed data. Compared with the ratio of coverage of tree species in the land cover map made with aerial photos, the area fraction of willow trees in the model result had similar coverage ratio (In 2014, actual : 2.02%, model : 2.21%). In 2016, the model adequately reproduced the actual situation by simulating the survival and growth of vegetation in the spring and the mortality of vegetation after the flood. Considering climate change scenario, the joint research team has performed a long-term modelling from 2012 to 2031. The vegetation cover continued to increase until 2031, and the area of trees reached 20% in 2031. This eco-morphodynamic model, jointly performed by KICT and Deltares, is a fully coupled model that links the hydrology-vegetation-morphololgy and able to reproduce the actual phenomenon better than other models. It has the advantage of increasing the model's reliability through application and verification in the actual river with abundant observed data. With this model, we can predict future changes in river landscape as well as ecosystem diversity and potential flood risks due to vegetation development. Dr. Lee said “This eco-morphodynamic model is able to aid decision making for implementing appropriate river and vegetation management by simulating the landscape of future rivers according to climate change, though it needs continuous improvement to reflect the complexity of real rivers.”
Regdate 2022/12/08
