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In light of the question of how to select the best noise control position and measures in the large noise transmission path of the cabins of a ship, based on the acoustic ray-tracing method in the theory of geometrical acoustics, and by considering the effect of the sound transmission of the bulkhead, this paper proposes the sound line search method. It is used to calculate the sound pressure of a ship's cabin, allowing the sound field distribution of multiple compartments to be simulated. The paper proposes a sound ray-searching method in which the acoustic sensitivity of different positions of the bulkhead to the noise of the target cabin is calculated by searching for the sound ray passing the target cabin. According to this, a cabin noise reduction plan can be designed to optimize medium and high frequency cabin noise. With this method, the noise of a typical cabin can be optimized and reduced by 7.3 dB. Through comparative analysis with the statistical energy method, it is proven that the method is feasible and can guide the refined design of noise reduction in ships' cabins.

Crop yield prediction is helpful for farmers to make reasonable agronomic decisions such as crop insurance, planning of harvest, storage requirement, cash flow budgeting and the determination of input factors. This paper presented an application for cotton yield prediction based on the remote visual and spectral images captured by a UAV (unmanned aerial vehicle) remote sensing system. Field experiments were carried out at a cotton field approximately 5 hm2. Images of the cotton field were captured by the UAV remote sensing system at a 50 m flight height before fully blooming. One RGB panoramic image and one CIR (color-infrared) panoramic image were obtained by stitching the collected partial images with 75% overlap. Yield data with GPS (global position system) information were registered respectively to the RGB and CIR images according to the GCPs (ground control points). Then 3 features including color feature, coverage rate and vegetation index were extracted from the images and used to develop predication models. Color feature was the hue channel in HSV (hue, saturation, value) color model. Coverage rate was calculated by using Excess Green value based on RGB (red, green, blue) color model. Vegetation index was NDVI (normalized difference vegetation index) value usually used to monitor the crop growth. The datasets including yield data and 3 features were randomly divided into training data (90% of raw data) for modeling and test data (10% of raw data) for evaluating the model accuracy. The training data were preprocessed though removing 10% partial outliers based on probability distribution of yield data and being smoothed by mean filter respectively. Then multiple linear regression models were built to predict cotton yield, and parameters of the models were estimated based on the least square method and calculated by SAS software. 3 parameters were used to evaluate the models including P value in F-test statistic, coefficient of determination R2, and MAPE (mean absolute percentage error) between predicted values and the ground truth data in acre area level. The results showed that 7 linear regression models built by 1 variable (color feature, coverage rate or vegetation index), 2 variables (color feature and coverage rate, color feature and vegetation index or coverage rate and vegetation index) and 3 variables (color feature, coverage rate and vegetation index) were all statistically significant at significance level 5%. However, the adequacy of the multiple linear regression model with 3 variables was the best due to the highest R2=0.977 3 compared to other 6 regression models built by 1 variable and 2 variables. So the multiple linear regression model with 3 variables was used to predict yield. And the average MAPEs on the multiple linear regression models built by 3 variables was 4.0% in acre area level. The results from this study indicate that combining visual and spectral features from image sensors can be used effectively to predict yield. [ABSTRACT FROM AUTHOR]

Crop stand count and uniformity are important measures for making proper field management decisions to improve crop production. Conventional methods for evaluating stand count based on visual observation are time consuming and labor intensive, making it difficult to adequately cover a large field. The overall goal of this study was to evaluate cotton emergence at two weeks after planting using unmanned aerial vehicle (UAV)-based high-resolution narrow-band spectral indices that were collected using a pushbroom hyperspectral imager flying at 50 m above ground. A customized image alignment and stitching algorithm was developed to process hyperspectral cubes efficiently and build panoramas for each narrow band. The normalized difference vegetation index (NDVI) was calculated to segment cotton seedlings from soil background. A Hough transform was used for crop row identification and weed removal. Individual seedlings were identified based on customized geometric features and used to calculate stand count. Results show that the developed alignment and stitching algorithm had an average alignment error of 2.8 pixels, which was much smaller than that of 181 pixels from the associated commercial software. The system was able to count the number of seedlings in seedling clusters with an accuracy of 84.1%. Mean absolute percentage error (MAPE) in estimation of crop density at the meter level was 9.0%. For seedling uniformity evaluation, the MAPE of seedling spacing was 9.1% and seedling spacing standard deviation was 6.8%. Results showed that UAV-based high-resolution narrow-band spectral images had the potential to evaluate cotton emergence. [ABSTRACT FROM AUTHOR]