Robust statistical approaches for feature extraction in laser scanning 3D point cloud data

Three dimensional point cloud data acquired from mobile laser scanning system commonly contain outliers and/or noise. The presence of outliers and noise means most of the frequently used methods for feature extraction produce inaccurate and non-robust results. We investigate the problems of outliers and how to accommodate them for automatic robust feature extraction. This thesis develops algorithms for outlier detection, point cloud denoising, robust feature extraction, segmentation and ground surface extraction

Study on Comparative Analysis of Basic Woven Fabrics Produced in Air-Jet Loom and Determining Structure for Optimum Mechanical Properties and Production

This analysis was directed at dissecting the impact of the structure of the fabric on different properties of the fabric, for example tear strength, tensile strength, shrinkage, elongation, skewness, and so on. The work demonstrated how various structures of the fabric influence these properties. Fabrics with a fundamental woven structure, namely plain, twill, satin and a couple of their subsidiaries, were produced to explore the influence of the structure on different properties of the fabric. The examination built up an approach to gauge the mechanical conduct of the fabric dependent on its structure. The exploration accentuated the structure and detail of the fabric to decide the underlying driver of the change in the mechanical conduct. The properties of the fabric, such as tear strength, tensile strength, elongation, shrinkage and skewness, were extraordinarily affected by the structure of the fabric. It likewise demonstrated to having more noteworthy mechanical properties for firmly interwoven structures, such as plain and twill. The analysis led to the conclusion that the plain structure has the best mechanical properties among different structure

アラスカ湾堆積物コアの地球化学データによる融氷期の古海洋復元とコルディエラ氷床の動態

富山大学・富理工博甲第194号・MdNurunnabiMondal・2021/9/28・★論文非公開★富山大学202

A COMPARATIVE STUDY BETWEEN ONE BATH DYEING METHOD FOR POLYESTER COTTON (PC) BLENDED FABRIC OVER CONVENTIONAL TWO BATH DYEING METHOD

Dyeing of Polyester Cotton (PC) blended knit fabrics is done by two different types of dyestuff namely reactive dyes for cotton part and disperse dyes for polyester part in conventional two bath method where after polyester part dyeing the liquor is drained and then cotton part is dyeing. This research work has been carried out on finding the possibility of dyeing this type of fabric in single bath method without drain the liquor after enzyme bio-polishing and polyester part dyeing. No extra chemicals are needed in one bath dyeing method. There is in significant change of fastness properties than conventional two bath method. This one bath dyeing method saves the consumption of water with energy and time. In this research work, it was found that in one dyeing method cost and time are saved to conventional two bath dyeing method

Resampling methods for a reliable validation set in deep learning based point cloud classification

A validation data set plays a pivotal role in tweaking a machine learning model trained in a supervised manner. Many existing algorithms select a part of available data by using random sampling to produce a validation set. However, this approach can be prone to overfitting. One should follow careful data splitting to have reliable training and validation sets that can produce a generalized model with a good performance for the unseen (test) data. Data splitting based on resampling techniques involves repeatedly drawing samples from the available data. Hence, resampling methods can give better generalization power to a model, because they can produce and use many training and/or validation sets. These techniques are computationally expensive, but with increasingly available high-performance computing facilities, one can exploit them. Though a multitude of resampling methods exist, investigation of their influence on the generality of deep learning (DL) algorithms is limited due to its non-linear black-box nature. This paper contributes by: (1) investigating the generalization capability of the four most popular resampling methods: k-fold cross-validation (k-CV), repeated k-CV (Rk-CV), Monte Carlo CV (MC-CV) and bootstrap for creating training and validation data sets used for developing, training and validating DL based point cloud classifiers (e.g., PointNet; Qi et al., 2017a), (2) justifying Mean Square Error (MSE) as a statistically consistent estimator, and (3) exploring the use of MSE as a reliable performance metric for supervised DL. Experiments in this paper are performed on both synthetic and real-world aerial laser scanning (ALS) point clouds

Investigation of Stretch and Recovery Property of Weft Knitted Regular Rib Fabric

Weft knitted regular rib (1×1) fabric stretch and recovery property are very tough to control. This project and thesis work have been devoted to studying the effect of variation of stitch length, yarn count, and GSM on the stretch and recovery properties of weft knit regular rib fabric. Three yarn counts, each with 4 level of stitch length, was manufactured for the purpose of this experiment, remaining the machine set up, dyeing and finishing process constant. In this research, it was found that the better stretch and recovery property of regular rib fabric can be produced by using 2.6 mm to 2.65 mm stitch length for yarn count of Ne 28/1 KH and GSM of 195 to 205

Utilization of fermented wheat bran extract medium as a potential low-cost culture medium for Chlorella ellipsoidea

Microalgae, Chlorella ellipsoidea is an excellent energy source for food and biofuel production. Nevertheless, the production cost of C. ellipsoidea using Bold's Basal Medium (BBM) is expensive, which led to exploring the alternation of a low-cost medium for large-scale production. Low-cost fermented wheat bran extract medium (FWBEM), which has good nutritional properties, might be an alternative approach to mass production of C. ellipsoidea. The present study was conducted to evaluate the growth and production of C. ellipsoidea using different concentrations of FWBEM. Wheat bran was fermented at the concentration of 8.33, 6.66, and 5.00 g/L water and used as treatments for T2, T3, and T4, respectively. The BBM was used as the control medium (T1). The growth and production of C. ellipsoidea were monitored at three days intervals through cell dry weight, specific growth rate, optical cell density, chlorophyll a content, and cell numbers. Those growth data revealed that C. ellipsoidea cultured at 6.66 g/L (T3) concentration did not vary significantly with the standard inorganic BBM. However, T2 and T4 showed substantially lower cell growth and chlorophyll a content than control and T3. Compared to the BBM, a significant reduction in production cost was obtained in the FWBEM. Based on the cell biomass growth, pigmentation, and production cost, FWBEM at a 6.66 g/L could be used as an alternative medium to BBM. Therefore, FWBEM has excellent potential to be used for the low-cost production of C. ellipsoidea

A discordance analysis in manual labelling of urban mobile laser scanning data used for deep learning based semantic segmentation

peer reviewedLabelled point clouds are crucial to train supervised Deep Learning (DL) methods used for semantic segmentation. The objective of this research is to quantify discordances between the labels made by different people in order to assess whether such discordances can influence the success rates of a DL based semantic segmentation algorithm. An urban point cloud of 30 m road length in Santiago de Compostela (Spain) was labelled two times by ten persons. Discordances and its significance in manual labelling between individuals and rounds were calculated. In addition, a ratio test to signify discordance and concordance was proposed. Results show that most of the points were labelled accordingly with the same class by all the people. However, there were many points that were labelled with two or more classes. Class curb presented 5.9% of discordant points and 3.2 discordances for each point with concordance by all people. In addition, the percentage of significative labelling differences of the class curb was 86.7% comparing all the people in the same round and 100% comparing rounds of each person. Analysing the semantic segmentation results with a DL based algorithm, PointNet++, the percentage of concordance points are related with F-score value in R2 = 0.765, posing that manual labelling has significant impact on results of DL-based semantic segmentation methods