Cup Plant (Silphium perfoliatum L.) Biomass as Substitute for Expanded Polystyrene in Bonded Leveling Compounds

Biomass for non-food applications is considered as a substitute for petro-based materials such as expanded polystyrene (EPS). This research analyzes physical properties of an EPS containing commercial bonded leveling compound (BLC) which was substituted with cup plant (Silphium perfoliatum L.) biomass. Cup plant is a high-yielding biomass plant with several ecological benefits that is yet mainly used for biogas production. Furthermore, the high amount of parenchyma in senescent biomass with its EPS-like structure could be a possible substitute for petrochemical foams in lightweight aggregates. The natural variation in parenchyma content of several European cup plant accessions is promising, regarding the development of cultivars with suitable biomass properties for the proposed material use. Two binders with different proportions of cup plant and EPS were used to produce samples of BLC for thermal conductivity and compression strength tests. The compression strength of 0.92 N mm−2 and a thermal conductivity of 84 mW m−1 K−1 were analyzed and comparable to the commercial BLC. The thermal conductivity within the tested borders appears nearly independent of the biomass content. With increasing cup plant content, the shape characteristics of the lightweight aggregate mix changes towards more elongated aggregates. The mechanical strength and thermal conductivity are highly sensitive to the water demand of the biomass. Direct partial substitution of EPS by cup plant appears feasible and could be a part of the decarbonization of the construction sector.

A Statistical Model for Analyzing Anthropometric Data and Developing Clothing Sizing Systems for Libyan School Children

A garment sizing system is essential for effective clothing design and production. A sizing system classifies a specific population into homogeneous subgroups based on some key dimensions. Persons of the same subgroup have the same body shape characteristics, and share the same garment size. Anthropometric data plays important role in creating clothing sizing system. The current work represents the sixth step towards the overall goal of developing the Libyan children’s clothing standards system based on physical measurements of the human body of Libyan schoolchildren. The objective of the current work is to study the physical measurements of students aged 6 to 17 years in the stages of primary, secondary. The body measurements of school children in Benghazi were collected and analyzed using simple statistics methods to understand the body ranges and current of student in all stages to develop the system sizing. The measurements were collected from previous projects. Some measurements were collected to complement a work of 90 (male and female) students between 6, 7 and 8 years old from a school in Benghazi. ANOVA test was used to determine differences between age groups.

Minimizing uncertainty in complete automation of Douglas-Peucker Algorithm for geospatial mapping

It is qualitatively evident that the greater the map scale change, the greater the optimal distance threshold of the Douglas-Peucker Algorithm, which is used in polyline simplification. However, no specific quantitative relationships between them are known by far, causing uncertainties in complete automation of the algorithm. To fill this gap, the current paper constructs quantitative relationships based on the spatial similarity theories of polylines. A quantitative spatial similarity relationship model was proposed and evaluated by setting two groups of control experiments and taking <C, T> as coordinates. In order to realize the automatic generalization of the polyline, we verified whether these quantitative relationships could be fitted using the same function with the same coefficients. The experiments revealed that the unary quadratic function is the best, whether the polylines were derived from different or the same geographical feature area(s). The results also show that using the same optimal distance threshold is unreasonable to simplify all polylines from different geographical feature areas. On the other hand, the same geographical feature area polylines could be simplified using the same optimal distance threshold. The uncertainties were assessed by evaluating the automated generalization results for position and geometric accuracy perspectives using polylines from the same geographic feature areas. It is demonstrated that in addition to maintaining the geographical features, the proposed model maintains the shape characteristics of polylines. Limiting the uncertainties would support the realization of completely automatic generalization of polylines and the construction of vector map geodatabases.

Multimedia Concepts on Object Detection and Recognition with F1 Car Simulation Using Convolutional Layers

This paper presents a framework for detecting objects in images based on global features and contours. The first step is a shape matching algorithm that uses the background subtraction process. Object detection is accomplished by an examination of the oversegmentation of the image, where the space of the potential boundary of the object is examined to identify boundaries that have a direct resemblance to the prototype of the object type to be detected. Our analysis method removes edges using bilinear interpolation and reestablishes color sensors as lines and retracts background lines from the previous frame. Object contours are generated with clustered lines. The objects detected will then be recognized using the extraction technique. Here, we analyze the color and shape characteristics with which each object is capable of managing occlusion and interference. As an extension of object detection and recognition, F1 car simulation is experimented with simulation using various layers, such as layer drops, convolutionary layers, and boundary elimination, avoiding obstacles in different pathways.

A Moving Ship Detection and Tracking Method Based on Optical Remote Sensing Images from the Geostationary Satellite

Geostationary optical remote sensing satellites, such as the GF-4, have a high temporal resolution and wide coverage, which enables the continuous tracking and observation of ship targets over a large range. However, the ship targets in the images are usually small and dim and the images are easily affected by clouds, islands and other factors, which make it difficult to detect the ship targets. This paper proposes a new method for detecting ships moving on the sea surface using GF-4 satellite images. First, the adaptive nonlinear gray stretch (ANGS) method was used to enhance the image and highlight small and dim ship targets. Second, a multi-scale dual-neighbor difference contrast measure (MDDCM) method was designed to enable detection of the position of the candidate ship target. The shape characteristics of each candidate area were analyzed to remove false ship targets. Finally, the joint probability data association (JPDA) method was used for multi-frame data association and tracking. Our results suggest that the proposed method can effectively detect and track moving ship targets in GF-4 satellite optical remote sensing images, with better detection performance than other classical methods.

A Framework to Automatic Detect Center Pivots Using Land Use and Land Cover Data

Water management is a key field to support life and economic activity nowadays. The greatly increased mechanization of agriculture, mainly through center pivot irrigation systems, represents a big challenge to control this resource. Irrigated agriculture makes up the large majority of consumptive water use, therefore it is important to identify and quantify these systems. Currently, with 6.95x10⁶ ha, Brazil is among the 10 largest countries in irrigation areas in the world. In this study, a combined Computer Vision and Machine Learning approach is proposed for the identification of center pivots in remote sensing images. The methodology is based on Circular Hough Transform (CHT) and Balanced Random Forest (BRF) classifier using vegetation indices NDVI/SAVI generated from Landsat 8 images and Land Use and Land Cover (LULC) data provided by project MapBiomas. The candidate's circles of pivots identified on images are filtered based on vegetation behavior and shape characteristics of these areas. Our approach was able to detect 7358 pivots, reaching 83.86% of Recall for 52 scenes analyzed overall Brazil compared with mapping done by the Brazilian National Water and Sanitation Agency (ANA). In some scenes, the Recall reaches up to 100%.