MEANING THE TREE AS A CONTEMPORARY ART IMAGINE

In the time period extending from the past to the present, the tree appears as both an object and an image not only Turkish culture but also in different cultures. In addition, the tree has taken its place in art and art history as an important asset in human life. The tree, which seems an ordinary object but exists imaginatively in the field of art, has been given deep meanings in history. The tree has sometimes become set into an expression as an object, as a form or as a legend. It has sometimes been the symbol of life and death as the tree of life, the genealogy as an indicator of personal history, or a wishing tree that expresses hopes for the future, and sometimes it has been the object of a political reaction. In this study, it is emphasized how the 'tree' image is valued, how it has experienced a historical change process and how it is interpreted in today's art. Besides, it is focused on creating different meanings artistically through images and reconstruction with changes in meaning also producing new meanings. In summary, in this study, a perspective that criticize the different meaning and change process of the tree image with examples from some contemporary artists is presented.

The Role of Fine Arts Activity in the Development of Artistic Thinking of Schoolchildren

This article deals with the development of artistic and imaginative thinking, imagination and the creation of an artistic image in fine arts lessons using a graphic image. An example of a lesson for elementary school students on creating a mysterious tree image is given, which will help the teacher develop the ability of his students to see and creatively realize their ideas.

A Real-Time Apple Targets Detection Method for Picking Robot Based on Improved YOLOv5

The apple target recognition algorithm is one of the core technologies of the apple picking robot. However, most of the existing apple detection algorithms cannot distinguish between the apples that are occluded by tree branches and occluded by other apples. The apples, grasping end-effector and mechanical picking arm of the robot are very likely to be damaged if the algorithm is directly applied to the picking robot. Based on this practical problem, in order to automatically recognize the graspable and ungraspable apples in an apple tree image, a light-weight apple targets detection method was proposed for picking robot using improved YOLOv5s. Firstly, BottleneckCSP module was improved designed to BottleneckCSP-2 module which was used to replace the BottleneckCSP module in backbone architecture of original YOLOv5s network. Secondly, SE module, which belonged to the visual attention mechanism network, was inserted to the proposed improved backbone network. Thirdly, the bonding fusion mode of feature maps, which were inputs to the target detection layer of medium size in the original YOLOv5s network, were improved. Finally, the initial anchor box size of the original network was improved. The experimental results indicated that the graspable apples, which were unoccluded or only occluded by tree leaves, and the ungraspable apples, which were occluded by tree branches or occluded by other fruits, could be identified effectively using the proposed improved network model in this study. Specifically, the recognition recall, precision, mAP and F1 were 91.48%, 83.83%, 86.75% and 87.49%, respectively. The average recognition time was 0.015 s per image. Contrasted with original YOLOv5s, YOLOv3, YOLOv4 and EfficientDet-D0 model, the mAP of the proposed improved YOLOv5s model increased by 5.05%, 14.95%, 4.74% and 6.75% respectively, the size of the model compressed by 9.29%, 94.6%, 94.8% and 15.3% respectively. The average recognition speeds per image of the proposed improved YOLOv5s model were 2.53, 1.13 and 3.53 times of EfficientDet-D0, YOLOv4 and YOLOv3 and model, respectively. The proposed method can provide technical support for the real-time accurate detection of multiple fruit targets for the apple picking robot.

Introduction: Understanding and Studying Meditation

The purpose of this chapter is twofold. First, it explores the different meanings of meditation and its varieties across Eastern and Western traditions, including the more recent therapeutic developments. Using a Meditation Tree image, specifically created for this volume, the chapter gives examples of practices from these traditions that rely on multiple techniques, such as concentration, recitation, breathing, singing, and visualization of physical or mental objects, among others. It highlights the richness of practices but equally of experiences and expected goals, which have led to debates and tensions among meditation experts and movements for over two thousand years. Second, the chapter summarizes the structure of this volume and the major achievements in the study of meditation, as well as current limitations and controversies.

TREE IMAGE OF SIBERIA: LARCH

The dendroimage image of Siberia is considered in the context of geopoetics and ethnodendrology. For the first time the proposed analysis systematizes the motives associated with the image of larch, one of the main trees in the region. The research materials are scientific works on ethnography and folklore studies, Russian and Russian-language fiction about Siberia written in the XIX-XX centuries. The name of the tree reflects its dual status: coniferous and deciduous simultaneously. The “gender” of the larch is also indeterminate: male / female. The larch has an «intermediate» position in the system of the most important dendroimages of the Siberian text: between cedar and birch. It can be associated with universal tree mythologemes (World Tree, Tree of Life and Death, family tree, etc.), but it most clearly embodies the basic concept of Siberia as a space of violence, hard labor, exile, concentration camps.

Accelerated strategy for the MLEM algorithm

BACKGROUND: A statistical method called maximum likelihood expectation maximization (MLEM) is quite attractive, especially in PET/SPECT. However, the convergence rate of the iterative scheme of MLEM is quite slow. OBJECTIVE: This study aims to develop and test a new method to speed up the convergence rate of the MLEM algorithm. METHODS: We introduce a relaxation parameter in the conventional MLEM iterative formula and propose the relaxation strategy on the condition that the spectral radius of the derived iterative matrix from the iterative scheme with the accelerated parameter reaches a minimum value. RESULTS: Experiments with Shepp-Logan phantom and an annual tree image demonstrate that the new computational strategy effectively accelerates computation time while maintains reasonable image quality. CONCLUSIONS: The proposed new computational method involving the relaxation strategy has a faster convergence speed than the original method.

Analisis Penyebab Kerusakan Mesin Produksi Kayu Lapis

PT. Sama Al-Tanmiah is a company engaged in the semi-finished plywood production industry. Semi-finished raw materials are further processed according to the needs of the buyer, the cause of damage is due to poor maintenance and deteriorating component quality. This research was conducted to determine the origin of the causes of damage to the production machine at PT. Same Al-Tanmiah. Retrieval of data obtained by means of interviews, observation and requesting secondary data for 6 months of production, for data processing using Fault Tree Analysis and Failure Mode and Effect Analysis. The data obtained from the company enters into data management. The next stage will be a fault tree image analysis using Microsoft Visio and weighting to the three production machines using Microsoft Excel. The results of the researchers obtained the root of the fault tree and the value of the RPN with weighted machine damage. Improvement of management and standard operating procedures (SOP).

Ggtree: a serialized data object for visualization of phylogenetic tree and annotation data

Ggtree supports mapping and visualizing associated external data on phylogeny with two general methods. The output of ggtree is a ggtree graphic object that can be rendered as a static image. Most importantly, the input tree and associated data that used in visualization can be extracted from the graphic object, making it an ideal data structure for publishing tree (image, tree and data in one single object) and thus enhance data reuse and analytic reproducibility.

Fast Recognition System for Tree Images based on Caffe Platform and Deep Learning

Aiming at the difficult problem of complex extraction for tree image in the existing complex background, we took tree species as the research object and proposed a fast recognition system solution for tree image based on Caffe platform and deep learning. In the research of deep learning algorithm based on Caffe framework, the improved Dual-Task CNN model (DCNN) is applied to train the image extractor and classifier to accomplish the dual tasks of image cleaning and tree classification. In addition, when compared with the traditional classification methods represented by Support Vector Machine (SVM) and Single-Task CNN model, Dual-Task CNN model demonstrates its superiority in classification performance. Then, in order for further improvement to the recognition accuracy for similar species, Gabor kernel was introduced to extract the features of frequency domain for images in different scales and directions, so as to enhance the texture features of leaf images and improve the recognition effect. The improved model was tested on the data sets of similar species. As demonstrated by the results, the improved deep Gabor convolutional neural network (GCNN) is advantageous in tree recognition and similar tree classification when compared with the Dual-Task CNN classification method. Finally, the recognition results of trees can be displayed on the application graphical interface as well. In the application graphical interface designed based on Ubantu system, it is capable to perform such functions as quick reading of and search for picture files, snapshot, one-key recognition, one-key e