Research on Parallel KD-Tree Construction for Ray Tracing

Zhang Peicheng,Xu Huahu,Bian Minjie,Honghao 보안공학연구지원센터 2016 International Journal of Grid and Distributed Comp Vol.9 No.11

Many computer graphics rendering algorithms and techniques use ray tracing for generation of photo-realistic images, and kd-tree is the most popular acceleration data structure for ray tracing. In order to avoid the inefficient parallel performance of kd-tree construction based on surface area heuristic (SAH), an algorithm using Morton code and path compression was present in this paper. Instead of building a kd-tree layer-by-layer, the proposed approach can be performed in parallel from bottom of a conceptual perfect binary tree. Experimental results on GPU show that our work achieves a faster kd-tree construction procedure.

Evaluation of the close degree of the spatial relationship between natural enemies of tea gardens and Frankliniella intonsa based on variance analysis of cluster samples

Honghao Cheng,Shiyan Chen,Xiaomeng Wu,Yue Xu,Lin Zhang,Jiazhao Sun,Xiazhi Zhou,Yunding Zou,Shoudong Bi 한국곤충학회 2022 Entomological Research Vol.52 No.6

The aim of this paper is to clarify the difference of spatial closeness between natural enemies and Frankliniella intonsa, and to provide a scientific basis for rational protection and utilization of natural enemies. This paper applied the block-sample square difference analysis method, Gray correlation degree method, the aggregation intensity index method and ρ index method to study the difference in the closeness of the spatial relationship between F. intonsa and natural enemies when the number of F. intonsa and their natural enemies was the highest and the minimum area occupied by F. i n t o n s a individuals and colonies in five tea plantations in Hefei, Anhui province in 2021 and four tea plantations in 2020. The results were as follows: 1) Tetragnatha squamata Karsch, Xysticus ephippiafus, Erigonidium graminicolum and Theridion Octomaculatum were the top four natural enemies closely related to F. intonsa in 2021. The top three natural enemies closely related to F. intonsa in 2020 were Tetragnatha squamata Karsch, Xysticus ephippiafus and Theridion Octomaculatum. Tw o y e a r s a g o , Tetragnatha squamata Karsch and Xysticus ephippiafus were the same. 2) Cluster size did not change the distribution patterns of natural enemies and F. intonsa. 3) The aggregation of F. intonsa was caused by their own causes or some environmental factors, and the aggregation of natural enemies was caused by environmental factors. 4) In 2021, the minimum area occupied by individuals and groups of F. intonsa in tea gardens was 2m2, and in 2020, it was 8 m2.

Analysis of the following effect of the natural enemies with Frankliniella intonsa in tea garden

Cheng Honghao,Chen Shiyan,Wu Xiaomeng,Xu Yue,Zhang Lin,Sun Jiazhao,Zhou Xiazhi,Zou Yunding,Bi Shoudong 한국곤충학회 2022 Entomological Research Vol.52 No.8

In order to reasonably protect and utilize natural enemies for comprehensive control of Frankliniella intonsa, a systematic investigation was conducted on F. i n t o n s a and natural enemies in tea gardens in Hefei, Anhui Province, and the spatial following effect of natural enemies on F. intonsa was studied. The semi-variogram of natural enemies and thrips was obtained by the geostatistics method, and the correlation degree between them was analyzed by the grey correlation degree analysis method. The higher the correlation degree between natural enemies and F. intonsa, the closer the relationship between natural enemies and F. intonsa was. In this study, we analyzed the relationship between 11 natural enemies of F. i n t o n s a in tea gardens from October 28, 2020 to November 20, 2020 and from August 22, 2021 to November 19, 2021. The top five natural enemies with the largest closeness index to F. intonsa in Huangshan Dayezhong tea garden are Oxyopes sertatus, Theridion octomaculatum, Plexippus setipes, Xysticus ephippiafus and Erigonidium graminicolum. The top five natural enemies with the largest closeness index to F. intonsa in Pingyang Tezao tea garden are Erigonidium graminicolum, Plexippus setipes, Xysticus ephippiafus, Oxyopes sertatus and Clubiona japonicola.Among the top five natural enemies in the two tea gardens, those in common are Oxyopes sertatus, Erigonidium graminicolum, Xysticus ephippiafus and Plexippus setipes. According to the summation of the closeness index and the sum of the serial numbers, the top five natural enemies closely related to the spatial following of F. intonsa in the tea gardens were Oxyopes sertatus, Plexippus setipes, Erigonidium graminicolum, Xysticus ephippiafus and Theridion octomaculatum. One of the factors closely related to the space of F. i n t o n s a in the same tea garden was the ratio of F. i n t o n s a to a certain natural enemy. The smaller the ratio was, the closer the following relationship was. The results of this study provide scientific basis for biological control and natural enemy protection of F. intonsa.

Direct position tracking method for non-circular signals with distributed passive arrays via first-order approximation

Jinke Cao,Xiaofei Zhang,Honghao Hao Electronics and Telecommunications Research Instit 2024 ETRI Journal Vol.46 No.3

In this study, a direct position tracking method for non-circular (NC) signals using distributed passive arrays is proposed. First, we calculate the initial positions of sources using a direct position determination (DPD) approach; next, we transform the tracking into a compensation problem. The offsets of the adjacent time positions are calculated using a first-order Taylor expansion. The fusion calculation of the noise subspace is performed according to the NC characteristics. Because the proposed method uses the signal information from the previous iteration, it can realize automatic data associations. Compared with traditional DPD and two-step localization methods, our novel process has lower computational complexity and provides higher accuracy. Moreover, its performance is better than that of the traditional tracking methods. Numerous simulation results support the superiority of our proposed method.

Expression of fox-related genes in the skin follicles of Inner Mongolia cashmere goat

Wenjing Han,Xiaoyan Li,Lele Wang,Honghao Wang,Kun Yang,Zhixin Wang,Ruijun Wang,Rui Su,Zhihong Liu,Yanhong Zhao,Yanjun Zhang,Jinquan Li 아세아·태평양축산학회 2018 Animal Bioscience Vol.31 No.3

Objective: This study investigated the expression of genes in cashmere goats at different periods of their fetal development. Methods: Bioinformatics analysis was used to evaluate data obtained by transcriptome sequencing of fetus skin samples collected from Inner Mongolia cashmere goats on days 45, 55, and 65 of fetal age. Results: We found that FoxN1, FoxE1, and FoxI3 genes of the Fox gene family were probably involved in the growth and development of the follicle and the formation of hair, which is consistent with previous findings. Real-time quantitative polymerase chain reaction detecting system and Western blot analysis were employed to study the relative differentially expressed genes FoxN1, FoxE1, and FoxI3 in the body skin of cashmere goat fetuses and adult individuals. Conclusion: This study provided new fundamental information for further investigation of the genes related to follicle development and exploration of their roles in hair follicle initiation, growth, and development.

Multiobjective Space Search Optimization and Information Granulation in the Design of Fuzzy Radial Basis Function Neural Networks

Wei Huang,Sung-Kwun Oh,Honghao Zhang 대한전기학회 2012 Journal of Electrical Engineering & Technology Vol.7 No.4

This study introduces an information granular-based fuzzy radial basis function neural networks (FRBFNN) based on multiobjective optimization and weighted least square (WLS). An improved multiobjective space search algorithm (IMSSA) is proposed to optimize the FRBFNN. In the design of FRBFNN, the premise part of the rules is constructed with the aid of Fuzzy C-Means (FCM) clustering while the consequent part of the fuzzy rules is developed by using four types of polynomials, namely constant, linear, quadratic, and modified quadratic. Information granulation realized with C-Means clustering helps determine the initial values of the apex parameters of the membership function of the fuzzy neural network. To enhance the flexibility of neural network, we use the WLS learning to estimate the coefficients of the polynomials. In comparison with ordinary least square commonly used in the design of fuzzy radial basis function neural networks, WLS could come with a different type of the local model in each rule when dealing with the FRBFNN. Since the performance of the FRBFNN model is directly affected by some parameters such as e.g., the fuzzification coefficient used in the FCM, the number of rules and the orders of the polynomials present in the consequent parts of the rules, we carry out both structural as well as parametric optimization of the network. The proposed IMSSA that aims at the simultaneous minimization of complexity and the maximization of accuracy is exploited here to optimize the parameters of the model. Experimental results illustrate that the proposed neural network leads to better performance in comparison with some existing neurofuzzy models encountered in the literature.

Multiobjective Space Search Optimization and Information Granulation in the Design of Fuzzy Radial Basis Function Neural Networks

Huang, Wei,Oh, Sung-Kwun,Zhang, Honghao The Korean Institute of Electrical Engineers 2012 Journal of Electrical Engineering & Technology Vol.7 No.4

This study introduces an information granular-based fuzzy radial basis function neural networks (FRBFNN) based on multiobjective optimization and weighted least square (WLS). An improved multiobjective space search algorithm (IMSSA) is proposed to optimize the FRBFNN. In the design of FRBFNN, the premise part of the rules is constructed with the aid of Fuzzy C-Means (FCM) clustering while the consequent part of the fuzzy rules is developed by using four types of polynomials, namely constant, linear, quadratic, and modified quadratic. Information granulation realized with C-Means clustering helps determine the initial values of the apex parameters of the membership function of the fuzzy neural network. To enhance the flexibility of neural network, we use the WLS learning to estimate the coefficients of the polynomials. In comparison with ordinary least square commonly used in the design of fuzzy radial basis function neural networks, WLS could come with a different type of the local model in each rule when dealing with the FRBFNN. Since the performance of the FRBFNN model is directly affected by some parameters such as e.g., the fuzzification coefficient used in the FCM, the number of rules and the orders of the polynomials present in the consequent parts of the rules, we carry out both structural as well as parametric optimization of the network. The proposed IMSSA that aims at the simultaneous minimization of complexity and the maximization of accuracy is exploited here to optimize the parameters of the model. Experimental results illustrate that the proposed neural network leads to better performance in comparison with some existing neurofuzzy models encountered in the literature.

Genetic Diversity and Population Structure of the Xanthomonas campestris pv. campestris Strains Affecting Cabbages in China Revealed by MLST and Rep-PCR Based Genotyping

Guo Chen,Congcong Kong,Limei Yang,Mu Zhuang,Yangyong Zhang,Yong Wang,Jialei Ji,Zhiyuan Fang,Honghao Lv 한국식물병리학회 2021 Plant Pathology Journal Vol.37 No.5

Xanthomonas campestris pv. campestris (Xcc) is the causal agent of black rot for cruciferous vegetables worldwide, especially for the cole crops such as cabbage and cauliflower. Due to the lack of resistant cabbage cultivars, black rot has brought about considerable yield losses in recent years in China. Understanding of the pathogen features is a key step for disease prevention, however, the pathogen diversity, population structure, and virulence are largely unknown. In this study, we studied 50 Xcc strains including 39 Xcc isolates collected from cabbage in 20 regions across China, us- ing multilocus sequence genotyping (MLST), repetitive DNA sequence-based PCR (rep-PCR), and pathogenicity tests. For MLST analysis, a total of 12 allelic profiles (AP) were generated, among which the largest AP was AP1 containing 32 strains. Further cluster analysis of rep-PCR divided all strains into 14 DNA groups, with the largest group DNA I comprising of 34 strains, most of which also belonged to AP1. Inoculation tests showed that the representative Xcc strains collected from diverse regions performed differential virulence against three brassica hosts compared with races 1 and 4. Interestingly, these results indicated that AP1/DNA I was not only the main pathotype in China, but also a novel group that differed from the previously reported type races in both genotype and virulence. To our knowledge, this is the first extensive genetic diversity survey for Xcc strains in China, which provides evidence for cabbage resistance breeding and opens the gate for further cabbage-Xcc interaction studies.

Fast Pedestrian Detection with Adaboost Algorithm Using GPU

Chong Chao Cai,Jue Gao,Bian Minjie,Peicheng Zhang,Honghao Gao 보안공학연구지원센터 2015 International Journal of Database Theory and Appli Vol.8 No.6

Pedestrian detection is one of the hot research problems in computer vision field. The Cascade AdaBoost System is a commonly used algorithm in this region. However, when the training datasets become larger, it is still a time consuming process to build one Adaboost classifier. In this paper we detail an implementation of the AdaBoost algorithm using the NVIDIA CUDA framework based on the haar features as feature vectors, and downscaling with integral image. The result shows that we can get nearly 6x from the standard code to with our CPU implementation to achieve a near real-time performance and ensure better classification results in misjudgment.

A Novel Facile and Green Synthesis Protocol to Prepare High Strength Regenerated Silk Fibroin/SiO2 Composite Fiber

Qiang Liu,Zhaohui Meng,Ronghui Wu,Liyun Ma,Wu Qiu,Honghao Zhang,Shuihong Zhu,Lingqing Kong,Zijjie Xu,Aniruddha Patil,Xiangyang Liu 한국섬유공학회 2019 Fibers and polymers Vol.20 No.10

In this work, regenerated silk fibroin (RSF) and silicon dioxide (SiO2) composite fiber was successfully extrudedby wet spinning method. The effect of SiO2 addition on structure of the composite fiber at microscopic level is studied, whichsubsequently correlated to the mechanical performance. The best concentration ratio for composite fiber is identified byscreening SiO2 concentration from 0.025 w/w% to 0.5 w/w%. The experimental results revealed that the SiO2 at a lowconcentration of 0.1 w/w% was well distributed. The breaking stress, breaking strain and Young’s modulus at 0.1 w/w% SiO2addition of the RSF fibers increased considerably compared to the neat RSF fibers from 243±3 to 458±21 MPa, 51±4 % to54±7 % and 6.34±0.55 to 11.69±1.12 GPa, respectively. To the best of our knowledge, this is the first report for RSF/SiO2composite fiber. We believed the insight provided in this report which looks into the structural evolution should be beneficialto the future design and building of other advanced functional fibers.