A Scheme of Selecting Vehicles to Assist Download Based on WebGIS for VANET

The assisted download is an effective method solving the problem that the coverage range is insufficient when Wi-Fi access is used in VANET. For the low utilization of time-space resource within blind area and unbalanced download services in VANET, this paper proposes an approximate global optimum scheme to select vehicle based on WebGIS for assistance download. For WebGIS, this scheme uses a two-dimensional matrix to respectively define the time-space resource and the vehicle selecting behavior, and uses Markov Decision Process to solve the problem of time-space resource allocation within blind area, and utilizes the communication features of VANET to simplify the behavior space of vehicle selection so as to reduce the computing complexity. At the same time, Euclidean Distance(Metric) and Manhattan Distance are used as the basis of vehicle selection by the proposed scheme so that, in the case of possessing the balanced assisted download services, the target vehicles can increase effectively the total amount of user downloads. Experimental results show that because of the wider access range and platform independence of WebGIS, when user is in the case of relatively balanced download services, the total amount of downloads is increased by more than 20%. Moreover, WebGIS usually only needs to use Web browser (sometimes add some plug-ins) on the client side, so the system cost is greatly reduced.

Genetic Variability and G×E Interactions in a Diverse Set of Groundnut Accessions

Background: Knowledge of the genetic diversity for various agronomic traits and their interaction with the environment and subsequent classification of genotypes will be beneficial for identification of divergent and stable sources of agronomic traits. Methods: A set of 96 groundnut germplasm accessions belonging to four botanical groups were evaluated for three years (2017 to 2019) for pod yield and component traits using AMMI analysis and subsequently accessions were classified based Euclidean cluster analysis. Result: Among different botanical groups, Virginia genotypes matured late and possessed high SPAD chlorophyll meter readings (SCMR) and pod yield compared to Spanish types. The component traits of pod maturity like days to flowering (first and 50%) showed low heritability and high genotype × environment interaction (GEI) and significant negatively affected sound mature kernel (SMK) and shelling per centage (SP). The cumulative contribution of environment and GEI component to the total variance was the highest in the expression of SP (67%) followed by days to maturity (54%) and days to 50% flowering (52%). Euclidean distance-based cluster analysis grouped the 96 accessions into five major clusters. Cluster I had accessions with higher pod yield, whereas cluster V contained accessions with low SLA, high SCMR and moderate pod yield. High yielding as well as stable accessions identified based on AMMI stability value (ASV) are NRCG 17332, 10076, 17268, 17197, 17108, 10106, 10089 and 17165. Trait specific as well as stable accessions identified in the present study can be useful donors for groundnut breeding programme.

Object Relocation Visual Tracking Based On Histogram Filter And Siamese Network

Siamese network based trackers formulate the visual tracking mission as an image matching process by regression and classification branches, which simplifies the network structure and improves tracking accuracy. However, there remain many problems as described below. 1) The lightweight neural networks decreases feature representation ability. The tracker is easy to fail under the disturbing distractors (e.g., deformation and similar objects) or large changes in viewing angle. 2) The tracker cannot adapt to variations of the object. 3) The tracker cannot reposition the object that has failed to track. To address these issues, we first propose a novel match filter arbiter based on the Euclidean distance histogram between the centers of multiple candidate objects to automatically determine whether the tracker fails. Secondly, Hopcroft-Karp algorithm is introduced to select the winners from the dynamic template set through the backtracking process, and object relocation is achieved by comparing the Gradient Magnitude Similarity Deviation between the template and the winners. The experiments show that our method obtains better performance on several tracking benchmarks, i.e., OTB100, VOT2018, GOT-10k and LaSOT, compared with state-of-the-art methods.

Multiclass Interactive Martial Arts Teaching Optimization Method Based on Euclidean Distance

Aiming at the problems of low optimization accuracy, poor optimization effect, and long running time in current teaching optimization algorithms, a multiclass interactive martial arts teaching optimization method based on the Euclidean distance is proposed. Using the K-means algorithm, the initial population is divided into several subgroups based on the Euclidean distance, so as to effectively use the information of the population neighborhood and strengthen the local search ability of the algorithm. Imitating the school's selection of excellent teachers to guide students with poor performance, after the “teaching” stage, the worst individual in each subgroup will learn from the best individual in the population, and the information interaction in the evolutionary process will be enhanced, so that the poor individuals will quickly move closer to the best individuals. According to different learning levels and situations of students, different teaching stages and contents are divided, mainly by grade, supplemented by different types of learning groups in the form of random matching, so as to improve the learning ability of members with weak learning ability in each group, which effectively guarantees the diversity of the population and realizes multiclass interactive martial arts teaching optimization. Experimental results show that the optimization effect of the proposed method is better, which can effectively improve the accuracy of algorithm optimization and shorten the running time of the algorithm.

DDMF: A Method for Mining Relatively Important Nodes Based on Distance Distribution and Multi-Index Fusion

In research on complex networks, mining relatively important nodes is a challenging and practical work. However, little research has been done on mining relatively important nodes in complex networks, and the existing relatively important node mining algorithms cannot take into account the indicators of both precision and applicability. Aiming at the scarcity of relatively important node mining algorithms and the limitations of existing algorithms, this paper proposes a relatively important node mining method based on distance distribution and multi-index fusion (DDMF). First, the distance distribution of each node is generated according to the shortest path between nodes in the network; then, the cosine similarity, Euclidean distance and relative entropy are fused, and the entropy weight method is used to calculate the weights of different indexes; Finally, by calculating the relative importance score of nodes in the network, the relatively important nodes are mined. Through verification and analysis on real network datasets in different fields, the results show that the DDMF method outperforms other relatively important node mining algorithms in precision, recall, and AUC value.

COMPUTATIONLESS PALM-PRINT VERIFICATION USING WAVELET ORIENTED ZERO-CROSSING SIGNATURE

Palmprints can be characterized by their texture and the patterns of that texture dominate in a vertical direction. Therefore, the energy of the coefficients in the transform domain is more concentrated in the vertical sideband. Using this idea, this paper proposes the characterization of the texture features of the palmprint using zero-crossing signatures based on the dyadic discrete wavelet transform (DWT) to effectively identify an individual. A zero-crossing signature of 4 x 256 was generated from the lower four resolution levels of dyadic DWT in the enrolment process and stored in the database to identify the person in recognition mode. Euclidean distance was determined to find the best fit for query palmprints zero-crossing signature from the dataset. The proposed algorithm was tested on the PolyU dataset containing 6000 multi-spectral images. The proposed algorithm achieved 96.27% accuracy with a lower recognition time of 0.76 seconds. ABSTRAK: Pengesan Tapak Tangan boleh dikategorikan berdasarkan ciri-ciri tekstur dan corak pada tekstur yang didominasi pada garis tegak. Oleh itu, pekali tenaga di kawasan transformasi adalah lebih penuh pada jalur-sisi menegak. Berdasarkan idea ini, cadangan kajian ini adalah berdasarkan ciri-ciri tekstur pada tapak tangan dan tanda pengenalan sifar-silang melalui transformasi gelombang kecil diadik yang diskret (DWT) bagi mengecam individu. Pada mod pengecaman, tanda pengenalan sifar-silang 4 x 256 yang terhasil daripada tahap diadik resolusi empat terendah DWT digunakan dalam proses kemasukan dan simpanan di pangkalan data bagi mengenal pasti individu. Jarak Euklidan yang terhasil turut digunakan bagi memperoleh padanan tapak tangan paling sesuai melalui tanda pengenalan sifar-silang dari set data. Algoritma yang dicadangkan ini diuji pada set data PolyU yang mengandungi 6000 imej pelbagai-spektrum. Algoritma yang dicadangkan ini berjaya mencapai ketepatan sebanyak 96.27% dengan durasi pengecaman berkurang sebanyak 0.76 saat.

Artificial intelligence technologies for more flexible recommendation in uniforms

PurposeThis research aims to collect human body variables via 2D images captured by digital cameras. Based on those human variables, the forecast and recommendation of the Digital Camouflage Uniforms (DCU) for Taiwan's military personnel are made.Design/methodology/approachA total of 375 subjects are recruited (male: 253; female: 122). In this study, OpenPose converts the photographed 2D images into four body variables, which are compared with those of a tape measure and 3D scanning simultaneously. Then, the recommendation model of the DCU is built by the decision tree. Meanwhile, the Euclidean distance of each size of the DCU in the manufacturing specification is calculated as the best three recommendations.FindingsThe recommended size established by the decision tree is only 0.62 and 0.63. However, for the recommendation result of the best three options, the DCU Fitting Score can be as high as 0.8 or more. The results of OpenPose and 3D scanning have the highest correlation coefficient even though the method of measuring body size is different. This result confirms that OpenPose has significant measurement validity. That is, inexpensive equipment can be used to obtain reasonable results.Originality/valueIn general, the method proposed in this study is suitable for applications in e-commerce and the apparel industry in a long-distance, non-contact and non-pre-labeled manner when the world is facing Covid-19. In particular, it can reduce the measurement troubles of ordinary users when purchasing clothing online.

Analisis Kualitas Pengelompokkan Algoritma K-Means di Knime dan Excel untuk PTMT Pasca Vaksinasi Covid-19

Pembelajaran tatap muka yang melibatkan interaksi diantara teman dan guru sangat sulit digantikan dengan pembelajaran jarak jauh. Vaksinasi pendidik dan tenaga kependidikan menjadi salah satu prioritas negara dalam upaya akselerasi pembelajaran tatap muka. Pendidik dan tenaga kependidikan di satuan pendidikan Kota Yogyakarta telah melaksanakan vaksinasi COVID-19 secara lengkap pada April 2021, sehingga SMK Negeri 4 Yogyakarta wajib menyediakan layanan pembelajaran tatap muka terbatas (PTMT) dan memfasilitasi pembelajaran jarak jauh. Pengelompokkan yang berkualitas dibutuhkan untuk membatasi jumlah peserta didik yang mendapatkan layanan PTMT sesuai dengan standar protokol kesehatan. Proses pengelompokan sekumpulan data ke dalam klaster yang memiliki kemiripan ditentukan dengan perhitungan jarak. Penelitian ini melakukan pengelompokkan peserta didik dengan aplikasi KNIME dan microsoft excel. Selanjutnya dilakukan analisis perhitungan kualitas pengelompokkan dari kedua aplikasi untuk mendapatkan prototype pengelompokkan terbaiknya. Perhitungan manual memanfaatkan rumus euclidean distance untuk menghitung jarak antar dokumen dan metode silhouette coefficien untuk menghitung perbedaan karakteristik (perbedaan jarak) diantara cluster dan persamaan karakteristi (kedekatan jarak) data-data didalam satu cluster. Hasil pengklasteran algoritma K-Means keluaran dari aplikasi KNIME memiliki kualitas 0,3208 (lemah) sedangkan manual excel memiliki kualitas 0,6331 (sedang), sehingga hasil manual excel direkomendasikan untuk membantu kurikulum sekolah mengelompokkan peserta didik yang akan diberikan layanan PTMT.