摘要：
Scientific classification of land production functions can promote the efficient use of land. In this paper, 16 land production functions were collected, and 10 characteristic features of land production function were put forward according to the different attributes of each function. The characteristics of the land production functions are assigned to obtain the land production function score table, Then use the hierarchical clustering and K-means clustering analysis those data, and the results of three kinds of hierarchical clustering and one K-means clustering were obtained. By comparing with the results of sequence classification, 16 land production functions are classified into three categories.
关键词：
Knowledge graph;Agricultural thesaurus;Schema layer;Triple;Mathematical model
摘要：
Google proposed the concept of knowledge graph to improve the quality of searching results in 2012, so the knowledge graph brought a hot topic in the field of academic and industry. The knowledge graph can effectively improve the searching quality and the accuracy of Q & A system, which is a hot issue. With the help of agricultural experts, this paper based on the Agricultural Thesaurus, determines the rules for judging whether a thesaurus is a concept or an entity, establishes maps from Agricultural Thesaurus to the agricultural knowledge graph schema layer and the data layer. Therefore, the large-scale automatic building is realized from the Agricultural Thesaurus to the agricultural knowledge graph. In order to effectively manage and utilize triples, this paper proposes a mathematical model for the management of triples with the RDF-based triple storage pattern, which lays the solid foundation for the semantic-based agricultural information retrieving and the construction of the Q & A system.
摘要：
The meteorological factors play an important role in rice yield. In this paper, according to the current agricultural meteorological factors on the impact of agricultural production, an the rice yield prediction model was established by using multiple stepwise regression analysis. The experimental results show that the average forecast accuracy is more than 98%, and the prediction result is consistent with the trend of the measured results, and the prediction results are credible.
摘要：
The rapid development of many open source and commercial image editing software makes the authenticity of the digital images questionable. Copy-move forgery is one of the most widely used tampering techniques to create desirable objects or conceal undesirable objects in a scene. Existing techniques reported in the literature to detect such tampering aim to improve the robustness of these methods against the use of JPEG compression, blurring, noise, or other types of post processing operations. These post processing operations are frequently used with the intention to conceal tampering and reduce tampering clues. A robust method based on the color moments and other five image descriptors is proposed in this paper. The method divides the image into fixed size overlapping block. Clustering operation divides entire search space into smaller pieces with similar color distribution. Blocks from the tampered regions will reside within the same cluster since both copied and moved regions have similar color distributions. Five image descriptors are used to extract block features, which makes the method more robust to post processing operations. An ensemble of deep compositional pattern-producing neural networks are trained with these extracted features. Similarity among feature vectors in clusters indicates possible forged regions. Experimental results show that the proposed method can detect copy-move forgery even if an image was distorted by gamma correction, addictive white Gaussian noise, JPEG compression, or blurring. (C) 2016 Published by Elsevier Ireland Ltd.
关键词：
Cache;Multi-core;task scheduling;schedule length;average response time
摘要：
In the past few years, multi-core processors incorporating four, six, eight, or more cores on a single die have become ubiquitous. Those cores, having their own private caches, often share a higher level cache memory, which leads to compete among different tasks. This can seriously affect the average performance of multi-core systems as the probability of cache hit could be lowered. In realizing this, we study the problem of scheduling bag-of-tasks (BoT) applications with shared cache constraint on multi-core systems. We first use cache space isolation techniques to divide shared caches into partitions. Then, we give a motivational example and outline the shared cache aware scheduling problem of multi-core systems. Finally, to provide an optimum solution for this problem, we propose a heuristic shared cache contention aware scheduling (SCAS) algorithm on multi-core systems. Our extensive simulation performance evaluation study clearly demonstrate that our proposed SCAS algorithm outperforms the existing traditional scheduling algorithm Min-min and the modified algorithm MSCAS in terms of schedule length and average response time.
摘要：
Massive events can be produced today because of the rapid development of the Internet of Things (IoT). Complex event processing, which can be used to extract high-level patterns from raw data, has become an essential part of the IoT middleware. Prediction analytics is an important technology in supporting proactive complex event processing. In this paper, we propose the use of dynamic Bayesian model averaging to develop a high-accuracy prediction analytic method for large-scale IoT application. This method, which is based on a new multilayered adaptive dynamic Bayesian network model, uses Gaussian mixture models and expectation-maximization inference for basic Bayesian prediction. Bayesian model averaging is implemented by using Markov chain Monte Carlo approximation, and a novel dynamic Bayesian model averaging method is proposed based on event context clustering. Simulation experiments show that the proposed prediction analytic method has better accuracy compared to traditional methods. Moreover, the proposed method exhibits acceptable performance when implemented in large-scale IoT applications.
