An energy-efficient data collection scheme using denoising autoencoder in wireless sensor networks

Sun

et al. 2020

Preprint

In stock farming, the body size parameters and weight of yaks can reasonably reﬂect the growth and development characteristics, production performance and genetic characteristics of yaks. However, it is diﬃcult for herders to measure the body size and weight of yaks by traditional manual methods. Fortunately, with the development of edge computing, herders can use mobile devices to estimate the yak’s body size and weight. The purpose of this paper is to provide a machine vision-based yak weight estimation method for the edge equipment and establish a yak estimation comprehensive display system based on the user’s use of the edge equipment in order to maximize the convenience of herdsmen’s work. In our method, a set of yak image foreground extraction and measurement point recognition algorithm suitable for edge equipment were developed to obtain yak’s measurement point recognition image, and the ratio between body sizes was transmitted to the cloud server. Then, the body size and weight of yaks were estimated using the data mining method, and the body size estimation data were constantly displayed in the yak estimation comprehensive display system. 25 yaks in different age groups were randomly selected from the herd to perform experiments. The experimental results show that the foreground extraction method can obtain segmentation image with good boundary, and the yak measurement point recognition algorithm has good accuracy and stability. The average error between the estimated values and the actual measured values of body height, oblique length, chest depth, cross height and body weight is 1.95%, 3.11%, 4.91%, 3.35% and 7.79%, respectively. Compared with the traditional manual measurement method, the use of mobile end to estimate the body size and weight of yaks can improve the measurement eﬃciency, facilitate the herdsmen to breed yaks, reduce the stimulation of manual measurement on yaks, and lay a solid foundation for the ﬁne breeding of yaks in Sanjiangyuan region.

Section: The Establishment For a Comprehensive Display System For Yakmentioning

confidence: 99%

Body Weight Estimation of Yak Based on Cloud Edge Computing

Sun

et al. 2020

Preprint

“…The inferred parameters make it most likely that these results will appear. If the probability distribution of trust is p(x), the credibility of subject i is t i , and the probability of honest recommendation of subject i is equal to its credibility, the transaction result with subject j is x i , j, and the neighbor node of subject i is recorded as n(i), then the MLE speculation method can be reduced to solving the maximum value of expression (2).…”

Section: Maximum Likelihood Estimation Of Blockchain Webmentioning

confidence: 99%

“…Lassar believes that technology can achieve the effect of multiplication of value through sharing, and cooperation innovation of enterprise technology is one of the most effective ways to achieve technology sharing and is a reliable guarantee for enterprises to establish a sustainable competitive advantage. It will become a kind of technology innovation of many enterprises [2]. Collaborative innovation has usually based on the common interests of partners, and it has based on the premise of sharing resources or complementing each other.…”

Section: Introductionmentioning

confidence: 99%

Research on trust mechanism of cooperation innovation with big data processing based on blockchain

Liu

Zou

2019

J Wireless Com Network

In recent years, the status of cooperation and innovation has become increasingly important. Research on the behavior of production, education, and research organizations has attracted widespread attention. Blockchain technology is considered as another subversive technology following cloud computing, wireless sensor networks, and big data. It is highly concerned by governments, financial institutions, and technology companies. Blockchain technology is essentially a technical solution that collectively maintains a reliable database through a decentralized, high-trust way. Blockchain WEB technology has not only used in financial and other fields, but also has a great potential for application in the fields of production, education, and research. It has expected to play an important role in the establishment of a trust mechanism in the "Internet +" production, research, and innovation and promote education. Drawing on the experience in blockchain applications in the financial sector, the blockchain in the education sector mainly embodies six application modes: building individual science credit data, creating an intelligent education Taobao platform, developing a degree certificate system, and building a new ecology of open educational resources. Of course, due to the uniqueness and complexity of the trust field in the cooperation innovation of industry, universities, and research institutes, the application of blockchain technology is also faced with difficulties in the application, promotion, and operation of production and research, fuzzy ownership of educational big data, limited data storage space, and potential safety problems of blockchain technology. There are many challenges such as the privacy protection risks of teachers and students.

“…Zhang et al [9][10][11] researched related data mining techniques such as business service recommendation and service quality query, Chen et al [12][13] researched data for business intelligence and business service computing. Data mining technology is also widely used in e-commerce, media, energy services, automotive engineering and other fields [14][15][16][17][18][19] .…”

Section: Introductionmentioning

confidence: 99%

Artificial Immune Network Clustering Based on a Cultural Algorithm

Deng

Yang

Liu

2020

Preprint

Data mining technology has been applied in many fields. Prototype-based cluster analysis is an important data mining method, but its ability to discover knowledge is limited because of the need to know the number of target data categories and cluster prototypes in advance. Artificial immune evolutionary network clustering is a clustering method based on network structure. Compared with prototype-based cluster analysis, it has the advantage of realizing unsupervised learning and clustering without any prior knowledge of data. However, artificial immune evolutionary network clustering also has problems such as a lack of guidance in the clustering process, fuzzy boundary sensitivity, and difficulty in determining parameters. To solve these problems, an artificial immune network clustering algorithm based on a cultural algorithm is proposed. First, three kinds of knowledge are constructed: normative knowledge is used to regulate the spatial range of population initialization to avoid blindness; state knowledge is used to distinguish the type of antigen, and immune defense measures are taken to prevent the network structure caused by noise and boundaries from being unclear; topology knowledge is used to guide the antigen for optimal antibody search. Second, topology knowledge in the cultural algorithm is used to characterize the distribution of antigens and antibodies in space, and elite learning is used to improve the traditional clone mutation operator. Based on the shadow set theory, a method for adaptively determining the compression threshold is proposed. Finally, the results of simulation experiments show that the proposed algorithm can effectively overcome the above problems, and the clustering performances on a synthetic dataset and an actual dataset are satisfactory.