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The operation of electric power industry is closely linked to the smooth development of society, so the study on the using efficiency of power resources is significant. Based on the study sample, the data from 2011 to 2018, this paper analyzes the annual using efficiency of power resources based on DEA model, obtains the slack variable values of input and output, calculates the projection values of invalid variables and gets that the amount of power resources used in power industry is relatively higher. This theory avoids the research on the cost problem of each investment, and completely transforms multi-input and multi-production into fractional problem, so that applying DEA to measure using efficiency can combine input with production, and evaluation indicators finally obtained are more comprehensive and representative. It can be observed from the analysis results that the using efficiency of electrical energy also significantly affects the using efficiency of power resources, so improving the using methods and making all resources allocated relatively optimal can maximize the output, in order to make the using efficiency of power resources reach the maximum value.

Aiming at the actual needs of industrial robots in the handling process of sorting and picking according to the types of objects, a design scheme of visual inspection and workpiece recognition based on deep learning is proposed. The solution uses deep learning to model the object recognition system, builds the mapping relationship between the image and the object category, and realizes the effective recognition of the object image information. After the analysis is completed, the industrial robot classifies the object according to the given information. Experiments show that the use of deep learning image detection mode can realize the classification and picking operation of industrial robots, which can meet the needs of some actual scene operations.

Target recognition is a very important part in the field of image processing. The effect of target recognition directly affects the success or failure of subsequent processing. This paper proposed a dynamic target recognition method based on correlation tracking algorithm. The correlation tracking algorithm only needs matching the original data of grey level of related images, no feature extraction or division is required for images. In this case, all information of this series of images can be kept, and the correlation tracking algorithm also has many advantages, no complex pre-processing of the original images is required, and the grey level of images can be used to directly matching key points, so the possibility of implementation is greatly increased. Moreover, under the condition of low noise, matching also can be carried out, and there is no high demand for the quality of sequential images. In such an approach, its local anti-interference ability is very strong. The correlation tracking algorithm can be used to predict the trajectory of a moving object in real life. The experimental results show that the proposed method can detect the objects in an image by providing the region of objects and the background with different colors. The conclusions can be drawn that the proposed method can successfully tackle the object recognition task in most cases.

An improved RSA cryptosystem based on chaotic synchronization is newly presented in this paper. First, the design of a discrete sliding mode controller (DSMC) is proposed to make the master and slave chaotic systems synchronized, then a modified RSA encryption algorithm is proposed. In the traditional RSA cryptosystem, the public key needs to be open or transmitted in the public channel which provides an opportunity to attack. In this design, the public key is hidden and does not appear in public channels. Therefore, not only RSA's security features can be retained, but also can significantly promote the security of traditional RSA cryptosystem.

Privacy, trust and confidentiality are the main issues of sensor layer in Internet of Things (IOT). In sensor layer, the sensing information must be transmitted from sensor node to gather node by multiple hopping. Every sensor node will be the sender and the relay node to forward the sensing information sent by other nodes. In this paper, BS embeds S+1 secret into the keys that are derived from the real identity of sensor node, to be as the private secret keys for every sensor node. One of the secret is the common secret that is owned by all sensor nodes, the rest S secretes are the random secret used for making mutual trust. Based on the secret index set and private secret keys, we propose a mutual trust method to explore the trust types and the pairing value among the sensor nodes without any negotiating process. To deny the right of malicious nodes to make trust with other nodes, we involve a group key to hash the secret index set to do making trust and pairing value mutually. Based on the trust types and the pairing values, the sensor nodes can set a secure path to deliver the sensing information to base station.

Motion estimation have a significant impact in H.265 video coding systems because it occupies a large amount of time in encoding. So the quality of motion search algorithm affect the entire encoding efficiency directly. In this paper, a novel search algorithm which utilizes an adaptive hexagon and small diamond search is proposed to overcome the drawbacks of the traditional block matching algorithm implemented in the most current video coding standards. The adaptive search pattern is chosen according to the motion strength of the current block. When the block is in active motion, the hexagon search provides an efficient search means; when the block is inactive, the small diamond search is adopted. Simulation results showed that our approach can speed up the search process with little effect on distortion performance compared with other adaptive approaches.

Motion estimation has an important effect in H.265 video coding systems because it occupies a large amount of time in encoding system. So the quality of motion search selection affects the entire encoding efficiency directly. In this paper, a novel search algorithm which utilises an adaptive hexagon and small diamond search is proposed to overcome the drawbacks of the traditional block matching selection implemented in most current video coding standards. The adaptive search algorithm is chosen according to the motion strength of the current block. When the block is in active motion, the hexagon search provides an efficient search method; when the block is inactive, the small diamond search is adopted. Experimental simulation results showed that our adaptive search algorithm can speed up the search process with little effect on distortion performance compared with other traditional approaches.