Sensor Networks can perceive the extensive area by application of many sensor nodes because the size of sensor nodes is small and cheap. Sensor nodes can transfer multi hop data to sink nodes which is far away than sending and receiving distance. Many routing methods are proposed in order to raise energy efficiency in sensor networks filled. There is a routing method based on structure as a composing method of network by cluster. Cluster technology consisting and maintaining network topology based on cluster is mostly studied in routing protocol. There are demerits that LEACH, EACHS shall be rest energy of all nodes and HEED can't guarantee the number of cluster head. The proposed energy efficiency of selected cluster head guarantees the number of cluster head which is a demerit of HEED and minimizes the node of DEAD.

A distribution system was designed and operated by considering unidirectional power flow from a utility source to end-use loads. The large penetrations of distributed generation (DG) into the existing distribution system causes a variety of technical problems, such as frequent tap changing problems of the on-load tap changer (OLTC) transformer, local voltage rise, protection coordination, exceeding short-circuit capacity, and harmonic distortion. In view of voltage regulation, the intermittent fluctuation of the DG output power results in frequent tap changing operations of the OLTC transformer. Thus, many utilities limit the penetration level of DG and are eager to find the reasonable penetration limits of DG in the distribution system. To overcome this technical problem, utilities have developed a new voltage regulation method in the distribution system with a large DG penetration level. In this paper, the impact of DG on the OLTC operations controlled by the line drop compensation (LDC) method is analyzed. In addition, a generalized determination methodology for the DG penetration limits in a distribution substation transformer is proposed. The proposed DG penetration limits could be adopted for a simplified interconnection process in DG interconnection guidelines.

Malware is any malicious program that can attack the security of other computer systems for various purposes. The threat of malware has significantly increased in recent years. To protect our computer systems, we need to analyze an executable file to decide whether it is malicious or not. In this paper, we propose two malware classification methods: malware classification using Simhash and PCA (MCSP), and malware classification using Simhash and linear transform (MCSLT). PCA uses the symmetrical covariance matrix. The former method combines Simhash encoding and PCA, and the latter combines Simhash encoding and linear transform layer. To verify the performance of our methods, we compared them with basic malware classification using Simhash and CNN (MCSC) using tanh and relu activation. We used a highly imbalanced dataset with 10,736 samples. As a result, our MCSP method showed the best performance with a maximum accuracy of 98.74% and an average accuracy of 98.59%. It showed an average F1 score of 99.2%. In addition, the MCSLT method showed better performance than MCSC in accuracy and F1 score.

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