2015, Vol.10, No.5

Journal of Knowledge Information Technology and Systems 2023 KCI Impact Factor : 0.89

pISSN : 1975-7700 / eISSN : 2734-0570
https://journal.kci.go.kr/kkits

Recently, in the multi-server environment, many studies have been conducted based upon remote user authentication scheme by dynamic identifier. For the access of legally permitted user to application server and protection of sensitive information, smart card and bio-based password authentication scheme were published. Lee-Lin-Chang's scheme found a vulnerability of Hsiang-Shih's scheme and announced an improved scheme in 2011. But Lee et al's scheme showed weakness to various attacks when the smart card was stolen or third party eavesdropped on network. Also, it is vulnerable in smart card stolen attack, malicious user attacks, server attacks (impersonation attacks)　and replay attacks since the shared information of each server and registration center are the same. In this paper, I suggest the improved scheme which is secure to user and server impersonation attack, replay attack, smart card stolen attack and password guessing attack by designing to allow access to multi-server with only one registration to the registration center rather than separating registration for each server. Efficiency of security, convenience and operating cost are enhanced by only using one-way hash function.

UAV are widely used in such affected areas of lifesaving, delivery and aerial photography. The most important thing in the function of unmanned aircraft equipped is to ensure stability. As the accident rate of a drone reaches 100 times that of general aircraft, the operation at the low altitude has caused the collisions with a number of structures. Therefore, studies for ensuring the safety of an unmanned aircraft, have become an important field. In this paper, the touch on measures to ensure the safety of the unmanned aircraft is covered. The most basic is implementation of reliable hovering in safety. Hovering has a method of using a GPS signal. However, hovering cannot be available when the GPS signal is lost or it has a room flight. In this paper, a possible stable hovering manner based on the optical flow is suggested. Therefore, the operation was tested, using the setting program, after an optical flow sensor is set to flight control board APM2.5. Then, the actual travel distance of the unmanned aircraft was calculated by implementing an algorithm for correcting the error in moving distance corresponding the altitude. Finally, using the calculated X and the Y coordinate value, it corrected the position of the unmanned aircraft. Position correction is adjusted by using the Roll and Pitch values. As a result, stable hovering in capable unmanned aircraft was implemented even if it was impossible to use the GSP signal under indoor flight.

Presenting the relationship of gene and gene is Gene Regulatory Networks(GRN). In the relationship of gene and gene, one of the target gene is affected by regulate genes. Gene regulatory network is divided into two ways by regulate gene activate expression of target gene and repress it. This paper predicted time series expression E.coli data to reconstruction Gene Regulatory Networks. E.coli used in test data is composed of eight genes and has 50 time point. To predict a one target gene, the rest is used for regulator genes. To predict E.coli data, it used Neural Network with Weighted Fuzzy Membership Function(NEWFM). E.coli Data which has time series is used Wavelet for feature extraction. Feature is used for prediction of a Gene Regulatory Networks. By the NEWFM, features were trained and selected minimum feature by Weighted Fuzzy Membership Function Bounded Sum. Data value of selected features is defuzzificated by Takagi-Sugeno value, and by using previous time expression value of regulator genes predicted current time expression value of target gene. Predicted value indicated mean square error(MSE). As comparing, it shows that predicted result is improved by each MSE result; NFRN algorithm is 0.12%, EK algorithm is 0.12%, and NEWFM is 0.003%.