2023, Vol.9, No.3

Journal of Internet of Things and Convergence 2023 KCI Impact Factor : 0.89

pISSN : 2466-0078
https://journal.kci.go.kr/kiots

One of the economical ways to satisfy the quality of service desired by the user in a web service environment is to adjust the size of the object. To this end, this study finds the maximum size of objects that satisfy this constraint when the mean response time is given below an arbitrary threshold for quality of service. It can be inferred that in the steady state of system, the mean response time in the deterministic model by using the round-robin will be the same as that of the queueing model following the general distribution. Based on this, analytical formulas and procedures for finding the maximum object size are obtained. As a service distribution of web traffic, the Pareto distribution is appropriate, so the maximum object size is computed by applying the M/G(Pareto)/1 model and the M/G/1/PS model using exponential distribution as computational experience. Performance evaluation through numerical calculation shows that as the shape parameter in the Pareto distribution increases, the M/G(Pareto)/1 model and M/G/1/PS model have the same maximum object size. The results of this study can be used to environments where objects can be sized for economical web service control.

One of the most important characteristics of digital forensics is integrity. Integrity means that the data has not been tampered with. If evidence is collected during digital forensic and later tampered with, it cannot be used as evidence. With analog evidence, it's easy to see if it's been tampered with, for example, by taking a picture of it. However, the data on the storage media, or digital evidence, is invisible, so it is difficult to tell if it has been tampered with. Therefore, hash values are used to prove that the evidence data has not been tampered with during the process of collecting evidence and submitting it to the court. The hash value is collected from the stored data during the evidence collection phase. However, due to the internal behavior of NAND flash memory, the physical data shape may change over time from the acquisition phase. In this paper, we study the characteristics and techniques of flash memory that can cause the physical shape of flash memory to change even if no intentional data corruption is attempted.

In this paper, we investigated the implementation of a hairstyle recommendation system based on big data and deepfake technology. The proposed hairstyle recommendation system recognizes the facial shapes based on the user's photo (image). Facial shapes are classified into oval, round, and square shapes, and hairstyles that suit each facial shape are synthesized using deepfake technology and provided as videos. Hairstyles are recommended based on big data by applying the latest trends and styles that suit the facial shape. With the image segmentation map and the Motion Supervised Co-Part Segmentation algorithm, it is possible to synthesize elements between images belonging to the same category (such as hair, face, etc.). Next, the synthesized image with the hairstyle and a pre-defined video are applied to the Motion Representations for Articulated Animation algorithm to generate a video animation. The proposed system is expected to be used in various aspects of the beauty industry, including virtual fitting and other related areas. In future research, we plan to study the development of a smart mirror that recommends hairstyles and incorporates features such as Internet of Things (IoT) functionality.