The optimal shipping location is determined by measuring the volume and weights of cargo shipped to non-standard cargo carriers. Currently, workers manually measure cargo volume, but automate it to improve work inefficiency. In this paper, we proposed the method of a real-time volume calculation using LiDar sensor for automating cargo measurement of non-standard cargo. For this purpose, we utilized the statistical techniques for data preprocessing and volume calculation, also used Voxel Grid filter to light weighted of data which are appropriate in real-time calculation. We implemented the function of Normal vectors and Triangle Mesh to generate surfaces and Alpha Shapes algorithms to process 3D modeling.

In this paper, direction normalization and CNN deep learning are used to develop a more reliable license plate recognition system. The existing license plate recognition system consists of three main modules: license plate detection module, character segmentation module, and character recognition module. The proposed system minimizes recognition error by adding a direction normalization module when a detected license plate is inclined. Experimental results show the superiority of the proposed method in comparison to the previous system.

In the manufacturing environment, research to minimize robot collisions with human beings have been widespread, but in order to interact with robots, it is important to precisely recognize and predict human actions. In this research, after enhancing performance by applying group normalization to the Hourglass model to detect the operator motion, the skeleton was estimated and data were created using this model. And then, three types of operator's movements were recognized using LSTM. As results of the experiment, the accuracy was enhanced by 1% using group normalization, and the recognition accuracy was 99.6%.

Although the object detection accuracy with a single image has been significantly improved with the advance of deep learning techniques, the detection accuracy for pig monitoring is challenged by occlusion problems due to a complex structure of a pig room such as food facility. These detection difficulties with a single image can be mitigated by using a video data. In this research, we propose a method in pig detection for video monitoring environment with a static camera. That is, by using both image processing and deep learning techniques, we can recognize a complex structure of a pig room and this information of the pig room can be utilized for improving the detection accuracy of pigs in the monitored pig room. Furthermore, we reduce the execution time overhead by applying a pruning technique for real-time video monitoring on an embedded board. Based on the experiment results with a video data set obtained from a commercial pig farm, we confirmed that the pigs could be detected more accurately in real-time, even on an embedded board.

In supervised learning, labeling of all data is essential, and in particular, in the case of object detection, all objects belonging to the image and to be learned have to be labeled. Due to this problem, continual learning has recently attracted attention, which is a way to accumulate previous learned knowledge and minimize catastrophic forgetting. In this study, a continaul learning model is proposed that accumulates previously learned knowledge and enables learning about new objects. The proposed method is applied to CenterNet, which is a object detection model of anchor-free manner. In our study, the model is applied the knowledge distillation algorithm to be enabled continual learning. In particular, it is assumed that all output layers of the model have to be distilled in order to be most effective. Compared to LWF, the proposed method is increased by 23.3%p mAP in 19+1 scenarios, and also rised by 28.8%p in 15+5 scenarios.

Recently, the market for masks has been growing due to air pollution, sun protection, pollen allergies and other reasons. In addition, the demand for masks has increased dramatically due to the new coronavirus from 2020, and masks are still one of the necessities of life. Although the reliance on masks is increasing, there are many inconveniences associated with wearing masks for long periods of time. At the same time, technology is developing rapidly, and the demand for smart wearable devices is increasing. Therefore, at the moment when the fourth industrial revolution is underway, combining people's common necessities with IoT technology to bring new convenient experiences to people is an important direction for future technology development and product development. In this study, smart masks were designed and implemented using IoT(Internet of Things) technology. The mask uses a microcomputer Adafruit circuit playground express, using the microcomputer's LED, optical sensors, can be in the dark place light, and through the temperature sensor real-time grasp of body temperature changes. If the body temperature rises above normal, the LED will turn "on" and activate the voice sensor to warn yourself and others around you.

This study analyzed the shot pattern through the tempo of segmented shot duration and studied the relationship with the unique emotion of the story. The structure of the story was classified into 3 chapters, 17 sequences, 83 scenes, 287 beats, and 1636 shots. Shot density is a method of visualizing tension in visual storytelling, and since it is a result obtained by mathematically calculating the density of divided shots, it can be helpful in designing tension delivered to the audience. Nine shot density patterns were extracted. The ascending(+) type was classified as A, B, C, D, 4, the descending(-) type, E, F, G, H, 4, and the maintenance(/) type, I, 1 type. Based on the spatiality of the 17 stages of Campbell's heroic narrative and McGee's story structure, the narrative level of the tree structure was proposed, and the symbolic meaning of the shot rhythm in the practical aspect of the story function was proposed to present a systematic methodology in the direction of production.

Starting from the content types of video advertisements produced by social media users, this paper conducts a comparative study on the information and entertainment video advertising effects of the same product, to help video producers achieve better advertising effects when facing different products. Experimental results show that. In Garment, the information is higher than that of entertainment in terms of advertising cognition. In terms of advertising attitude and purchase intention, the entertainment is higher than that of information; in food, the entertainment is higher than that of information in terms of advertising cognition, advertising attitude, and purchase intention; in household items, the entertainment is higher than that of information; in terms of advertising cognition and purchase intention, the information is higher than that of entertainment; in terms of advertising attitude, the entertainment is higher than that of information; in cosmetics, the information is higher than that of entertainment in terms of advertising cognition; in terms of advertising attitude and purchase intention, the entertainment is higher than that of the information; in digital accessories, the information is higher than that of the entertainment in terms of advertising cognition, advertising attitude and purchase intention.

Menopausal women have to care for their health. Menopause is a symptom that needs to be managed in the long term, which requires safe and economical treatment even after long term use. This study proposes digital-based exercise therapy. This study also suggests resistance exercise and walking exercise methods according to the proof of previous studies. It provides motivational contents for those who cannot perform regularly. As motivational methods, self-efficacy and narrative-based history methods are accepted. Self-efficacy is improved through voice agents feedback during exercise. Feedback is a type of messages such as encouragement and motivation. The narrative-based history consists of characters of visualization that improves through values, which leveraged by the exercise.

The most important factor in pre-school children's psychological perception is ease of learning, and the closest measure is "natural" interaction. This study aims to explore the potential of tangible user interfaces (TUI) for AR collaboration for children's cognitive development. The conceptual model is constructed by analyzing physical interaction, spatial perception and social collaboration on the usability of TUI, to explore the role of TUI in pre-school children's cognition. In the empirical study, children aged 3-6 were taken as research objects. The experimental tool is "Plugo" education application. Parents answered questionnaires after observing their children's use. Research shows that physical interaction are the most critical factor in TUI. TUI is beneficial to the cultivation of spatial ability. The results are as follows: 1. Cronbach's Alpha and KMO were 0.921 and 0.965, which were significant and passed the reliability and validity test. 2. Through confirmatory factor analysis (model fit index, combinatorial validity), we found that physical interaction were closely related to usability. 3. The path analysis of the relationship proves that usability has a significant impact on the cultivation of pre-school children's spatial ability.