As mechanical facilities are interacting with each other, the failure of some equipment can affect the entire system, so it is necessary to quickly detect and diagnose the abnormality of mechanical equipment. This study proposes a deep learning model that can effectively diagnose abnormalities in rotating machinery and equipment. CNN is widely used for feature extraction and LSTMs are known to be effective in learning sequential information. In LSTM, the number of parameters and learning time increase as the length of input data increases. In this study, we propose a method of segmenting an input segment signal into shorter-length sub-segment signals, sequentially inputting them to CNN through a time-distributed method for extracting features, and inputting them into LSTM. A failure diagnosis test was performed using the vibration data collected from the motor for ventilation equipment installed at the urban railway station. The experiment showed an accuracy of 99.784% in fault diagnosis. It shows that the proposed method is effective in the fault diagnosis of rotating machinery and equipment.

Currently Spatial Data is collected and processed in various methods, and its usability is very high. However, the existing Spatial Data analysis Software usually requires professional knowledge in the collection, refinement, and application of spatial Date, making it difficult to access and apply it. Therefore, this study established a new WebGIS platform with improved accessibility and usability to solve these problems. This platform supports various services such as master map sharing, spatial data generation, automatic coordinate system conversion, WMS issuance, grid generation, and grid analysis. These services increase operational convenience, such as simplifying repetitive tasks and automatically expressing text files. While it is believed that non-experts can easily and conveniently because of them to simplify and express the results. In addition, it is judged to have high accuracy and reliability compared to the analysis results using the existing Open Source-based GIS software.

Wireless sensor network(WSN) is being used in various fields such as reconnaissance, echelon scale identification, weather observation, etc. using small sensors in an environment without a network infrastructure environment. In addition, WSN uses limited battery power, so study on routing protocols to extend the lifetime of the network is important. PEGASIS, a hierarchical routing protocol, accounts for the majority of energy-efficient routing protocol studies and is well known as a representative protocol. In this study, based on PEGASIS, we propose a protocol that constructs multiple chains rather than one chain using the AoA of nodes. The proposed protocol has the advantage of reducing the transmission distance of nodes and eliminating unnecessary transmissions, thereby increasing energy efficiency compared to the existing protocols.

Energy conversion to renewable energy is being promoted to solve the recently serious environmental pollution problem. Solar energy is one of the promising natural renewable energy sources. Compared to other energy sources, it is receiving great attention because it has less ecological impact and is sustainable. It is important to predict power generation at a future time in order to maximize the output of solar energy and ensure the stability and variability of power. In this paper, solar power generation data and sensor data were used. Using the PCC(Pearson Correlation Coefficient) analysis method, factors with a large correlation with power generation were derived and applied to the GAM(Generalized Additive Model). And the prediction accuracy of the power generation prediction model was judged. It aims to derive efficient solar power generation in the future and improve power generation performance.

Due to the situation of the widespread of the coronavirus, which causes the problem of lack of face image data occluded by masks at recent time, in order to solve the related problems, this paper proposes a method to generate face images with masks using a combination of generative adversarial networks and spatial transformation networks based on CNN model. The system we proposed in this paper is based on the GAN, combined with multi-scale convolution kernels to extract features at different details of the human face images, and used Wasserstein divergence as the measure of the distance between real samples and synthetic samples in order to optimize Generator performance. Experiments show that the proposed method can effectively put masks on face images with high efficiency and fast reaction time and the synthesized human face images are pretty natural and real.

With the prosperity of Internet, the game industry is occuping an important position in the national economic system. The study on the game industry which has been drawing eyes from researchers is of great significance. Many scholars have done researches on game motivations, but the elements of intrinsic motivation are still in the exploratory stage. This study determines the dimensions of the influencing factors of players 'intrinsic game motivation and their game experience by adopting the method of literature research. A theoretical model is established to explore the influence of intrinsic game motivation on players' game experience. Questionnaires from players of "The Honor of Kings" are collected through the Internet. SPSS 21.0 is utilized to test the model and hypotheses, and finally the results of the data are analysed. It verifies that intrinsic motivation mainly consists of four components: achievement, social interaction, immersion and escapism. By exploring the influence of the four elements on players' experience, it is concluded that the players' immersion and social interaction have a greater positive impact on their game experience. It is indicated that a better game design highlights the storyline of the game and the interaction system is of importance to enable players to gain a better game experience.

As human brands, Idols, the central members of K-pop, are actively engaging in various industries based on the support of their fandoms, greatly impacting the national economy. In particular, due to the development of digital devices and technologies, they are active both online and offline, and idol avatars are a significant part of their activities across diverse contexts. Therefore, this study aims to categorize avatars used in various contexts and explore fandoms' perceptions according to each type. Based on case studies and literature review, idol avatar usage was categorized into products, supplier-led content, and interactive content, and accordingly, surveys and in-depth interviews were conducted. As a result, regardless of category, fandom showed a negative perception of idol avatar usage, as shown in the order of interactive content, supplier-led content, and products. Regarding human brand characteristics of idol avatars, attractiveness was evaluated higher than intimacy and likeability. However, the attachment to real-life idols showed a higher correlation with the characteristics of intimacy and likeability than attractiveness. This study is significant as it looked at the future directions of idol avatar usage by analyzing the fandoms' perception of avatar usage in the K-pop industry from a human brand perspective.

Through previous studies, it was found that news from legacy media, including television, has an agenda-setting effect and priming effect on the perceptions and attitudes of audiences about politics and war, and that film media also has an agenda-setting effect and political priming effect on war issues. However, it is difficult to find studies on the effects of war-related TV documentaries on media audiences. Therefore, in this study, An experimental study was conducted to investigate whether there is a change in 'recognition of the importance of security', 'will for South-North Unification' and 'will to participate in war in case of emergency' for the audience who watched the KBS special <100 Days of Invasion of Ukraine, Part 1 into the Fire>. As a result of the analysis, it was found that watching a war-related TV documentary reinforced the audience's 'recognition of the importance of security' and 'will for South-North Unification'. However, it was confirmed that watching a war-related TV documentary did not strengthen the audience's will to participate in war in case of emergency.

The present study aims to design an application for anger control training of individuals and test its practical usability with the goal of encouraging preventive training in daily life. This study also investigates, through usability evaluation, whether users can use the application to carry out the actual anger management training program, whether it is useful and convenient, and whether it produces adequate learning effects. In order to conduct usability evaluation, a usability evaluation scale comprised of six factors-utility, reuse intention, learning, error, and reflectivity-was derived, and survey items tailored to each factor were produced. The association between usability evaluation elements, user demographic parameters, mobile usage behavior, and state anger was also examined. The result demonstrated that additional menus and features are necessary to increase the usability of the application for anger management. The result also revealed that it is vital to build an intuitive application interface that users unfamiliar with mobile app functionality can easily navigate, as well as to add entertaining components in the content, as users may be somewhat bored. On the basis of the findings, ideas of modifying and creating anger management training programs were discussed.

Numerous studies have attempted to present converting 2D drawings to 3D. However, converting 3D shapes to exactly how a person thinks is challenging because 2D drawings include numerous variables and possibilities. This study focuses on visualizing 2D to 3D in a commonly used 3D animation software required in animation education or the 3D industry. We implemented our SketchTo3D tool to add the editing and automatic texture assigning method from the imported 2D image into the 3D software that previously had to be done manually. As a result, the SketchTo3D tool saves time to immediately visualize the composition, shape, and volume to express the 3D character, providing an opportunity to break down the barrier between 2D and 3D.

Most of the existing machine reading research has used Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) algorithms as networks. Among them, RNN was slow in training, and Question Answering Network (QANet) was announced to improve training speed. QANet is a model composed of CNN and self-attention. CNN extracts semantic and syntactic information well from the local corpus, but there is a limit to extracting the corresponding information from the global corpus. Graph Convolutional Networks (GCN) extracts semantic and syntactic information relatively well from the global corpus. In this paper, to take advantage of this strength of GCN, we propose I-QANet, which changed the CNN of QANet to GCN. The proposed model performed 1.2 times faster than the baseline in the Stanford Question Answering Dataset (SQuAD) dataset and showed 0.2% higher performance in Exact Match (EM) and 0.7% higher in F1. Furthermore, in the Korean Question Answering Dataset (KorQuAD) dataset consisting only of Korean, the learning time was 1.1 times faster than the baseline, and the EM and F1 performance were also 0.9% and 0.7% higher, respectively.

Crop diseases affect crop production, more than 30 billion USD globally. We proposed a classification study of crop species and diseases using deep learning algorithms for corn, cucumber, pepper, and strawberry. Our study has three steps of species classification, disease detection, and disease classification, which is noteworthy for using captured images without additional processes. We designed deep learning approach of deep learning convolutional neural networks based on Mask R-CNN model to classify crop species. Inception and Resnet models were presented for disease detection and classification sequentially. For classification, we trained Mask R-CNN network and achieved loss value of 0.72 for crop species classification and segmentation. For disease detection, InceptionV3 and ResNet101-V2 models were trained for nodes of crop species on 1,500 images of normal and diseased labels, resulting in the accuracies of 0.984, 0.969, 0.956, and 0.962 for corn, cucumber, pepper, and strawberry by InceptionV3 model with higher accuracy and AUC. For disease classification, InceptionV3 and ResNet 101-V2 models were trained for nodes of crop species on 1,500 images of diseased label, resulting in the accuracies of 0.995 and 0.992 for corn and cucumber by ResNet101 with higher accuracy and AUC whereas 0.940 and 0.988 for pepper and strawberry by Inception.