• Korean Journal of Cognitive Science
• /
• v.34 no.3
• /
• pp.197-226
• /
• 2023

Generative Text Summarization is one of the Natural Language Processing tasks. It generates a short abbreviated summary while preserving the content of the long text. ROUGE is a widely used lexical-overlap based metric for text summarization models in generative summarization benchmarks. Although it shows very high performance, the studies report that 30% of the generated summary and the text are still inconsistent. This paper proposes a methodology for evaluating the performance of the summary model without using the correct summary. AggreFACT is a human-annotated dataset that classifies the types of errors in neural text summarization models. Among all the test candidates, the two cases, generation summary, and when errors occurred throughout the summary showed the highest correlation results. We observed that the proposed evaluation score showed a high correlation with models finetuned with BART and PEGASUS, which is pretrained with a large-scale Transformer structure.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.10
• /
• pp.37-53
• /
• 2023

The AI based intelligent command and control system can automatically analyzes the properties of intricate battlefield information and tactical data. In addition, commanders can receive situation analysis results and battlefield awareness through the system to support decision-making. It is necessary to build a battlefield situation analysis dataset similar to the actual battlefield situation for learning AI in order to provide decision-making support to commanders. In this paper, we explain the next step of the dataset construction method of the existing previous research, 'A Virtual Battlefield Situation Dataset Generation for Battlefield Analysis based on Artificial Intelligence'. We proposed a method to build the dataset required for the final battlefield situation analysis results to support the commander's decision-making and recognize the future battlefield. We developed 'Dataset Generator SW', a software tool to build a learning dataset for battlefield situation analysis, and used the SW tool to perform data labeling. The constructed dataset was input into the Siamese Network model. Then, the output results were inferred to verify the dataset construction method using a post-processing ranking algorithm.

• Coupled systems mechanics
• /
• v.12 no.6
• /
• pp.531-537
• /
• 2023

Municipal solid waste landfills are unpredictable bioreactors which in cases of mishandling and bad supervision presents numerous risks. The key to municipal waste landfills is to approach them from the point of prevention of the possible consequences, which means using methods of organized waste disposal, and also utilizing landfill gas, as an unavoidable consequence with disposal of municipal solid waste with a high share of biodegradable organic matter. This paper presents an overview about problems of solid municipal waste management, type and composition of waste, and an overview of waste management condition. Further, the problem of landfill and landfill gasses is described with the calculation models of landfill production, as well as the use of the SWM GHG Calculator and LandGEM software on a specific example of gas production for the central zone at Sarajevo landfill "Smiljevici". Main focus of this thesis is the analysis of potentials of greenhouse gas emission reduction measures from the waste management. Overview of the best available techniques in waste management is presented as well as the methodology used for calculations. Scenarios of greenhouse gas emission reduction in waste management were defined so that emissions were calculated using the appropriate model. In the final section of the paper, its description of the problem of collection and utilization the landfill gas at the sanitary landfill "Smiljevici", and implementation of the system for landfill gas collection and solution suggestion for the gasification and exploitation of gas. Energy, environmental and economic benefits can be accomplished by utilizing municipal solid waste as fuel in industry and energy and moreover by utilizing energy generation from landfill gas, which this thesis emphasizes.

• Smart Media Journal
• /
• v.12 no.9
• /
• pp.19-27
• /
• 2023

Since morphemes are the smallest unit of meaning in Korean, it is necessary to develop an accurate morphemes analyzer to improve the performance of the Korean language model. However, most existing analyzers present morpheme analysis results by learning word unit tokens as input values. However, since Korean words are consist of postpositions and affixes that are attached to the root, even if they have the same root, the meaning tends to change due to the postpositions or affixes. Therefore, learning morphemes using word unit tokens can lead to misclassification of postposition or affixes. In this paper, we use morpheme-level tokens to grasp the inherent meaning in Korean sentences and propose a morpheme analyzer based on a sequence generation method using Transformer. In addition, a user dictionary is constructed based on corpus data to solve the out - of-vocabulary problem. During the experiment, the morpheme and morpheme tags printed by each morpheme analyzer were compared with the correct answer data, and the experiment proved that the morpheme analyzer presented in this paper performed better than the existing morpheme analyzer.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.46 no.3
• /
• pp.1-6
• /
• 2023

In the realm of dental prosthesis fabrication, obtaining accurate impressions has historically been a challenging and inefficient process, often hindered by hygiene concerns and patient discomfort. Addressing these limitations, Company D recently introduced a cutting-edge solution by harnessing the potential of intraoral scan images to create 3D dental models. However, the complexity of these scan images, encompassing not only teeth and gums but also the palate, tongue, and other structures, posed a new set of challenges. In response, we propose a sophisticated real-time image segmentation algorithm that selectively extracts pertinent data, specifically focusing on teeth and gums, from oral scan images obtained through Company D's oral scanner for 3D model generation. A key challenge we tackled was the detection of the intricate molar regions, common in dental imaging, which we effectively addressed through intelligent data augmentation for enhanced training. By placing significant emphasis on both accuracy and speed, critical factors for real-time intraoral scanning, our proposed algorithm demonstrated exceptional performance, boasting an impressive accuracy rate of 0.91 and an unrivaled FPS of 92.4. Compared to existing algorithms, our solution exhibited superior outcomes when integrated into Company D's oral scanner. This algorithm is scheduled for deployment and commercialization within Company D's intraoral scanner.

• ETRI Journal
• /
• v.45 no.5
• /
• pp.910-921
• /
• 2023

In this study, we designed a 6T-SRAM cell using 16-nm CMOS process and analyzed the performance in terms of read-speed latency. The temperaturedependent Cu and multilayered graphene nanoribbon (MLGNR)-based nanointerconnect materials is used throughout the circuit (primarily bit/bit-bars [red lines] and word lines [write lines]). Here, the read speed analysis is performed with four different chip operating temperatures (150K, 250K, 350K, and 450K) using both Cu and graphene nanoribbon (GNR) nano-interconnects with different interconnect lengths (from 10 ㎛ to 100 ㎛), for reading-0 and reading-1 operations. To execute the reading operation, the CMOS technology, that is, the16-nm PTM-HPC model, and the16-nm interconnect technology, that is, ITRS-13, are used in this application. The complete design is simulated using TSPICE simulation tools (by Mentor Graphics). The read speed latency increases rapidly as interconnect length increases for both Cu and GNR interconnects. However, the Cu interconnect has three to six times more latency than the GNR. In addition, we observe that the reading speed latency for the GNR interconnect is ~10.29 ns for wide temperature variations (150K to 450K), whereas the reading speed latency for the Cu interconnect varies between ~32 ns and 65 ns for the same temperature ranges. The above analysis is useful for the design of next generation, high-speed memories using different nano-interconnect materials.

• Journal of KIBIM
• /
• v.13 no.4
• /
• pp.96-105
• /
• 2023

This study focuses on developing a building object recognition technology for efficient use in the remodeling of buildings constructed without drawings. In the era of the 4th industrial revolution, smart technologies are being developed. This research contributes to the architectural field by introducing a deep learning-based method for automatic object classification and recognition, utilizing point cloud data. We use a TD3D network with voxels, optimizing its performance through adjustments in voxel size and number of blocks. This technology enables the classification of building objects such as walls, floors, and roofs from 3D scanning data, labeling them in polygonal forms to minimize boundary ambiguities. However, challenges in object boundary classifications were observed. The model facilitates the automatic classification of non-building objects, thereby reducing manual effort in data matching processes. It also distinguishes between elements to be demolished or retained during remodeling. The study minimized data set loss space by labeling using the extremities of the x, y, and z coordinates. The research aims to enhance the efficiency of building object classification and improve the quality of architectural plans by reducing manpower and time during remodeling. The study aligns with its goal of developing an efficient classification technology. Future work can extend to creating classified objects using parametric tools with polygon-labeled datasets, offering meaningful numerical analysis for remodeling processes. Continued research in this direction is anticipated to significantly advance the efficiency of building remodeling techniques.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.23 no.1
• /
• pp.167-181
• /
• 2024

Utilization of a driving simulator in the development of autonomous driving technology allows us to perform various tests effectively in criticial environments, thereby reducing the development cost and efforts. However, there exists a serious drawback that the driving simulator has a big difference from the real environment, so a problem occurs when the autonomous driving algorithm developed using the driving simulator is applied directly to the real vehicle system. This is defined as so-called Sim2Real problem and can be classified into scenarios, sensor modeling, and vehicle dynamics. This Paper presensts on a method to solve the Sim2Real problem in autonomous driving simulator focusing on IMU sensor. In order to reduce the difference between emulated virtual IMU sensor real IMU sensor, IMU sensor emulation techniques through precision error modeling of IMU sensor are introduced. The error model of IMU sensors takes into account bias, scale factor, misalignmnet, and random walk by IMU sensor grades.

• Journal of Animal Reproduction and Biotechnology
• /
• v.38 no.4
• /
• pp.199-208
• /
• 2023

Background: Canine induced pluripotent stem cells (iPSCs) are an attractive source for veterinary regenerative medicine, disease modeling, and drug development. Here we used vitamin C (Vc) to improve the reprogramming efficiency of canine iPSCs, and its functions in the reprogramming process were elucidated. Methods: Retroviral transduction of Oct4, Sox2, Klf4, c-Myc (OSKM), and GFP was employed to induce reprogramming in canine fetal fibroblasts. Following transduction, the culture medium was subsequently replaced with ESC medium containing Vc to determine the effect on reprogramming activity. Results: The number of AP-positive iPSC colonies dramatically increased in culture conditions supplemented with Vc. Vc enhanced the efficacy of retrovirus transduction, which appears to be correlated with enhanced cell proliferation capacity. To confirm the characteristics of the Vc-treated iPSCs, the cells were cultured to passage 5, and pluripotency markers including Oct4, Sox2, Nanog, and Tra-1-60 were observed by immunocytochemistry. The expression of endogenous pluripotent genes (Oct4, Nanog, Rex1, and telomerase) were also verified by PCR. The complete silencing of exogenously transduced human OSKM factors was observed exclusively in canine iPSCs treated with Vc. Canine iPSCs treated with Vc are capable of forming embryoid bodies in vitro and have spontaneously differentiated into three germ layers. Conclusions: Our findings emphasize a straightforward method for enhancing the efficiency of canine iPSC generation and provide insight into the Vc effect on the reprogramming process.

• International Journal of Computer Science & Network Security
• /
• v.24 no.1
• /
• pp.226-234
• /
• 2024

The frequent handover problem and playing ping-pong effects in 5G (5th Generation) ultra-dense networking cannot be effectively resolved by the conventional handover decision methods, which rely on the handover thresholds and measurement reports. For instance, millimetre-wave LANs, broadband remote association techniques, and 5G/6G organizations are instances of group of people yet to come frameworks that request greater security, lower idleness, and dependable principles and correspondence limit. One of the critical parts of 5G and 6G innovation is believed to be successful blockage the board. With further developed help quality, it empowers administrator to run many systems administration recreations on a solitary association. To guarantee load adjusting, forestall network cut disappointment, and give substitute cuts in case of blockage or cut frustration, a modern pursuing choices framework to deal with showing up network information is require. Our goal is to balance the strain on BSs while optimizing the value of the information that is transferred from satellites to BSs. Nevertheless, due to their irregular flight characteristic, some satellites frequently cannot establish a connection with Base Stations (BSs), which further complicates the joint satellite-BS connection and channel allocation. SF redistribution techniques based on Deep Reinforcement Learning (DRL) have been devised, taking into account the randomness of the data received by the terminal. In order to predict the best capacity improvements in the wireless instruments of 5G and 6G IoT networks, a hybrid algorithm for deep learning is being used in this study. To control the level of congestion within a 5G/6G network, the suggested approach is put into effect to a training set. With 0.933 accuracy and 0.067 miss rate, the suggested method produced encouraging results.