• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1663-1669
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• 2021

This paper considers the cooperative control problem for multiple leaders and a single follower with interactions. The leaders are controllable, and the follower has interactions with all leaders and is controlled by the interactions. Then, we study the cooperative control problem that achieves the consensus by controlling the leaders. The leaders and the follower are modeled by the single-integrator and the double-integrator, respectively, and it is assumed that the interactions have the nonlinearities. The leaders can estimate the interaction between the follower and exchange the estimated information with neighbors. Then, this paper proposes the consensus-based cooperative control algorithm using the information exchange of the estimated interactions and the virtual velocity variables to achieve the velocity consensus. We analyze the convergence of the agents to the common state based on the Lasalle's Invaraince Principle. Finally, we provide the numerical example to validate the theoretical results.

• Natural Product Sciences
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• v.28 no.2
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• pp.45-52
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• 2022

The identification of Plasmodium falciparum enoyl acyl-carrier protein reductase (pfENR) is considered as a potential biological target against malaria. Trema orientalis is considered a rich source of phytochemicals useful in malaria treatment. This study evaluated the in-vitro inhibitory activity of the extract and isolated compounds of T. orientalis leaf; the isolated compounds and the analogues of the most active compound were subjected to in-silico molecular docking studies on pfENR. The methanolic extract of T. orientalis was subjected to repeated chromatographic separation which led to the isolation of some compounds. The isolated compounds from the plant were examined for their antimalarial activity using β-hematin inhibition assay. Virtual screening via molecular docking and ADMET studies were conducted to gain insight into the mechanism of binding of ligand and to identify effective pfENR inhibitors. The isolated compounds and the analogues of the most active isolates were gotten from PubChem library for use in docking study. Hexacosanol and β-sitosterol showed inhibition of the β-hematin formation. The docking results showed that hexacosanol, β-sitosterol and the analogues of β-sitosterol displayed binding energy ranging between -6.1 kcal/mol and -11.6 kcal/mol. Sitosterol glucoside has the highest docking score. Some of the ligands showed more binding affinity than known bioactive compounds used as reference. Analogues of β-sitosterol has been shown to be potential inhibitors of pfENR, therefore, the findings from this study suggest that sitosterol glucoside and ergosterol peroxide could act as antimalarial agents after further lead optimisation investigations.

• Journal of the Korea Computer Graphics Society
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• v.30 no.3
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• pp.109-123
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• 2024

Research into vision-based end-to-end autonomous driving systems utilizing deep learning and reinforcement learning has been steadily increasing. These systems typically encode continuous and high-dimensional vehicle states, such as location, velocity, orientation, and sensor data, into latent features, which are then decoded into a vehicular control policy. The complexity of urban driving environments necessitates the use of state representation learning through networks like Variational Autoencoders (VAEs) or Convolutional Neural Networks (CNNs). This paper analyzes the impact of different image state encoding methods on reinforcement learning performance in autonomous driving. Experiments were conducted in the CARLA simulator using RGB images and semantically segmented images captured by the vehicle's front camera. These images were encoded using VAE and Vision Transformer (ViT) networks. The study examines how these networks influence the agents' learning outcomes and experimentally demonstrates the role of each state representation technique in enhancing the learning efficiency and decision- making capabilities of autonomous driving systems.

• International Journal of High-Rise Buildings
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• v.12 no.3
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• pp.225-234
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• 2023

Even at this point in the era of digital transformation, we are still facing many problems in the safety sector that cannot prevent the occurrence or spread of human casualties. When you are in an unexpected emergency, it is often difficult to respond only with human physical ability. Human casualties continue to occur at construction sites, manufacturing plants, and multi-use facilities used by many people in everyday life. If you encounter a situation where normal judgment is impossible in the event of an emergency at a life site where there are still many safety blind spots, it is difficult to cope with the existing manual guidance method. New variable guidance technology, which combines artificial intelligence and digital twin, can make it possible to prevent casualties by processing large amounts of data needed to derive appropriate countermeasures in real time beyond identifying what safety accidents occurred in unexpected crisis situations. When a simple control method that divides and monitors several CCTVs is digitally converted and combined with artificial intelligence and 3D digital twin control technology, intelligence augmentation (IA) effect can be achieved that strengthens the safety decision-making ability required in real time. With the enforcement of the Serious Disaster Enterprise Punishment Act, the importance of distributing a smart location guidance system that urgently solves the decision-making delay that occurs in safety accidents at various industrial sites and strengthens the real-time decision-making ability of field workers and managers is highlighted. The smart location guidance system that combines artificial intelligence and digital twin consists of AIoT HW equipment, wireless communication NW equipment, and intelligent SW platform. The intelligent SW platform consists of Builder that supports digital twin modeling, Watch that meets real-time control based on synchronization between real objects and digital twin models, and Simulator that supports the development and verification of various safety management scenarios using intelligent agents. The smart location guidance system provides on-site monitoring using IoT equipment, CCTV-linked intelligent image analysis, intelligent operating procedures that support workflow modeling to immediately reflect the needs of the site, situational location guidance, and digital twin virtual fencing access control technology. This paper examines the limitations of traditional fixed passive guidance methods, analyzes global technology development trends to overcome them, identifies the digital transformation properties required to switch to intelligent variable smart location guidance methods, explains the characteristics and components of AI-based public facility smart fire safety integrated system (ISFS).

• The Journal of the Acoustical Society of Korea
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• v.43 no.2
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• pp.214-224
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• 2024

Sonobuoys are disposable devices that utilize sound waves for information gathering, detecting engine noises, and capturing various acoustic characteristics. They play a crucial role in accurately detecting underwater targets, making them effective detection systems in anti-submarine warfare. Existing sonobuoy deployment methods in multistatic systems often rely on fixed patterns or heuristic-based rules, lacking efficiency in terms of the number of sonobuoys deployed and operational time due to the unpredictable mobility of the underwater targets. Thus, this paper proposes an optimal sonobuoy placement strategy for Unmanned Aerial Vehicles (UAVs) to overcome the limitations of conventional sonobuoy deployment methods. The proposed approach utilizes reinforcement learning in a simulation-based experimental environment that considers the movements of the underwater targets. The Unity ML-Agents framework is employed, and the Proximal Policy Optimization (PPO) algorithm is utilized for UAV learning in a virtual operational environment with real-time interactions. The reward function is designed to consider the number of sonobuoys deployed and the cost associated with sound sources and receivers, enabling effective learning. The proposed reinforcement learning-based deployment strategy compared to the conventional sonobuoy deployment methods in the same experimental environment demonstrates superior performance in terms of detection success rate, deployed sonobuoy count, and operational time.

• Korean small business review
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• v.43 no.2
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• pp.59-86
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• 2021

The platform concept has been used for business operations in various forms: product platforms, transaction platforms and industry platforms. All these platforms have common characteristics of having 'core' that is reused frequently and 'peripherals' that are less reusable and changed often. Companies use platforms to enable efficient development and creation of product family, transactions and innovation. These platforms provide new opportunities for many small and medium-sized companies (SMEs) by bringing changes to traditional industrial structures focused on the products rather than platforms. The service robotics industry in South Korea is mainly composed of technology-intensive SMEs due to its small market size. Although these SMEs succeed in developing technologies, they have difficulties creating and expanding markets to sell products. Thus, this study addresses the characteristics and problems of the South Korean service robotics industry and analyses how ROBOPRINT, one of the SMEs in the service robotics industry, successfully creates and continuously expands the service robot market by adopting platform concept. The results indicate that ROBOPRINT has been applying two types of platforms: product and transaction platforms. First, ROBOPRINT created art robots that were apartment mural service robots. Rather than selling art robots, the company developed various robots such as painting robots, building exterior wall-cleaning robots by reusing the core technology of the robots. The company also developed various robots according to the buyers request. In addition, the company used the robots to directly provide apartment mural services for customers. This mural service has been extended into various areas, not only in apartments but also in soundproof walls, underground passages, and retaining walls. Besides, ROBOPRINT added new services continuously by developing technologies such as virtual reality. Second, ROBOPRINT mediated mural service buyers and mural designers. This platform reduced buyers' workload, which necessitates requesting mural services to ROBOPRINT and searching for mural designers. For designers, this opened up new opportunities to participate in the mural business. The platform attracted both mural buyers and designers who were scattered before. Finally, ROBOPRINT seeks to expand the platform's scope to outside company. To share internally reused ROBOPRINT's technology with other companies, the company participated in Daegu city's 'New Technology Platform Industry'. Furthermore, ROBOPRINT is trying to share the service platform by leasing robots to other companies. This allows external agents to develop technologies and provide services by reusing resources from ROBOPRINT. This study contributes to existing theories by showing that SMEs continuously create and expand markets by building various platforms. Moreover, it provides useful implications for practitioners by describing the firm's specific platform-building strategy.