• KIEAE Journal
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• v.3 no.2
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• pp.25-32
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• 2003

This study deals with the investigation of recognition structure on architectural environment and reconstruction of it by artificial intelligence. To test the possibility of the reconstruction, recognition structure on architectural environment is analysed and each steps of the structure are matched with computational methods. Edge Detection and Neural Network were selected as matching methods to each steps of recognition process. Visual perception system established by selected methods is trained and tested, and the result of the system is compared with that of experiment of human. Assuming that the artificial system resembles the process of human recognition on architectural environment, does the system give similar response of human? The result shows that it is possible to establish artificial visual perception system giving similar response with that of human when it models after the recognition structure and process of human.

• Journal of Sensor Science and Technology
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• v.31 no.1
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• pp.16-23
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• 2022

Cardiac disease is the most common cause of death worldwide. Therefore, detection and classification of electrocardiogram (ECG) signals are crucial to extend life expectancy. In this study, we aimed to implement an artificial intelligence signal recognition system in field programmable gate array (FPGA), which can recognize patterns of bio-signals such as ECG in edge devices that require batteries. Despite the increment in classification accuracy, deep learning models require exorbitant computational resources and power, which makes the mapping of deep neural networks slow and implementation on wearable devices challenging. To overcome these limitations, spiking neural networks (SNNs) have been applied. SNNs are biologically inspired, event-driven neural networks that compute and transfer information using discrete spikes, which require fewer operations and less complex hardware resources. Thus, they are more energy-efficient compared to other artificial neural networks algorithms.

• Journal of Information Processing Systems
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• v.18 no.3
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• pp.311-318
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• 2022

Tobacco sales enterprises often need to summarize and verify the daily sales bills, which may consume substantial manpower, and manual verification is prone to occasional errors. The use of artificial intelligence technology to realize the automatic identification and verification of such bills offers important practical significance. This study presents a novel multi-branch residual network for tobacco sales bills to improve the efficiency and accuracy of tobacco sales. First, geometric correction and edge alignment were performed on the input sales bill image. Second, the multi-branch residual network recognition model is established and trained using the preprocessed data. The comparative experimental results demonstrated that the correct recognition rate of the proposed method reached 98.84% on the China Tobacco Bill Image dataset, which is superior to that of most existing recognition methods.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.5
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• pp.777-782
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• 2017

The Signal Intelligence (SIGINT) system based on the adaptive beamformer, comprised of the AOA estimator followed by the interference canceller, is a cutting edge technology for collecting various signal information utilizing all sorts of devices such as the radar and satellite. In this paper, we present the efficient adaptive SIGINT structure consisted of an AOA estimator and an adaptive beamformer. For estimating AOA information of various signals, we employ the Multiple Signal Classification (MUSIC) algorithm and for efficiently suppressing high-power interference signals, we employ the Minimum Variance Distortionless Response (MVDR) algorithm. Also, we provide computer simulation examples to verify the performance of the presented adaptive beamformer structure.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.370-378
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• 2024

Recently, Artificial Intelligence (AI) has been emerging as a technology that has transformed and revolutionized various industries around the world. In recent years, businesses in Vietnam have also started to embrace AI applications to enhance their operations and gain a competitive edge in the market. As AI technologies continue to evolve rapidly, their impact on Vietnamese businesses is becoming increasingly profound. As artificial intelligence continues to progress across various fields, the need to democratize AI technology becomes increasingly clear. In a rapidly growing market like Vietnam, leveraging AI offers significant opportunities for businesses to improve operational efficiency, customer engagement, and overall competitiveness. However, significant barriers to AI adoption in Vietnam are the scarcity of skilled developers and the high cost of implementing traditional AI. No-code/low-code platforms offer an innovative solution that can accelerate AI adoption by making these technologies accessible to a wider audience. This article analyzes and understands the benefits of no-code/low-code solutions and proposes a roadmap for implementing no-code/low-code solutions in promoting AI applications in Vietnamese businesses.

• Smart Structures and Systems
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• v.33 no.5
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• pp.333-347
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• 2024

There has been an increasing interest in the construction of smart buildings that can actively monitor and react to their surroundings. The capacity of these intelligent structures to precisely predict and respond to deflection is a crucial feature that guarantees both their structural soundness and efficiency. Conventional techniques for determining deflection often depend on intricate mathematical models and computational simulations, which may be time- and resource-consuming. Artificial intelligence (AI) algorithms have become a potent tool for anticipating and controlling deflection in intelligent structures in response to these difficulties. The term "deflection-aware smart structures" in this sense refers to constructions that have AI algorithms installed that continually monitor and analyses deflection data in order to proactively detect any problems and take appropriate action. These structures anticipate deflection across a range of operating circumstances and environmental factors by using cutting-edge AI approaches including deep learning, reinforcement learning, and neural networks. AI systems are able to predict real-time deflection with high accuracy by using data from embedded sensors and actuators. This capability enables the systems to identify intricate patterns and linkages. Intelligent buildings have the potential to self-correct in order to reduce deflection and maximize performance. In conclusion, the development of deflection-aware smart structures is a major stride forward for structural engineering and has enormous potential to enhance the performance, safety, and dependability of designed systems in a variety of industries.

• Journal of Korea Multimedia Society
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• v.23 no.3
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• pp.476-482
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• 2020

In this paper, we propose a block-based modularity architecture design method for distributed machine learning. The proposed architecture is a block-type module structure with various machine learning algorithms. It allows free expansion between block-type modules and allows multiple machine learning algorithms to be organically interlocked according to the situation. The architecture enables open data communication using the metadata query protocol. Also, the architecture makes it easy to implement an application service combining various edge computing devices by designing a communication method suitable for surrounding applications. To confirm the interlocking between the proposed block-type modules, we implemented a hardware-based modularity application system.

• Transactions of the Korean Society of Mechanical Engineers
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• v.12 no.1
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• pp.173-182
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• 1988

A program for drawing of penetrating lines was developed in personal computer. PROLOG, a language of Artificial Intelligence, was used and a data structure using relational data base was designed. An algorithm for finding the penetrating lines in the real space was developed. The program can be applied at any types of penetrating problems like curve-surface, surface-surface, curve-object, surface-object, object-object, etc. In developing the program, the following results were obtained. (1) Relational data base built in PROLOG and the function of backtracking are helpful in Computer Graphics. (2) In spite of increasing the number of edges, assigning direction to the edges makes it possible to represent the polygon meshes as the non ordered sets of directional half edges. (3) Topologicaly the penetrating lines of a polygon can be represented as the edge-pairs in the edge list of the polygon,

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.272-275
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• 2022

The federated learning can be utilized in conjunction with the blockchain technology to provide good privacy protection and reward distribution mechanism in the field of intelligent IOT in edge computing scenarios. Nonetheless, the synchronous federated learning ignores the waiting delay due to the heterogeneity of edge devices (different computing power, communication bandwidth, and dataset size). Moreover, the potential of smart contracts was not fully explored to do some flexible design. This paper investigates the fusion application based on the FLchain, which is the combination of asynchronous federated learning and blockchain, discusses the communication optimization, and explores the feasible design of smart contract to solve some problems.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.166-171
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• 2023

With significant advancements in the field of artificial intelligence, many novel algorithms and technologies have emerged. Currently, AI painting can generate high-quality images based on textual descriptions. However, it is often challenging to control details when generating images, even with complex textual inputs. Therefore, there is a need to implement additional control mechanisms beyond textual descriptions. Based on ControlNet, this passage describes a combined utilization of various local controls (such as edge maps and depth maps) and global control within a single model. It provides a comprehensive exposition of the fundamental concepts of ControlNet, elucidating its theoretical foundation and relevant technological features. Furthermore, combining methods and applications, understanding the technical characteristics involves analyzing distinct advantages and image differences. This further explores insights into the development of image generation patterns.