• Journal of Korea Multimedia Society
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• v.12 no.5
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• pp.745-756
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• 2009

An autonomous virtual character can conduct itself like a human after recognizing and interpreting the virtual environment. Artificial vision is mainly used in the recognition of the environment for a virtual character. The present artificial vision that has been developed takes all the information at once from everything that comes into view. However, this can reduce the efficiency and reality of the system by saving too much information at once, and it also causes problems because the speed slows down in the dynamic environment of the game. Therefore, to construct a vision system similar to that of humans, a visual observation system which saves only the required information is needed. For that reason, this research focuses on the descriptive artificial intelligence engine which detects the most important information visually recognized by the character in the virtual world and saves it into the memory by degrees. In addition, a visual system is constructed in accordance with an image transaction theory to make it sense and recognize human feelings. This system finds the attention area of moving objects quickly and effectively through the experiment of the virtual environment with three dynamic dimensions. Also the experiment enhanced processing speed more than 1.6 times.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.29-37
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• 2024

In this paper, we propose an efficient image processing system to detect and track the movement of specific objects such as patients. The proposed system extracts the outline area of an object from a binarized difference image by applying a thinning algorithm that enables more precise detection compared to previous algorithms and is advantageous for mixed-mode design. The binarization and thinning steps, which require a lot of computation, are designed based on RTL (Register Transfer Level) and replaced with optimized hardware blocks through logic circuit synthesis. The designed binarization and thinning block was synthesized into a logic circuit using the standard 180n CMOS library and its operation was verified through simulation. To compare software-based performance, performance analysis of binary and thinning operations was also performed by applying sample images with 640 × 360 resolution in a 32-bit FPGA embedded system environment. As a result of verification, it was confirmed that the mixed-mode design can improve the processing speed by 93.8% in the binary and thinning stages compared to the previous software-only processing speed. The proposed mixed-mode system for object recognition is expected to be able to efficiently monitor patient movements even in an edge computing environment where artificial intelligence networks are not applied.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.552-560
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• 2021

Currently, the domestic aquaculture industry is pursuing smartization, but it is still proceeding with human subjective judgment in many processes in the aquaculture stage. The prerequisite for the smart aquaculture industry is to effectively grasp the condition of fish in the farm. If real-time monitoring is possible by identifying the number of fish populations, size, pathways, and speed of movement, various forms of automation such as automatic feed supply and disease determination can be carried out. In this study, we proposed an algorithm to identify the state of fish in real time using underwater video data. The fish detection performance was compared and evaluated by applying the latest deep learning-based object detection models, and an algorithm was proposed to measure fish object identification, path tracking, and moving speed in continuous image frames in the video using the fish detection results. The proposed algorithm showed 92% object detection performance (based on F1-score), and it was confirmed that it effectively tracks a large number of fish objects in real time on the actual test video. It is expected that the algorithm proposed in this paper can be effectively used in various smart farming technologies such as automatic feed feeding and fish disease prediction in the future.

• The KIPS Transactions:PartA
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• v.13A no.7 s.104
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• pp.561-572
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• 2006

Performance of the mobile devices greatly depends on the efficient resource management because they are usually resource-restricted. In particular, the dynamic storage allocation algorithms very important part of the mobile device's operating system and OS-like software platform. The existing dynamic storage allocation algorithms did not consider application's execution style and the type, life-time, and characteristics of memory objects that the application uses. Those algorithms, as a result, could not manage memory efficiently Therefore, this Paper analyzes the mobile application's execution characteristics and proposes anew dynamic storage allocation algorithm which saves the memory space and improves mobile application's execution speed. The test result shows that the proposed algorithm works 6.5 times faster than the linked-list algorithm[11], 2.5 times faster better than the Doug. Lea's algorithm[12] and 10.5 times faster than the Brent algorithm[14].

• The Journal of the Korea Contents Association
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• v.20 no.10
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• pp.715-725
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• 2020

In this paper, we propose an optimization scheme for a k-Nearest Neighbor(k-NN) query, which finds k objects closest to the query in the high dimensional feature vectors. The k-NN query is converted and processed into a range query based on the range that is likely to contain k data. In this paper, we propose an optimization scheme using DNN model to derive an optimal range that can reduce processing cost and accelerate search speed. The entire system of the proposed scheme is composed of online and offline modules. In the online module, a query is actually processed when it is issued from a client. In the offline module, an optimal range is derived for the query by using the DNN model and is delivered to the online module. It is shown through various performance evaluations that the proposed scheme outperforms the existing schemes.

• The KIPS Transactions:PartA
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• v.19A no.2
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• pp.85-92
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• 2012

In this paper, a highly scalable real-time locating system which can measure location of fast moving targets is proposed. Within the system, the location service area is partitioned into grids with squares which is referred to as a macro-cell. Also, a macro-cell is further partitioned into $N{\times}N$ micro-cells. In a micro-cell, location reference nodes are placed on every vertex and an arbitration node is placed on the center. When a mobile node tries to measure its location, it should first communicate with the arbitration nodes for granting location measurement operation. Therefore, within a micro-cell, only one granted mobile node can calculate its location by a series of communication with location reference nodes. To evaluate performance of the proposed system, the system is modeled and simulated. The simulation result shows that the proposed system requires small communication time for location measurement operation and produces small location calculation error for fast moving targets.

• Journal of Internet Computing and Services
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• v.9 no.1
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• pp.43-54
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• 2008

In this paper, we implement an automatic entity information management system for smart refrigerator using RFID technology in which objects containing electronic tags are automatically identified using radio wave. Unlike current "smart" refrigerators, the system presented in this paper implements smart tag information acquisition mechanism and real-time information management system to provide various information on entities in refrigerators to local and remote users. As the first step, this paper analyzes the requirements for smart refrigerator system based on the RFID and suggests design considerations. Based on the analysis, we propose and implement an efficient tag location tracking method based on antenna transfer method and an intelligent tag information management system based on embedded database and web server. We also provide a wide range of experimental results on the number of tags identified at a time and the tag recognition ratio according to the RFID antenna transfer speed and the angle between tag reader and tags.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.10
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• pp.323-328
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• 2020

Recently, new approaches that significantly improve performance in object detection and recognition using deep learning technology have been proposed quickly. Of the various techniques for object detection, especially facial object detection (Faster R-CNN, R-CNN, YOLO, SSD, etc), SSD is superior in accuracy and speed to other techniques. At the same time, multiple object detection networks are also readily available. In this paper, among object detection networks, Mobilenet v2 network is used, models combined with SSDs are trained, and methods for detecting objects at a rate of four times or more than conventional performance are proposed using TensorRT engine, and the performance is verified through experiments. Facial object detector was created as an application to verify the performance of the proposed method, and its behavior and performance were tested in various situations.

• Journal of the Society of Disaster Information
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• v.19 no.2
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• pp.439-450
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• 2023

Purpose: When conducting fire inspections in Korea, there are objects that violate the fire protection regulations that require a straight line distance of more than 5m between the air inlet and the discharge section if the floor area is less than 400m2, and this paper analyzes the reasons and conducts a model experimental study to support the need for related fire protection regulations. Method: Domestic firefighting objects were investigated and confirmed, domestic and foreign papers, policies, and laws and regulations were examined, and spaces with a straight line distance of less than 5m and more than 5m between the air inlet and discharge section were selected and analyzed through model experiments in a living room of less than 400m² . Result: When examining the domestic fire protection regulations (NFPCNational Fire Perpormance Code), the separation distance between the air inlet and the outlet is more than 5m when the floor area is less than 400m² , but as a result of the actual investigation, it was confirmed that there are firefighting objects that cannot keep the separation distance. In addition, when a paper review of overseas fire protection regulations for a straight line distance of more than 5m showed that there was no regulation on the straight line distance between the air inlet and the discharge section, the model experiment showed that the discharge speed was better when the straight line distance between the air inlet and the discharge section was more than 5m than when it was less than 5m. Conclusions: In this study, when examining overseas fire laws and regulations by comparing the performance of the fire protection ratio for the straight line distance between the air inlet and the exhaust section, there is no mandatory regulation for the straight line distance, but the domestic fire protection regulations (NFPCNational Fire Perpormance Code) require more than 5m. It is hoped that this will be reflected in the design stage in the future, and a foundation will be laid to reduce the responsibility and burden of fire superintendents.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.99-107
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• 2024

Along with economic growth and industrial development, there is an increasing demand for various electronic components and device production of semiconductor, SMT component, and electrical battery products. However, these products may contain foreign substances coming from manufacturing process such as iron, aluminum, plastic and so on, which could lead to serious problems or malfunctioning of the product, and fire on the electric vehicle. To solve these problems, it is necessary to determine whether there are foreign materials inside the product, and may tests have been done by means of non-destructive testing methodology such as ultrasound ot X-ray. Nevertheless, there are technical challenges and limitation in acquiring X-ray images and determining the presence of foreign materials. In particular Small-sized or low-density foreign materials may not be visible even when X-ray equipment is used, and noise can also make it difficult to detect foreign objects. Moreover, in order to meet the manufacturing speed requirement, the x-ray acquisition time should be reduced, which can result in the very low signal- to-noise ratio(SNR) lowering the foreign material detection accuracy. Therefore, in this paper, we propose a five-step approach to overcome the limitations of low resolution, which make it challenging to detect foreign substances. Firstly, global contrast of X-ray images are increased through histogram stretching methodology. Second, to strengthen the high frequency signal and local contrast, we applied local contrast enhancement technique. Third, to improve the edge clearness, Unsharp masking is applied to enhance edges, making objects more visible. Forth, the super-resolution method of the Residual Dense Block (RDB) is used for noise reduction and image enhancement. Last, the Yolov5 algorithm is employed to train and detect foreign objects after learning. Using the proposed method in this study, experimental results show an improvement of more than 10% in performance metrics such as precision compared to low-density images.