• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.3
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• pp.181-187
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• 2020

In this paper, we proposed a register allocation algorithm and resource allocation algorithm in the high level synthesis process for the SoC design synthesis system of high performance vehicles We have analyzed to the operator characteristics and structure of datapath in the most important high-level synthesis. We also introduced the concept of virtual operator for the scheduling of multi-cycle operations. Thus, we demonstrated the complexity to implement a multi-cycle operation of the operator, regardless of the type of operation that can be applied for commonly use in the resources allocation algorithm. The algorithm assigns the functional operators so that the number of connecting signal lines which are repeatedly used between the operators would be minimum. This algorithm provides regional graphs with priority depending on connected structure when the registers are allocated. The registers with connecting structure are allocated to the maximum cluster which is generated by the minimum cluster partition algorithm. Also, it minimize the connecting structure by removing the duplicate inputs for the multiplexor in connecting structure and arranging the inputs for the multiplexor which is connected to the operators. In order to evaluate the scheduling performance of the described algorithm, we demonstrate the utility of the proposed algorithm by executing scheduling on the fifth digital wave filter, a standard bench mark model.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.8-14
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• 2020

Recently, there was an increasing demand for an integrated access control system which is capable of user recognition, door control, and facility operations control for smart buildings automation. The market available door lock access control solutions need to be improved from the current level security of door locks operations where security is compromised when a password or digital keys are exposed to the strangers. At present, the access control system solution providers focusing on developing an automatic access control system using (RF) based technologies like bluetooth, WiFi, etc. All the existing automatic door access control technologies required an additional hardware interface and always vulnerable security threads. This paper proposes the user identification and authentication solution for automatic door lock control operations using camera based visible light communication (VLC) technology. This proposed approach use the cameras installed in building facility, user smart devices and IoT open source controller based LED light sensors installed in buildings infrastructure. The building facility installed IoT LED light sensors transmit the authorized user and facility information color grid code and the smart device camera decode the user informations and verify with stored user information then indicate the authentication status to the user and send authentication acknowledgement to facility door lock integrated camera to control the door lock operations. The camera based VLC receiver uses the artificial intelligence (AI) methods to decode VLC data to improve the VLC performance. This paper implements the testbed model using IoT open-source based LED light sensor with CCTV camera and user smartphone devices. The experiment results are verified with custom made convolutional neural network (CNN) based AI techniques for VLC deciding method on smart devices and PC based CCTV monitoring solutions. The archived experiment results confirm that proposed door access control solution is effective and robust for automatic door access control.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.4
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• pp.436-444
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• 2018

Currently, the new substation automation uses the international standard IEC 61850 communication protocol. KEPCO is also constructing a new substation based on IEC 61850 from 2013 through the pilot application and research and development starting from 2007. However, there are few cases where existing substations(Transformer, T/L GIS, D/L GIS, etc.) have been used, and RTU based substations operating systems have been changed to SA substations based on IEC 61850. Therefore, the introduction of IEC 61850 in existing substation facilities has the advantage of enhancing the substantiality of the substation by reusing existing facilities, improving the interoperability with the latest substations introduced, and converting existing substations into systems suitable for unmanned operation. In this paper, we introduce a case of changing the existing RTU based substation operation system to digital substation using IEC 61850 field information processor, Ethernet switch and SA operation system. Also, IEC 61850 client authentication program and Wireshark, which is a packet analysis tool, verify IEC 61850 conformance and its feasibility.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.8
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• pp.171-180
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• 2020

Smart Factory consists of digital automation solutions throughout the production process, including design, development, manufacturing and distribution, and it is an intelligent factory that installs IoT in its internal facilities and machines to collect process data in real time and analyze them so that it can control itself. The smart factory's equipment works in a physical combination of numerous hardware, rather than a virtual character being driven by a single object, such as a game. In other words, for a specific common goal, multiple devices must perform individual actions simultaneously. By taking advantage of the smart factory, which can collect process data in real time, if reinforcement learning is used instead of general machine learning, behavior control can be performed without the required training data. However, in the real world, it is impossible to learn more than tens of millions of iterations due to physical wear and time. Thus, this paper uses simulators to develop grid sortation systems focusing on transport facilities, one of the complex environments in smart factory field, and design cooperative multi-agent-based reinforcement learning to demonstrate efficient behavior control.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.4
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• pp.389-394
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• 2023

In recent years, use of machine automation is rising in the industry. Ships also obtain machine condition information from sensor as digital information. However, on ships, crew members regularly surveil the engine room to check the condition of equipment and their information through analog gauges. This is a time-consuming and tedious process and poses safety risks to the crew while on surveillance. To address this, engine room surveillance using an autonomous mobile robot is being actively explored as a solution because it can reduce time, costs, and the safety risks for crew. Analog gauge reading using an autonomous mobile robot requires digitization for the robot to recognize the gauge value. In this study, image processing techniques were applied to achieve this. Analog gauge images were subjected to image preprocessing to remove noise and highlight their features. The center point, indicator point, minimum value and maximum value of the analog gauge were detected through image processing. Through the straight line connecting these points, the angle from the minimum value to the maximum value and the angle from the minimum value to indicator point were obtained. The obtained angle is digitized as the value currently indicated by the analog gauge through a formula. It was confirmed from the experiments that the digitization of the analog gauge using image processing was successful, indicating the equivalent current value shown by the gauge. When applied to surveillance robots, this algorithm can minimize safety risks and time and opportunity costs of crew members for engine room surveillance.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.230-238
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• 2023

Small and medium-sized food manufacturing enterprises are largely reliant on manual labor, from inputting raw materials to palletizing the final product. Recently, there has been a trend toward smartness and digitization through the implementation of robotics and sensor data technology. In this study, we examined the effectiveness of improvement through 3D simulation on two repetitive work processes within a food manufacturing company. These processes involve workers whose speed cannot match the capacity of the applied equipment. Two manual processes were selected: the weighing and packing process performed by workers after skewer assembly, and the manual batch process of counting randomly delivered frozen foods, packing (both internal and external), and palletizing. The production volume, utilization rate, and number of workers were chosen as verification indicators. As a result of the simulation for improving the 3D process, production increased by 13.5% and 56.8% compared to the existing process, respectively. This was particularly evident in the process of applying palletizing robots. In both processes, as the utilization rate and number of input workers decreased, robots could replace tasks with high worker fatigue, thereby reducing work overload. This study demonstrates the potential to visually compare the process flow improvement using 3D simulations and confirms the possibility of pre-validation for improvement.

• Journal of the Korean Society for information Management
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• v.41 no.1
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• pp.133-162
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• 2024

The rapid digitalization has highlighted the importance of identifying and managing online resources. Especially, the need for a systematic identification system for the efficient distribution and preservation of digital content is growing. This study aims to respond to these contemporary demands by investigating the current state of identification systems for online resources and exploring more systematic management and utilization methods through linking these systems with legal deposit. To achieve this, the study surveyed the identification systems and their issuance status for online resources and analyzed prior research related to these online resources. Based on the analysis, the proposed strategies for linking with legal deposit can be summarized into three categories: First, to prioritize and enhance the utilization of legal deposit, strategies are required to strengthen the mutual complementarity of deposit and use, to assign priorities to certain deposits, and to increase the usability of deposited materials. Second, as strategies based on international standard numbers for linking with legal deposit, it is necessary to integrate ISBN and UCI in the deposit process, to link international standard resource numbers with deposit, to interconnect metadata between international standard numbers and UCI, to integrate UCI and ICN, and to introduce automation technology for upgrading the deposit system. Third, to effectively implement the aforementioned strategies, policy support is essential. This includes enhancing the role of the Korean Bibliographic Standards Center, strengthening cooperation with publishers, compensating for deposited materials, and increasing awareness and institutional compensation for the legal deposit system.

• Land and Housing Review
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• v.15 no.4
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• pp.1-9
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• 2024

Visual scene understanding AI, a pivotal factor for digital transformation and robotic automation in construction, has primarily been researched under the hypothesis that the more training images, the higher the model performance. Alternatively, one can hypothesize that prioritizing activity-specific training images tailored to each construction phase would be more critical than merely enlarging the size of the dataset. This approach is particularly vital in dynamic construction environments where visual characteristics undergo significant changes across the construction phases, from earthmoving, foundation, and superstructure to finishing activities. In this background, we investigate the effects of a training image dataset's size and specificity on visual scene understanding AI in construction. We build an all-in-one, universal training image dataset as well as an activity-specific dataset, varying the number of training images. We then train vision-based worker detection models using each dataset and assess their performance in activity-specific, dynamic test environments. We analyze the optimal performance achieved in each test environment and how the model's performance varies depending on the dataset's size over the entire test phase. Our findings will help scientifically validate the dual hypotheses and lay a solid foundation for building and updating a training image dataset when developing a visual scene understanding AI model in dynamic construction sites.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.9
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• pp.317-322
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• 2022

The rapid development of information technology is also bringing about many changes in the medical environment. In particular, it is leading the rapid change of medical image information systems using big data and artificial intelligence (AI). The prescription delivery system (OCS), which consists of an electronic medical record (EMR) and a medical image storage and transmission system (PACS), has rapidly changed the medical environment from analog to digital. When combined with multiple solutions, PACS represents a new direction for advancement in security, interoperability, efficiency and automation. Among them, the combination with artificial intelligence (AI) using big data that can improve the quality of images is actively progressing. In particular, AI PACS, a system that can assist in reading medical images using deep learning technology, was developed in cooperation with universities and industries and is being used in hospitals. As such, in line with the rapid changes in the medical image information system in the medical environment, structural changes in the medical market and changes in medical policies to cope with them are also necessary. On the other hand, medical image information is based on a digital medical image transmission device (DICOM) format method, and is divided into a tomographic volume image, a volume image, and a cross-sectional image, a two-dimensional image, according to a generation method. In addition, recently, many medical institutions are rushing to introduce the next-generation integrated medical information system by promoting smart hospital services. The next-generation integrated medical information system is built as a solution that integrates EMR, electronic consent, big data, AI, precision medicine, and interworking with external institutions. It aims to realize research. Korea's medical image information system is at a world-class level thanks to advanced IT technology and government policies. In particular, the PACS solution is the only field exporting medical information technology to the world. In this study, along with the analysis of the medical image information system using big data, the current trend was grasped based on the historical background of the introduction of the medical image information system in Korea, and the future development direction was predicted. In the future, based on DICOM big data accumulated over 20 years, we plan to conduct research that can increase the image read rate by using AI and deep learning algorithms.