• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.501-512
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• 2018

Radio frequency identification (RFID) sensor tag provides several advantages including battery-less operation and low cost, which are suitable for long-term monitoring. This paper presents a self-powered RFID temperature sensor tag for online temperature monitoring in substation. The proposed sensor tag is used to measure and process the temperature of high voltage equipments in substation, and then wireless deliver the data. The proposed temperature sensor employs a novel phased-locked loop (PLL)-based architecture and can convert the temperature sensor in frequency domain without a reference clock, which can significantly improve the temperature accuracy. A two-stage rectifier adopts a series of auxiliary floating rectifier to boost its gate voltage for higher power conversion efficiency. The sensor tag chip was fabricated in TSMC $0.18{\mu}m$ 1P6M CMOS process. The measurement results show that the proposed temperature sensor tag achieve a resolution of $0.15^{\circ}C$/LSB and a temperature error of $-0.6/0.7^{\circ}C$ within the range from $-30^{\circ}C$ to $70^{\circ}C$. The proposed sensor tag achieves maximum communication distance of 11.8 m.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.9
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• pp.1158-1165
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• 2021

This paper proposes a CNN accelerator which is optimized Pooling layer operation incorporated in Multiplication And Accumulation(MAC) to reduce the memory size. For optimizing memory and data path circuit, the quantized 8bit integer weights are used instead of 32bit floating-point weights for pre-training of MNIST data set. To reduce chip area, the proposed CNN model is reduced by a convolutional layer, a 4*4 Max Pooling, and two fully connected layers. And all the operations use specific MAC with approximation adders and multipliers. 94% of internal memory size reduction is achieved by simultaneously performing the convolution and the pooling operation in the proposed architecture. The proposed accelerator chip is designed by using TSMC65nmGP CMOS process. That has about half size of our previous paper, 0.8*0.9 = 0.72mm². The presented CNN accelerator chip achieves 94% accuracy and 77us inference time per an MNIST image.

• Journal of IKEEE
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• v.25 no.3
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• pp.479-484
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• 2021

This paper proposed and designed a structure to achieve high performance with a small number of cores in GPGPU with SIMT structure. GPGPU for application to mobile devices requires a structure to increase performance compared to power consumption. In order to reduce power consumption, the number of cores decreased, but to improve performance, the size of the warp scheduler for managing threads was set to 4, which was greatly reduced than 32 of general GPGPU. Reducing warp size can reduce the number of idle cycles in pipelines and efficiently apply memory latency to reduce miss penalty when accessing cache memory. The designed GPGPU measured computational performance using a test program that includes floating point operations and measured power consumption through a 28nm CMOS process to obtain 104.5GFlops/Watt as a performance per power. The results of this paper showed about four times better performance per power compared to Tegra K1 of Nvidia

• International Journal of Naval Architecture and Ocean Engineering
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• v.11 no.1
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• pp.22-32
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• 2019

On offshore platforms, oil and gas leaks are apt to be the initial events of major accidents that may result in significant loss of life and property damage. To prevent accidents induced by leakage, it is vital to perform a case-specific and accurate risk assessment. This paper presents an integrated method of Ddynamic Qquantitative Rrisk Aassessment (DQRA)-using the Decision Making Trial and Evaluation Laboratory (DEMATEL)-Bayesian Network (BN)-for evaluation of the system vulnerabilities and prediction of the occurrence probabilities of accidents induced by leakage. In the method, three-level indicators are established to identify factors, events, and subsystems that may lead to leakage, fire, and explosion. The critical indicators that directly influence the evolution of risk are identified using DEMATEL. Then, a sequential model is developed to describe the escalation of initial events using an Event Tree (ET), which is converted into a BN to calculate the posterior probabilities of indicators. Using the newly introduced accident precursor data, the failure probabilities of safety barriers and basic factors, and the occurrence probabilities of different consequences can be updated using the BN. The proposed method overcomes the limitations of traditional methods that cannot effectively utilize the operational data of platforms. This work shows trends of accident risks over time and provides useful information for risk control of floating marine platforms.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.208-222
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• 2021

Managing with the presence of sea ice is the primary challenge in the operation of floating platforms in the Arctic region. It is widely accepted that offshore structures operating in Arctic conditions need station-keeping methods as well as ice management by icebreakers. Dynamic Positioning (DP) is one of the station-keeping methods that can provide mobility and flexibility in marine operations. The presence of sea ice generates complex external forces and moments acting on the vessel, which need to be counteracted by the DP system. In this paper, an icevaning control algorithm is proposed that enables Arctic offshore vessels to perform DP operations. The proposed icevaning control enables each vessel to be oriented toward the direction of the mean environmental force induced by ice drifting so as to improve the operational safety and reduce the overall thruster power consumption by having minimum external disturbances naturally. A mathematical model of an Arctic offshore vessel is summarized for the development of the new icevaning control algorithm. To determine the icevaning action of the Arctic offshore vessel without any measurements and estimation of ice conditions including ice drift, task and null space are defined in the vessel model, and the control law is formulated in the task space. A backstepping technique is utilized to handle the nonlinearity of the Arctic offshore vessel's dynamic model, and the Lyapunov stability theory is applied to guarantee the stability of the proposed icevaning control algorithm. Experiments are conducted in the ice tank of the Korea Research Institute of Ships and Ocean Engineering to demonstrate the feasibility of the proposed approach.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.1
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• pp.15-20
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• 2022

An improvement of computational resources with a large scale cluster service is available to the individual person, which has been limited to the original industry and research institute. Therefore, the application of powerful computational resources to the engineering design has been increased fast. In naval and marine industry, the application of Computational Fluid Dynamics, which requires a huge computational effort, to a design of ship and offshore structure has been increased. Floating bodies such as the ship or offshore structure is exposed to ocean waves, current and wind in the ocean, therefore the precise modelling of those environmental disturbances is important in Computational Fluid Dynamics. Especially, ocean waves has to be nonlinear rather than the linear model based on the superposition due to a nonlinear characteristics of Computational Fluid Dynamics. In the present study, a fast reconstruction technique is suggested and it is validated from a series of simulations by using the Computational Fluid Dynamics.

• Journal of the Korean Society of Safety
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• v.38 no.2
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• pp.87-95
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• 2023

On October 29, 2022, a very large number of people gathered in Itaewondong, Yongsan-gu, Seoul, Korea for a Halloween festival, and as crowds pushed through narrow alleys, 159 deaths and 195 injuries occurred, making it the largest crushing incident in Korea. There have been a number of stampede deaths where crowds gathered at large-scale festivals, event venues, and stadiums, both at home and abroad. When the density increases, the physical contact between bodies becomes very strong, and crowd turbulence occurs when the force of the crowd is suddenly added from one body to another; thus, the force is amplified and causes the crowd to behave like a mass of fluid. When crowd turbulence occurs, people cannot control themselves and are pushed into he crowd. To prevent a stampede accident, investigation and management of areas expected to be crowded and congested must be systematically conducted, and related ministries and local governments are planning to establish a crowd management system to prepare safety management measures to prevent accidents involving multiple crowds. In this study, based on national data, a continuous digital topographic map is modeled in 3D to analyze the risk of crowding and present a plan for selecting high-risk walking routes. Areas with a high risk of crowding are selected in advance based on various data (numerical data, floating population, and regional data) in a realistic and feasible way, and the analysis is based on the visible results from 3D modeling of the risk area. The study demonstrates that it is possible to prepare measures to prevent cluster accidents that can reflect the characteristics of the region.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.161-164
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• 2022

부유식 라이다는 해상풍력단지 조성시 필수적으로 수행하고 있는 풍황관측 업무에 새로운 패러다임을 제공하고 있는 시스템으로, 전통적으로 풍황관측을 수행하고 있던 해상기상관측탑을 대체하여 사업 초기의 대규모 공사를 획기적으로 축소하여 시간과 비용을 절약하고, 환경적 영향을 최소화 하며, 지역사회의 반발 요소까지 줄일 수 있어 해당 업계의 표준으로 자리잡고 있는 중이다. 다만 부표식의 동요에 따른 외란적 요소가 관측자료의 신뢰성에 영향을 미치는 만큼 안정적인 플랫폼의 설계 및 검증이 매우 중요한 상황이며, 국내에서는 해당기술에 대한 늦은 진입으로 인해 다수의 외산장비 제조사들이 국내시장까지 선점하고 있는 상황이다. 한국의 서해안은 천해 환경으로 조석차가 매우 커 지역에 따라 강한 조류가 반복적으로 나타나며, 계절별로 상이한 강한 에너지의 파랑이 형성되는 등 플랫폼에 안정도에 많은 영향을 미치는 바다 환경을 갖고 있다. 본 논문에서는 이러한 복잡한 환경적 특성을 갖고 있는 우리나라의 해역에 라이다 운영에 적합한 부표식에 대한 연구를 수행하며, 우선적으로 적용하였던 선박형 부표식의 최적화 설계 및 검증 사례를 소개하고, 향후 다양한 플랫폼 개발에 토대가 되는 중요 개념을 도출하고자 한다.

• The Transactions of the Korea Information Processing Society
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• v.13 no.3
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• pp.140-147
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• 2024

In the Republic of Korea, people with hearing impairments are the second-largest demographic within the registered disability community, following those with physical disabilities. Despite this demographic significance, research on sign language translation technology is limited due to several reasons including the limited market size and the lack of adequately annotated datasets. Despite the difficulties, a few researchers continue to improve the performacne of sign language translation technologies by employing the recent advance of deep learning, for example, the transformer architecture, as the transformer-based models have demonstrated noteworthy performance in tasks such as action recognition and video classification. This study focuses on enhancing the recognition performance of sign language translation by combining transformers with 3D-CNN. Through experimental evaluations using the PHOENIX-Wether-2014T dataset [1], we show that the proposed model exhibits comparable performance to existing models in terms of Floating Point Operations Per Second (FLOPs).

• Journal of the Korea Society of Computer and Information
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• v.29 no.8
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• pp.85-92
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• 2024

In this paper presents a comparative analysis of the performance between the latest object detection model, YOLOv10, and its previous versions. YOLOv10 introduces NMS-Free training, an enhanced model architecture, and an efficiency-centric design, resulting in outstanding performance. Experimental results using the COCO dataset demonstrate that YOLOv10-N maintains high accuracy of 39.5% and low latency of 1.84ms, despite having only 2.3M parameters and 6.7G floating-point operations (FLOPs). The key performance metrics used include the number of model parameters, FLOPs, average precision (AP), and latency. The analysis confirms the effectiveness of YOLOv10 as a real-time object detection model across various applications. Future research directions include testing on diverse datasets, further model optimization, and expanding application scenarios. These efforts aim to further enhance YOLOv10's versatility and efficiency.