• 전기전자학회논문지
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• 제25권3호
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• pp.506-510
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• 2021

본 논문에서는 current detection 구조로 인하여 load regulation의 변화를 향상시킨 LDO를 제안하였다. 제안된 LDO 레귤레이터는 출력단에 제안된 current detection 회로를 추가하였다. 그로인하여 출력에 부하전류에 따른 전압 값의 regulation을 향상시켜 기존 LDO 레귤레이터보다 load Regulation의 변화량을 향상시켰다. 제안한 current detection 구조를 사용하여 부하전류의 변화에 따른 출력 변화를 약 60 % 가량 향상시킬 수 있었다. Cadence의 Virtuoso, Spectre 시뮬레이션을 사용하여 특성을 시뮬레이션 및 검증하였다.

• 전기전자학회논문지
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• 제24권4호
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• pp.1162-1166
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• 2020

본 논문에서는 피드백 감지 회로 구조로 인하여 향상된 load regulation 특성을 개선시킨 LDO를 제안하였다. LDO 레귤레이터 내부 오차증폭기의 출력단과 패스 트랜지스터의 입력단 사이에 제안된 feedback 감지 회로를 추가하여 출력에 들어오는 델타 값의 regulation을 개선시켜 기존의 LDO 레귤레이터보다 개선된 load Regulation 특성의 전압 값을 갖는다. 제안된 회로는 Cadence의 Spectre, Virtuoso 시뮬레이션을 이용하여 삼성 0.13um 공정에서 특성을 시뮬레이션 하였다.

• Steel and Composite Structures
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• 제46권6호
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• pp.731-744
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• 2023

Vehicle load information is an important role in operating and ensuring the structural health of cable-stayed bridges. In this regard, an efficient and economic method is proposed for vehicle load detection based on the observed cable tension and vehicle position using a graph neural network (GNN). Datasets are first generated using the practical advanced analysis program (PAAP), a robust program for modeling and considering both geometric and material nonlinearities of bridge structures subjected to vehicle load with low computational costs. With the superiority of GNN, the proposed model is demonstrated to precisely capture complex nonlinear correlations between the input features and vehicle load in the output. Four popular machine learning methods including artificial neural network (ANN), decision tree (DT), random forest (RF), and support vector machines (SVM) are refereed in a comparison. A case study of a cable-stayed bridge with the typical truck is considered to evaluate the model's performance. The results demonstrate that the GNN-based model provides high accuracy and efficiency in prediction with satisfactory correlation coefficients, efficient determination values, and very small errors; and is a novel approach for vehicle load detection with the input data of the existing monitoring system.

• Smart Structures and Systems
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• 제30권2호
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• pp.145-157
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• 2022

The design of safe and economical structures depends on the reliable live load from load survey. Live load surveys are traditionally conducted by randomly selecting rooms and weighing each item on-site, a method that has problems of low efficiency, high cost, and long cycle time. This paper proposes a deep learning-based method combined with Internet big data to perform live load surveys. The proposed survey method utilizes multi-source heterogeneous data, such as images, voice, and product identification, to obtain the live load without weighing each item through object detection, web crawler, and speech recognition. The indoor objects and face detection models are first developed based on fine-tuning the YOLOv3 algorithm to detect target objects and obtain the number of people in a room, respectively. Each detection model is evaluated using the independent testing set. Then web crawler frameworks with keyword and image retrieval are established to extract the weight information of detected objects from Internet big data. The live load in a room is derived by combining the weight and number of items and people. To verify the feasibility of the proposed survey method, a live load survey is carried out for a meeting room. The results show that, compared with the traditional method of sampling and weighing, the proposed method could perform efficient and convenient live load surveys and represents a new load research paradigm.

• 전기전자학회논문지
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• 제25권1호
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• pp.201-205
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• 2021

본 논문에서는 push-pull 감지 회로 구조로 인해 load transient 특성을 개선시킨 LDO를 제안하였다. LDO 레귤레이터 패스 트랜지스터의 입력단과 내부 오차증폭기의 출력단 사이에 제안된 push-pull 감지 회로 구조로 인한 전압 델타 값의 응답 특성을 개선시켜 종래의 LDO 레귤레이터보다 load transient 특성에서 우수한 효과를 가진다. 기존의 LDO 레귤레이터보다 rising time에서는 약 244 ns, falling time에서는 약 90 ns 만큼의 향상된 응답속도를 가진다. 제안된 회로는 Cadence사의 Spectre, Virtuoso 시뮬레이션 tool을 사용하여 samsung 0.13um 공정으로 특성 및 결과를 시뮬레이션 하였다.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권3호
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• pp.161-165
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• 2004

In this paper, we have designed the ground faults detection and interrupting controller at normal condition of AC 120v to 240v rating voltage. Ground faults in electrical network have the characteristics of low current, 60㎐ frequency to 2㎑frequency. The load condition are no load and 20A load. The trip level of the controller is 6㎃ with ground faults. The Controller algorithm is implemented using pic16c71 microprocessor.

• IEIE Transactions on Smart Processing and Computing
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• 제5권3호
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• pp.169-177
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• 2016

To realize high-speed intrusion detection by accommodating many regular expression (regex)-based signatures and growing network link capacities, we propose the Service TimE-Aware Load-balancing (STEAL) algorithm. This work is motivated from the observation that utilization of a many-core network intrusion detection system (NIDS) is influenced by unfair computational distribution among many-core NIDS nodes. To avoid such unfair computational distribution, STEAL is designed to dynamically distribute a large volume of traffic among many-core NIDS nodes based on packet service time, which is represented by the deep packet time in many-core NIDS nodes. From experiments, we show that compared to the commonly used load-balancing algorithm based on arrival rate, STEAL increases the number of received packets (i.e., decreases the number of dropped packets) in many-core NIDS. Specifically, by integrating an open source NIDS (i.e. Bro) with STEAL, we show that even under attack-dominant traffic and with many signatures, STEAL can rapidly improve the performance of many-core NIDS to realize high-speed intrusion detection.

• 전기전자학회논문지
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• 제26권1호
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• pp.124-128
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• 2022

기존 LDO 레귤레이터 외부 커패시터는 오버슈트 및 언더슈트와 같은 과도 응답 특성을 줄일 수 있다. 그러나 본 연구에서 제안한 Capless LDO 레귤레이터는 과도 응답을 개선하고 우수한 전류 구동 능력을 제공하기 위해 패스 트랜지스터에 바디 기술을 적용하였다. 제안하는 LDO 레귤레이터의 동작 조건은 3.3V ~ 4.5V 범위의 입력 전압, 최대 부하 전류 200mA, 출력 전압 3V로 설정하였다. 측정 결과, 부하 전류가 100mA일 때 전압은 언더슈트 상태에서 95 mV, 오버슈트 상태에서 105 mV임을 확인 할 수 있었다.

• International Journal of Control, Automation, and Systems
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• 제1권3호
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• pp.368-375
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• 2003

Data fusion approach is investigated to avoid suction in the left ventricular assist system (LVAS) using a nonpulsatile pump. LVAS requires careful control of pump speed to support the heart while preventing suction in the left ventricle and providing proper cardiac output at adequate perfusion pressure to the body. Since the implanted sensors are usually unreliable for long-term use, a sensorless approach is adopted to detect suction. The pump model is developed to provide the load coefficient as a necessary signal to the data fusion system without the implanted sensors. The load coefficient of the pump mimics the pulsatility property of the actual pump flow and provides more comparable information than the pump flow after suction occurs. Four signals are generated from the load coefficient as inputs to the data fusion system for suction detection and a neural fuzzy method is implemented to construct the data fusion system. The data fusion approach has a good ability to classify suction status and it can also be used to design a controller for LVAS.

• 조명전기설비학회논문지
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• 제28권7호
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• pp.81-90
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• 2014

This paper proposes new arc algorithm to detect series quasi-arc. This algorithm analyzes odd and even harmonics until 9th using discrete fourier transform (DFT) and detect series arc comparing RMS values of load current. Resistors, lights, dimmer and vacuum cleaner which can be distinguished linearity load and quasi arc load are adopted to perform experiments. This algorithm is confirmed to emulate arc detecting with measuring current data.