• Journal of IKEEE
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• v.25 no.3
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• pp.506-510
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• 2021

In this paper, we propose an LDO that improves the load regulation change due to the current detection structure. The proposed LDO regulator adds the proposed current detection circuit to the output stage. Thereby to improve the load regulation of the delta value coming in on the output has a voltage value of an improved load Regulation characteristics than conventional LDO regulator. Using the proposed current detection structure, it was possible to improve the output change according to the change of the load current by about 60%. The proposed circuit has been simulated and verified characteristics by using a Spectre, Virtuoso simulation of Cadence.

• Journal of IKEEE
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• v.24 no.4
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• pp.1162-1166
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• 2020

In this paper Low Drop-Out (LDO) regulator that improved load regulation characteristics due to the feedback detection structure. The proposed feedback sensing circuit is added between the output of the LDO's internal error amplifier and the input of the pass transistor to improve the regulation of the delta value coming into the output. It has a voltage value with improved load regulation characteristics than existing LDO regulator. The proposed LDO structure was analyzed in Samsung 0.13um process using Cadence's Virtuoso, Spectre simulator.

• Steel and Composite Structures
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• v.46 no.6
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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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• v.30 no.2
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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.

• Journal of IKEEE
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• v.25 no.1
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• pp.201-205
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• 2021

In this paper present Low Drop-Out (LDO) regulator that improved load transient characteristics due to the push-pull detection structure. The response characteristic of the voltage delta value is improved due to the proposed push-pull sensing circuit structure between the input terminal of the LDO regulator pass transistor and the output terminal of the internal error amplifier. Voltage value has improved load transient characteristics than conventional LDO regulator. Compared to the conventional LDO regulator, it has an improved response speed of approximately 244 ns at rising time and approximately 90 ns at falling time. The proposed circuit was simulated by the samsung 0.13um process using Cadence's Specter and Virtuoso simulator.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.3
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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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• v.5 no.3
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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.

• Journal of IKEEE
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• v.26 no.1
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• pp.124-128
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• 2022

Conventional LDO regulator external capacitors can reduce transient response characteristics such as overshoot and undershoot. However, the capacitorless LDO regulator proposed in this study applied body technology to the pass transistor to improve the transient response and provide excellent current drive capability. The operating conditions of the proposed LDO regulator are set to an input voltage that varies from 3.3V to 4.5V, a maximum load current of 200mA, and an output voltage of 3V. As a result of the measurement, it was found that when the load current was 100 mA, the voltage was 95 mV in the undershoot state and 105 mV in the overshoot state.

• International Journal of Control, Automation, and Systems
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• v.1 no.3
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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.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.7
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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.