• Structural Engineering and Mechanics
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• v.86 no.5
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• pp.673-686
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• 2023

Determining the properties of pile from cone penetration test (CPT) is costly, and need several in-situ tests. At the present study, two novel hybrid learning models, namely PSO-RF and HHO-RF, which are an amalgamation of random forest (RF) with particle swarm optimization (PSO) and Harris hawks optimization (HHO) were developed and applied to predict the pile set-up parameter "A" from CPT for the design aim of the projects. To forecast the "A," CPT data along were collected from different sites in Louisiana, where the selected variables as input were plasticity index (PI), undrained shear strength (S_u), and over consolidation ratio (OCR). Results show that both PSO-RF and HHO-RF models have acceptable performance in predicting the set-up parameter "A," with R² larger than 0.9094, representing the admissible correlation between observed and predicted values. HHO-RF has better proficiency than the PSO-RF model, with R² and RMSE equal to 0.9328 and 0.0292 for the training phase and 0.9729 and 0.024 for testing data, respectively. Moreover, PI and OBJ indices are considered, in which the HHO-RF model has lower results which leads to outperforming this hybrid algorithm with respect to PSO-RF for predicting the pile set-up parameter "A," consequently being specified as the proposed model. Therefore, the results demonstrate the ability of the HHO algorithm in determining the optimal value of RF hyperparameters than PSO.

• Journal of the Korea Society of Computer and Information
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• v.24 no.12
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• pp.35-41
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• 2019

This paper propose design of remotely controllable hologram based interactive signage. General idea is organization of work of hologram signage through using Raspberry Pi hardware platform and Intel realsense r200 for interaction opportunity. Remote content management is based on Screenly software solution. Open CV based solutions are used for content controlling on the spectators side. Represented work describe of using of the 3D content rendering algorithm based on 3D gaming technology Unity 5. An experimental model was carried out with the purpose of IoS designing, to 3D data visualization and to introduce a new method for visualizing and displaying 3D data on a hologram pyramid signage. Description of working model of hologram signage is given in this paper.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.12
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• pp.2885-2890
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• 2014

Localization of a sensor network node using machine learning has been recently studied. It is easy for Support vector machines algorithm to implement in high level language enabling parallelism. Raspberrypi is a linux system which can be used as a sensor node. Pi can be used to construct IP based Hadoop clusters. In this paper, we realized Support vector machine using python language and built a sensor network cluster with 5 Pi's. We also established a Hadoop software framework to employ MapReduce mechanism. In our experiment, we implemented the test sensor network with a variety of parameters and examined based on proficiency, resource evaluation, and processing time. The experimentation showed that with more execution power and memory volume, Pi could be appropriate for a member node of the cluster, accomplishing precise classification for sensor localization using machine learning.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.4
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• pp.183-189
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• 2021

In recent years, active research has been devoted toward developing a monitoring system using ambient vibration data in order to quantitatively determine the deterioration occurring in a structure over a long period of time. This study developed a low-cost edge computing system that detects the abnormalities in structures by utilizing the dynamic characteristics acquired from the structure over the long term for ensemble learning. The system hardware consists of the Raspberry Pi, an accelerometer, an inclinometer, a GPS RTK module, and a LoRa communication module. The structural abnormality detection afforded by the ensemble learning using dynamic characteristics is verified using a laboratory-scale structure model vibration experiment. A real-time distributed processing algorithm with dynamic feature extraction based on the experiment is installed on the Raspberry Pi. Based on the stable operation of installed systems at the Community Service Center, Pohang-si, Korea, the validity of the developed system was verified on-site.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1935-1941
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• 2021

In order to develop an algorithm using deep learning, which has been recently applied to various fields, it is necessary to have rich, high-quality learning data. In this paper, we propose an acquisition system for biological signals that simultaneously collects bio-signal data such as optical videos, thermal videos, and voices, which are mainly used in developing deep learning algorithms and useful in derivation of information, and transmit them to the server. To increase the portability of the collector, it was made based on Raspberry Pi, and the collected data is transmitted to the server through the wireless Internet. To enable simultaneous data collection from multiple collectors, an ID for login was assigned to each subject, and this was reflected in the database to facilitate data management. By presenting an example of biological data collection for fatigue measurement, we prove the application of the proposed acquisition system.

• Journal of the Korean Institute of Telematics and Electronics T
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• v.36T no.1
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• pp.41-50
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• 1999

The brushless DC motor is widely being used in unmanned factories for its easy maintenance and characteristics of controllability. In this paper, we designed a speed control servo system of a sinusoidal type bmshless DC motor which has high efficiency and usefulness in the industrial fields. This servo system is realized by a controller which is required for driving motors and a new auto-tuning PI control algorithm. The DSP(Digita1 Signal Processor) is adopted as a main controller and a sensor signal processor owing to its fast computational capability and suitable architecture. Also, the hardware PWnl(Pulse Width Modulation) current controller is implemented to pursue a speed command exactly. By experimental results, it is verified that the speed response is pursued fast after command value and the steady-state response is well converged for command value variation without overshoots.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1535-1547
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• 2016

This paper proposes various design procedures for computing Power System Ancillary Service Requirement Assessment Indices (PSASRAI) for a Two-Area Thermal Reheat Interconnected Power System (TATRIPS) in a restructured environment. In an interconnected power system, a sudden load perturbation in any area causes the deviation of frequencies of all the areas and also in the tie-line powers. This has to be corrected to ensure the generation and distribution of electric power companies to ensure good quality. A simple Proportional and Integral (PI) controllers have wide usages in controlling the Load Frequency Control (LFC) problems. So the design of the PI controller gains for the restructured power system are obtained using Bacterial Foraging Optimization (BFO) algorithm. From the simulation results, the PSASRAI are calculated based on the settling time and peak over shoot concept of control input deviations of each area for different possible transactions. These Indices are useful for system operator to prepare the power system restoration plans. Moreover, the LFC loop coordinated with Redox Flow Batteries (RFB) has greatly improved the dynamic response and it reduces the control input requirements and to ensure improved PSASRAI, thereby improving the system reliability.

• Journal of Power Electronics
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• v.15 no.5
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• pp.1274-1285
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• 2015

This paper deals with model reference adaptive system (MRAS) speed estimators based on a secondary flux for linear induction motors (LIMs). The operation of these estimators significantly depends on an adaptation mechanism. Fixed-gain PI controller is the most common adaptation mechanism that may fail to estimate the speed correctly in different conditions, such as variation in machine parameters and noisy environment. Two adaptation mechanisms are proposed to improve LIM drive system performance, particularly at very low speed. The first adaptation mechanism is based on fuzzy theory, and the second is obtained from an LIM mechanical model. Compared with a conventional PI controller, the proposed adaptation mechanisms have low sensitivity to both variations of machine parameters and noise. The optimum parameters of adaptation mechanisms are tuned using an offline method through chaotic optimization algorithm (COA) because no design criterion is given to provide these values. The efficiency of MRAS speed estimator is validated by both numerical simulation and real-time hardware-in-the-loop (HIL) implementations. Results indicate that the proposed adaptation mechanisms improve performance of MRAS speed estimator.

• The Transactions of the Korean Institute of Power Electronics
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• v.11 no.5
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• pp.417-425
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• 2006

This paper presents a new speed sensorless vector control scheme of Spindle Induction Motors(SIM) which can be successfully applied to at any speed including even zero speed. The proposed sensorless vector control of SIM uses rotor flux estimator with a variable bandwidth. This approach is based on the Closed-Loop Rotor Flux Observer(CLRFO) which includes a variable bandwidth of the PI controller. For low speed operation, the bandwidth of CLRFO has a variable bandwidth structure according to the estimated rotor velocity. The experimental results show the satisfactory operation of the proposed sensorless algorithm.

• Proceedings of the KIPE Conference
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• 2001.10a
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• pp.462-465
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• 2001

The wound induction motor can provide high starting torque and reduced starting current simultaneously by inserting large scale resistor. And this technique is one of the well known methods among the induction motor starting methods and generally used for heavy load starting such as Crain and Cement factories. The conventional PI controller has been widely used in industrial application due to the simple control algorithm and in general, PI controller is used for control of current, torque, position, and speed for the wound induction motor drive system. However, the system may result in poor performance since sensors have to be used, which in turn is limited by the environmental condition. Recently, to overcome these problems, many sensorless vector control methods for the wound induction motor have been studied. This paper presents MRAS method with on-line stator resistance tuning for sensorless vector control of the wound induction motor drive. In conventional MRAS method, in low frequency, stator resistance variation can result in poor performance. Therefore, to overcome several shortages of the conventional MRAS caused by parameter variation and enhance robustness of the sensor less vector control, this paper investigates a MRAS method with on-line stator resistance tuning for sensorless vector control of the wound induction motor. The validity and effectiveness of the proposed method is verified through digital simulation.