• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.5
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• pp.407-417
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• 2019

When a free-falling ballistic missile intercepts a maritime target in a sea clutter environment at high grazing angle, detection performance of the ballistic missile's seeker can be rapidly degraded by the effect of sea clutter. To solve this problem, it is necessary to verify the performance of maritime target detection via simulations based on various scenarios. We accomplish this by applying a two-dimensional cell -averaging constant false alarm rate detector to a two-dimensional radar image, which is generated by merging a sea clutter signal at high grazing angle with a maritime target signal corresponding to the signal-to-clutter ratio. Simulation results using a computer-aided design model and commercial numerical electromagnetic solver in various scenarios show that the performance of maritime target detection significantly depends on the grazing and azimuth angles.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.77-83
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• 2019

Major causes of industrial accidents include falls and gas leak. The existing safety helmet and smart device combination products are focused on convenience, so the functions to prevent such accidents are insufficient. We developed a smart helmet focusing on fall accident detection and gas leak detection. We also developed management system to manage workers efficiently. Its core function is to detect dangerous conditions of employees, to communicate with managers and to confirm the situations of workers. The effectiveness of the combustible gas measurement capability was verified through experiments. However, since a significant amount of power consumption is founded due to continuous operation of the board and the sensor, countermeasures such as replacing with a large capacity battery are required.

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.2
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• pp.86-91
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• 2020

As the number of elderly people living alone increases, the frequency of fall accidents is also increasing. Falls are a threat to the health of older adults and can reduce their ability to remain independent. To solve this problem, we need real-time technology to recognize and respond to the critical condition of the elderly living alone. Therefore, this paper proposes a modified fall detection algorithm based on YOLO-KCF that can check one of the emergency situations in real time for the elderly living alone. YOLO can detect not only the detection of objects, but also the behavior of objects, namely stand and fall. Therefore, this paper can detect fall using the ratio of change of boundary box between stand and falling situation, and this algorithm can improve the shortcomings of KCF.

• Journal of Korea Multimedia Society
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• v.23 no.1
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• pp.50-57
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• 2020

The work that detection and elimination to disease and pest have important in agricultural field because it is directly related to the production of the crops, early detection and treatment of the disease insects. Image classification technology based on traditional computer vision have not been applied in part such as disease and pest because that is falling a accuracy to extraction and classification of feature. In this paper, we proposed model that determine to disease and pest of shiitake based on deep-CNN which have high image recognition performance than exist study. For performance evaluation, we compare evaluation with Alexnet to a proposed deep learning evaluation model. We were compared a proposed model with test data and extend test data. The result, we were confirmed that the proposed model had high performance than Alexnet which approximately 48% and 72% such as test data, approximately 62% and 81% such as extend test data.

• Journal of Electrical Engineering and Technology
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• v.13 no.4
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• pp.1515-1527
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• 2018

This paper presents a sensorless speed control of IPMSM (Interior Permanent Magnet Synchronous Motor) using the high-frequency (HF) square wave injection method. In the proposed HF pulsating square wave injection method, injection voltage is applied into the estimated d-axis of rotor and high-frequency induced q-axis current is considered to estimate the rotor position. Conventional square wave injection methods may need complex demodulation process to find rotor position, while in the proposed method, an easy demodulation process based on the rising-falling edge of the injected voltage and carrier induced q-axis current is implemented, which needs less processing time and improves control bandwidth. Unlike some saliency-based sensorless methods, the proposed method uses maximum torque per ampere (MTPA) strategy, instead of zero d-axis command current strategy, to improve control performance. Furthermore, this paper directly uses resultant d-axis current to detect the magnet polarity and eliminates the need to add an extra pulse injection for magnet polarity detection. As experimental results show, the proposed method can quickly find initial rotor position and MTPA strategy helps to improve the control performance. The effectiveness of the proposed method and all theoretical concepts are verified by mathematical equations, simulation, and experimental tests.

• Journal of the Korea Institute of Building Construction
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• v.23 no.1
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• pp.81-92
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• 2023

According to the 2020 industrial accident reports of the Ministry of Employment and Labor, the number of fatal accidents in the construction industry over the past 5 years has been higher than in other industries. Of these more than 50% of fatal accidents are initially caused by fall accidents. The central government is intensively managing falling/jamming protection device and the use of personal protective equipment to eradicate the inappropriate factors disrupting safety at construction sites. In addition, although efforts have been made to prevent safety accidents with the proposal of the Special Act on Construction Safety, fatalities on construction sites are constantly occurring. Therefore, this study developed a model that automatically detects the wearing state of the worker's safety helmet and belt using computer vision technology. In considerations of conditions occurring at construction sites, we suggest an optimization method, which has been verified in terms of the accuracy and operation speed of the proposed model. As a result, it is possible to improve the efficiency of inspection and patrol by construction site managers, which is expected to contribute to reinforcing competency of safety management.

• Journal of Biomedical Engineering Research
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• v.32 no.1
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• pp.7-14
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• 2011

This study suggested an optimized algorithm for detecting the loss of balance(LOB) in the seated position. And the sensitivity analysis was performed in order to identify the role of each design variable in the algorithm. The LOB algorithm consisted of data processing of measured signals, an internal model of the central nervous system and a control error anomaly(CEA) detector. This study optimized design variables of a CEA detector to obtain improved values of the success rate(SR) of detecting the LOB and the margin time(MT) provided for preventing the falling. Nine healthy adult volunteers were involved in the experiments. All the subjects were asked to balance their body in a predescribed seated posture with the rear legs of a four-legged wooden chair. The ground reaction force from the right leg was measured from the force plate while the accelerations of the chair and the head were measured from a couple of piezoelectric accelerometers. The measured data were processed to predict the LOB using a detection algorithm. Variables S2, h2 and hd are related to the detector: S2 represents a data selecting window, h2 a time shift and hd an operating period of the LOB detection algorithm. S2 was varied from 0.1 to 10 sec with an increment of 0.1 sec, and both h2 and hd were varied from 0.01 to 1.0 sec with an increment of 0.01 sec. It was found that the SR and MT were increased by up to 9.7% and 0.497 sec comparing with the previously published case when the values of S2, h2 and hd were set to 4.5, 0.3 and 0.2 sec, respectively. Also the results of sensitivity analysis showed that S2 and h2 had considerable influence on the SR while these variables were not so sensitive to the MT.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.11B
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• pp.655-662
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• 2007

Monitoring systems used at present to operate HVAC(Heating, Ventilation and Air Conditioning) optimally do not have a function that enables to detect faults properly when there are faults of such as operating plants or performance falling, so they are unable to manage faults rapidly and operate optimally. In this paper, we have developed a classified rule-based fault detection system which can be inclusively used in HVAC system of a building by installation of sensor which is composed of HVAC system and required low costs compare to the model based fault detection system which can be used only in a special building or system. In order to experiment this algorithm, it was applied to HVAC system which is installed inside EC(Environment Chamber), verified its own practical effect, and confirmed its own applicability to the related field in the future.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1649-1654
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• 2021

In this paper, we introduce a regularization of long short-term memory (LSTM) based fall detection system using TensorFlow that can detect falls that can occur in the elderly. Fall detection uses data from a 3-axis acceleration sensor attached to the body of an elderly person and learns about a total of 7 behavior patterns, each of which is a pattern that occurs in daily life, and the remaining 3 are patterns for falls. During training, a normalization process is performed to effectively reduce the loss function, and the normalization performs a maximum-minimum normalization for data and a L2 regularization for the loss function. The optimal regularization conditions of LSTM using several falling parameters obtained from the 3-axis accelerometer is explained. When normalization and regularization rate λ for sum vector magnitude (SVM) are 127 and 0.00015, respectively, the best sensitivity, specificity, and accuracy are 98.4, 94.8, and 96.9%, respectively.

• Journal of Korea Multimedia Society
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• v.18 no.4
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• pp.565-574
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• 2015

Falls are the main cause of serious injuries and accidental deaths in people over the age of 65. Due to widespread adoption of smartphones, there has been a growing interest in the use of smartphones for detecting human behavior and activities. Modern smartphones are equipped with a wide variety of sensors such as an accelerometer, a gyroscope, camera, GPS, digital compass and microphone. In this paper, we introduce a new method that determines the fall direction of human subjects by analyzing the three axis components of acceleration vector.