• Proceedings of the KIEE Conference
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• summer
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• pp.301-303
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• 2004

This paper presents an islanding detection algorithm based on the second harmonic. When the DG(distributed Generator) was connected with utility supply, for maintenance and repair of equipment, an islanding occurred. So islanding detection algorithm must be developed for safety of human. Although the DG generating power is similar to power consumption. the proposed algorithm can detect the islanding condition very successfully.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.4
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• pp.441-447
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• 2021

Construction sites are complex and dangerous because heavy equipment and workers perform various operations simultaneously within limited working areas. Solitary works of heavy equipment in complex job sites can cause fatal accidents, and thus they should interact with spotters and obtain information about surrounding environments during operations. Recently, many computer vision technologies have been developed to automatically monitor construction equipment and detect their interactions with other resources. However, previous methods did not take into account the interactions between equipment and spotters, which is crucial for identifying solitary works of heavy equipment. To address the drawback, this research develops a computer vision-based solitary work detection model that considers interactive operations between heavy equipment and spotters. To validate the proposed model, the research team performed experiments using image data collected from actual construction sites. The results showed that the model was able to detect workers and equipment with 83.4 % accuracy, classify workers and spotters with 84.2 % accuracy, and analyze the equipment-to-spotter interactions with 95.1 % accuracy. The findings of this study can be used to automate manual operation monitoring of heavy equipment and reduce the time and costs required for on-site safety management.

• The Korean Journal of Community Living Science
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• v.28 no.3
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• pp.391-401
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• 2017

This study was conducted to evaluate the relationships between exposure level to pyrethroid pesticide and wearing of protective equipment in 194 Chung-nam orchard male farmers. The urinary metabolites of pyrethroid pesticides, including Cis, Trans, DBCA, and 3-PBA, were analyzed by GC/MSD. As a result of this study, the detection rate and exposure level of 3-PBA was the highest among pyrethroid metabolites discovered by orchard farmers. As a result of analyzing the actual conditions of wearing protective equipment by the subjects of this study, the rate of agricultural farmers who wore four pieces of protective equipment compared to agricultural farmers wearing a single piece of protective clothing was as high as 35.1%. Pyrethroid exposure levels were low when farmers wore more personal protective equipment (PPE). In conclusion, training with regards to pesticide hazards and protective equipment for farmers who spray pesticides will help reduce pesticide exposure levels.

• Journal of Environmental Science International
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• v.25 no.4
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• pp.525-534
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• 2016

The biological wastewater treatment plant, which uses microbial community to remove organic matter and nutrients in wastewater, is known as its nonlinear behavior and uncertainty to operate. Therefore, operation of the biological wastewater treatment process much depends on observation and knowledge of operators. The manual inspection of human operators is essential to manage the process properly, however, it is impossible to detect a fault promptly so that the process can be exposed to improper condition not securing safe effluent quality. Among various process faults, equipment malfunction is critical to maintain normal operational state. To detect equipment faults automatically, the dynamic time warping was tested using on-line oxidation-reduction potential (ORP) and dissolved oxygen (DO) profiles in a sequencing batch reactor (SBR), which is a type of wastewater treatment process. After one cycle profiles of ORP and DO were measured and stored, they were warped to the template profiles which were prepared already and the distance result, accumulated distance (D) values were calculated. If the D values were increased significantly, some kinds of faults could be detected and an alarm could be sent to the operator. By this way, it seems to be possible to make an early detecting of process faults.

• Journal of Magnetics
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• v.22 no.1
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• pp.43-48
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• 2017

In order to detect the current flowing through concealed conductor, this paper proposes a new method based on derivative method. Firstly, this paper analyzes the main peak characteristic of the derivative function of magnetic field generated by a current-carrying conductor, and a relationship between the current flowing through the conductor and the main peak of the derivative function is obtained and applied to calculate the current. Then, the method is applied to detect the conductor current flowing through grounding grids of substations. Finally, the numerical experimental and field experiment verified the feasibility and accuracy of the method, and the computing results show that the method can effectively measure the conductor current of grounding grids with low error, and the error is within 5 %.

• 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.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.313-316
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• 2015

Construction is one of the most dangerous job sectors, which reports tens of thousands of time-loss injuries and deaths every year. These disasters incur delays and additional costs to the projects. The safety management needs to be on the top primary tasks throughout the construction to avoid fatal accidents and to foster safe working environments. One of the safety regulations that are frequently violated is the wearing of personal protection equipment (PPE). In order to facilitate monitoring of the compliance of the PPE wearing regulations, this paper proposes a vision based method that automatically identifies whether workers wear hard hats and safety vests. The method involves three modules - human body detection, identification of safety vest wearing, and hard hat detection. First, human bodies are detected in the video frames captured by real-time on-site construction cameras. The detected human bodies are classified into with/without wearing safety vests based on the color features of their upper parts. Finally, hard hats are detected on the nearby regions of the detected human bodies and the locations of the detected hard hats and human bodies are correlated to reveal their corresponding matches. In this way, the proposed method provides any appearance of the workers without wearing hard hats or safety vests. The method has been tested on onsite videos and the results signify its potential to facilitate site safety monitoring.

• The KIPS Transactions:PartA
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• v.17A no.2
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• pp.81-92
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• 2010

There has been some research in the equipment defect detection based on vibration information. Most research of them is based on vibration monitoring to determine the equipment defect or not. In this paper, we introduce more accurate system for engine defect detection based on vibration information and we focus on detection of engine defect for boat and system control. First, it uses the duplicated-checking method for vibration information to determine the engine defect or not. If there is a defect happened, we use the method using error part of vibration information basis with error range to determine which kind of error is happened. On the other hand, we use the engine trend analysis and standard of safety engine to implement the vibration information database. Our simulation results show that the probability of engine defect determination is 100% and the probability of engine defect classification and detection is 96%.

• Journal of the Korean Society of Safety
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• v.35 no.1
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• pp.1-11
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• 2020

Article 325 (Prevention of Fire Explosion due to Electrostatic) of the Rule for Occupational Safety and Health Standard specifies that in order to prevent the risk of disasters caused by static electricity, fire, explosion and static electricity in the production process, However, in order to do this, it is absolutely necessary to use a pre-detection technology and a detector for antistatic discharge prediction, which is a precautionary measure by static electricity in a fire / explosion hazard place, but in Korea, And there is no technical standard for the application of the technology of the explosion proof structure of the related equipment. Research methods include domestic and overseas electrostatic discharge detection technology and literature investigation of related equipment explosion proofing technology, domestic and foreign electrostatic discharge detection device production and use situation investigation, advanced foreign technology data analysis and benchmarking. In particular, we sought to verify the results of empirical experiments using electrostatic discharge detection technology through sample purchase and analysis of related major products, development of optimization technology through prototype production, evaluation, and supplementation, and expert knowledge through expert consultation. The results of this study were developed and fabricated two prototypes of electrostatic discharge detector based on the technology / standard related to electrostatic discharge detection technology in Korea and abroad through development of electrostatic discharge detection technology and development and production of detector. In addition, based on the development of electrostatic discharge detection technology, we developed an intrinsic safety explosion proof ib class explosion proof technology applicable to the process of using and handling flammable gas and flammable liquid vapor and combustible dust. In the case of the over voltage and minimum voltage are supplied to the explosion-proof structure ESD detector, check the state of the circuit and the transient and transient currents generated by the coil and capacitor elements during the input and standby of the signal pulse voltage. Explosion-proof equipment-Part 11: Intrinsically safe explosion proof structure The comparative evaluation with the reference curve in Annex A of "i" confirms that the characteristics of the intrinsically safe explosion protection structure are met.

• Journal of the Korea Institute of Military Science and Technology
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• v.24 no.4
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• pp.392-400
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• 2021

Unlike optical equipment, SAR(Synthetic Aperture Radar) has the advantage of obtaining images in all weather, and object detection in SAR images is an important issue. Generally, deep learning-based object detection was mainly performed in real-valued network using only amplitude of SAR image. Since the SAR image is complex data consist of amplitude and phase data, a complex-valued network is required. In this paper, a complex-valued ResNet network is proposed. SAR image object detection was performed by combining the ROI transformer detector specialized for aerial image detection and the proposed complex-valued ResNet. It was confirmed that higher accuracy was obtained in complex-valued network than in existing real-valued network.