• Fire Science and Engineering
• /
• v.28 no.3
• /
• pp.87-91
• /
• 2014

This paper is aimed to find electrical fire danger for analyzing the characteristics of temperature, current and voltage signals for motor on electric fan. In order to attain this purpose, detected were the temperature, current and voltage signals on electric wire with free (normal state) and locked (abnormal state) motor. For voltage and current signals, voltage signal is no big difference with normal and abnormal states and current signal is higher in abnormal state (highest 309 mA) than the normal state (highest 203 mA). In the case of Temperature signal, the temperature distribution of the motor as a whole is different. It is difference in the case of the normal state $4^{\circ}C$ and the abnormal state $18^{\circ}C$. In particular, most of the electric wiring to the motor of the fan is attached to the fixture of motor back. Considering at allowable temperature ($60^{\circ}C$) of the electric wire could be accelerated to insulation deterioration. The results of this study will be effectively used in analyzing for electric fire and developing the preventive devices of electric fan.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.12 no.4
• /
• pp.136-145
• /
• 1998

This paper describes the FFT and coefficient analysis of vibration signals for preventive diagnosis of a mold transformer at normal and abnormal state. Varying applied voltage, loading current and temperature as control variables for he experiment, measurement variables such as magnitude of vibration signals, frequency spectrum and time series coefficient were analyzed. The vibration signals by variation of control variables were measured by acceleration sensor adhered on the surface of winding and core, and measurement variables were calculated using dat acquisition system. After analyzing the normal state, the structural distortion was also simulated. The vibration signals at abnormal state were measured by the same control variables variation as the normal state. As a result, vibration signals between normal and abnormal state could be distinguished by comparison of the perpendicular and horizontal vibration signal.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.7 no.4
• /
• pp.905-915
• /
• 2012

This paper suggests the online method to identify normal and abnormal state of water quality on the ocean USN. To define normal of the ocean water quality, we utilize the negative selection algorithm of artificial immunity system which has self and nonself identification characteristics. To distinguish abnormal status, normal state set of the ocean water quality needs to be defined. For this purpose, we generate normal state set base on mutations of each data and mutation of the data as logical product. This mutated normal (or self) sets used to identify abnormal status of the water quality. We represent the experimental result about mutated self set with the Gaussian function. Through setting the method on the ocean sensor logger, we can monitor whether the ocean water quality is normal or abnormal state by online.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.12 no.6
• /
• pp.1233-1242
• /
• 2017

Various attempts have been made to prevent crime risk. One of the cases where outdoor pedestrians are attacked by criminals is the abnormal health condition. When a mental or mental condition that can not sustain normal walking due to drunkenness is exposed, the case of being a crime is revealed through crime case analysis. In this study, we propose a method for estimating the state of an individual that can be detected in outdoor activities. In order to avoid the inconvenience of installing a separate terminal for event information transmission of sensors and sensors, it is possible to estimate an abnormal state by using a 3-axis acceleration sensor built in a smart phone. The state of the user can be estimated by analyzing the momentum of the user and analyzing it with the passage of time. It is possible to distinguish the flow of time at regular intervals, to recognize the activity patterns in each time band, and to distinguish between normal and abnormal. In this study, we have evaluated the total amount of kinetic energy and kinetic energy in each direction of the acceleration sensor and the Fourier transformed value of the total energy amount to distinguish the abnormal state.

• The Korean Journal of Physiology and Pharmacology
• /
• v.9 no.1
• /
• pp.17-22
• /
• 2005

We examined whether the abnormal EEG state by NMDA receptor blocker MK-801 can be reversed by typical and atypical antipsychotics differentially by comparing their spectral profiles after drug treatment in rats. The spectral profiles produced by typical antipsychotics chlorpromazine (5 mg/kg, i.p.) and haloperidol (0.5 mg/kg, i.p.) were differ from that by atypical antipsychotic clozapine (5 mg/kg, i.p.) in the rats treated with or without MK-801 treatment (0.2 mg/kg, i.p.) which produce behavioral abnormalities like hyperlocomotion and stereotypy. The dissimilarity between the states produced by antipsychotics and the control state was examined with the distance of the location of the canonical variables calculated by stepwise discriminant analysis with the relative band powers as input variables. Although clozapine produced more different state from normal state than typical antipsychotics, clozapine could reverse the abnormal schizophrenic state induced by MK-801 to the state closer to the normal state than the typical antipsychotics. The results suggest that atypical anesthetic can reverse the abnormal schizophrenic state with negative symptom to the normal state better than typical antipsychotic. The results indicate that the multivariate discriminant analysis using the spectral parameters can help differentiate the antipsychotics with different actions.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.12 no.2
• /
• pp.361-366
• /
• 2017

The abnormal state of the main equipment across the industry is accompanied by abnormal rise of temperature, change in the vibration and noise. In this study, we developed a smart sensing module equipped with BLE, and developed a smart sensing device that can detect abnormal vibration due to its own flaws of the equipment by interfacing with an MEMS-based acceleration sensor. The smart sensing device developed in this study can be easily installed in a small space and can monitor the vibration status of the equipment in real time, and can easily inform the user of the steady state and the problem occurrence status with array LED display.

• Asian Pacific Journal of Cancer Prevention
• /
• v.16 no.13
• /
• pp.5477-5482
• /
• 2015

Background: This study aimed to investigate the incidence and risk factors for a prethrombotic state in patients with malignant tumors. Materials and Methods: Plasma d-dimer (D-D) in patients with malignant tumors was measured. Abnormal rates of D-D and possible risk factors like gender, age, type of tumor, and staging of tumor were analyzed. Results: Of 1,453 patients, 629 demonstrated plasma D-D abnormality (43.3%). The D-D abnormal rate of male patients (n=851, 43.5%) was not statistically significantly different from that for female patients (n=602, 43.0%) (p>0.05). D-D abnormal rate increased with age and was statistically significant among different age groups (p<0.05). Regarding staging of tumor, D-D abnormal rate in patients with phase I was 2.0%, 6.2% in phase II, 47.6% in phase III and 83.1% in phase IV, with statistically significant differences between phase III and II, as well as phase III and IV (p<0.01). Conclusions: A prethrombotic state was closely related to malignancy of tumors. The risk factors for a prethrombotic state include age and tumor stage.

• International Journal of Internet, Broadcasting and Communication
• /
• v.15 no.2
• /
• pp.168-174
• /
• 2023

In the current world, with the massive scale of SOC construction, it is difficult to diagnose and check all of a bridge's abnormal states with even the experts' eyes for maintenance. It is because we should spend huge costs and time on maintenance. Still, there are not many alternative ways to inspect bridges remotely regarding accuracy or reality. Therefore, we remark on the advantages and disadvantages of previous methods through practices in SOC maintenance. To inspect the abnormal state of the Bridge, we suggest inspecting bridges with an Augmented Reality (AR) mechanism to reduce cost, human resource consumption, and the risk of work. Through the proposed approach, we expect that it provides ways to solve massive construction problems with software-based technologies.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2000.11a
• /
• pp.888-891
• /
• 2000

Drilling is one of the most important operations in machining industry and usually the most efficient and economical method of cutting a hole in metal. From automobile parts to aircraft components, almost every manufactured product requires that holes are to be drilled for the purpose of assembly, creation of fluid passages, and so on. It is therefore desirable to monitor drill wear and hole quality changes during the hole drilling process. One important aspect in controlling the drilling process is drill wear status monitoring. With the monitoring, we may decide on optimal timing for tool change. The necessity of the detection of tool wear, fracture and the abnormal tool state has been emphasized in the machining process. Accordingly, this paper deals with the cutting characteristics of the hot-rolled high strength steels using common HSS drill. The performance variables include drill wear data obtained from drilling experiments conducted on the workpiece. The results are obtained from monitoring of the cutting force and Acoustic Emission (AE) signals, and from the detection of the abnormal tool state with the computer vision system.

• Nuclear Engineering and Technology
• /
• v.55 no.4
• /
• pp.1528-1539
• /
• 2023

Since the leakage of sodium in an SFR (sodium-cooled fast reactor) causes an explosion upon reaction with air and water, sodium leakages represent an important safety issue. In this study, a novel technique for improving the reliability of sodium leakage detection applying DDVR (dynamic data validation and reconciliation) is proposed and verified to resolve this technical issue. DDVR is an approach that aims to improve the accuracy of a target system in a dynamic state by minimizing random errors, such as from the uncertainty of instruments and the surrounding environment, and by eliminating gross errors, such as instrument failure, miscalibration, or aging, using the spatial redundancy of measurements in a physical model and the reliability information of the instruments. DDVR also makes it possible to estimate the state of unmeasured points. To validate this approach for supporting sodium leakage detection, this study applies experimental data from a sodium leakage detection experiment performed by the Korea Atomic Energy Research Institute. The validation results show that the reliability of sodium leakage detection is improved by cooperation between DDVR and hardware measurements. Based on these findings, technology integrating software and hardware approaches is suggested to improve the reliability of sodium leakage detection by presenting the expected true state of the system.