• Journal of applied mathematics & informatics
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• v.41 no.6
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• pp.1257-1274
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• 2023

Rotating machines heavily rely on an intricate network of interconnected sub-components, with bearing failures accounting for a substantial proportion (40% to 90%) of all such failures. To address this issue, intelligent algorithms have been developed to evaluate vibrational signals and accurately detect faults, thereby reducing the reliance on expert knowledge and lowering maintenance costs. Within the field of machine learning, Artificial Immune Systems (AIS) have exhibited notable potential, with applications ranging from malware detection in computer systems to fault detection in bearings, which is the primary focus of this study. In pursuit of this objective, we propose a novel procedure for detecting novel instances of anomalies in varying operating conditions, utilizing only the signals derived from the healthy state of the analyzed machine. Our approach incorporates AIS augmented by Dynamic Time Warping (DTW). The experimental outcomes demonstrate that the AIS-DTW method yields a considerable improvement in anomaly detection rates (up to 53.83%) compared to the conventional AIS. In summary, our findings indicate that our method represents a significant advancement in enhancing the resilience of AIS-based novelty detection, thereby bolstering the reliability of rotating machines and reducing the need for expertise in bearing fault detection.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2360-2362
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• 1998

This paper presents a new collison-free trajectory problem for SCARA robot manipulator. we use artificial potential field for collison detection and avoidance. The potential function is typically defined as the sum of attractive potential pulling the robot toward the goal configuration and a repulsive potential pushing the robot away from the obstacles. In here, end-effector of manipulator is represented as a particle in configuration space and moving obstacles is simply represented, too. we consider not fixed obstacle but moving obstacle in random. So, we propose new distance function of artificial potential field with moving obstacle for SCARA robot. At every sampling time, the artificial potential field is update and the force driving manipulator is derived from the gradient vector of artificial potential field. To real-time path planning, we apply very simple modeling to obstacle. Some simulation results show the effectiveness of the proposed approach.

• Proceedings of the KIEE Conference
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• 2008.10c
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• pp.215-217
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• 2008

DC fraction system can damage human and other facilities due to the rising of rail potential. Therefore the earth fault detection relay protects system using rail potential induced in train operation and ground fault. However the conventional protection system cannot operate due to the fault resistance and might operate unwanted voltage rising due to the other substation ground fault. So this paper models DC traction system using PSCAD/EMTDC and simulates the rail potential rising. We can estimate the rail potential rising in DC traction system through the various simulation.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.5
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• pp.841-848
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• 1987

This paper presents a new two-stage edge detection algorithm which can give thin edges and can find edges between low contrast regions. In the first stage, the potential edge points are obtained using the proposed edge operator which is motivated by the concept of the E filter. And in the second stage, the real edge points adre detected by comparing the potential edge values with the lically adaptive threshold values of the corresponding potential edge points and by testing whether the potential edge points are zero-crossing points or not. It is shown quantitatively that the proposed algorithm can find edges effectively in an image with impulse noise, and also the results of applying the proposed algorithm to real images are presented.

• The Korean Journal of Applied Statistics
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• v.30 no.1
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• pp.159-167
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• 2017

Observations identified as potential outliers are usually tested for real outliers; however, some outlier detection methods skip a formal test or perform a test using simulated p-values. We introduce test procedures for outliers by testing subsets of potential outliers rather than by testing individual observations of potential outliers to avoid masking or swamping effects. Examples to illustrate methods and a Monte Carlo study to compare the power of the various methods are presented.

• Smart Structures and Systems
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• v.15 no.4
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• pp.997-1018
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• 2015

Structural health monitoring along with damage detection and assessment of its severity level in non-accessible reinforced concrete members using piezoelectric materials becomes essential since engineers often face the problem of detecting hidden damage. In this study, the potential of the detection of flexural damage state in the lower part of the mid-span area of a simply supported reinforced concrete beam using piezoelectric sensors is analytically investigated. Two common severity levels of flexural damage are examined: (i) cracking of concrete that extends from the external lower fiber of concrete up to the steel reinforcement and (ii) yielding of reinforcing bars that occurs for higher levels of bending moment and after the flexural cracking. The purpose of this investigation is to apply finite element modeling using admittance based signature data to analyze its accuracy and to check the potential use of this technique to monitor structural damage in real-time. It has been indicated that damage detection capability greatly depends on the frequency selection rather than on the level of the harmonic excitation loading. This way, the excitation loading sequence can have a level low enough that the technique may be considered as applicable and effective for real structures. Further, it is concluded that the closest applied piezoelectric sensor to the flexural damage demonstrates higher overall sensitivity to structural damage in the entire frequency band for both damage states with respect to the other used sensors. However, the observed sensitivity of the other sensors becomes comparatively high in the peak values of the root mean square deviation index.

• Food Science of Animal Resources
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• v.30 no.4
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• pp.590-596
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• 2010

Meat and meat products are a potential source of food-borne pathogens, including Staphylococcus aureus, Salmonella spp., Escherichia coli O157:H7, and Bacillus cereus. A sensitive and specific PCR assay for the detection of these pathogens in meat and meat products was developed in this study, as part of a broader effort to reduce the potential health hazards posed by these pathogens. Initially, PCR conditions were standardized with purified DNA. Under standard conditions, the detection level for PCR was as low as 10 pg of purified bacterial DNA. After overnight growth of bacteria in a broth medium, as few as $10^2$ CFU of bacteria were detected by PCR assay. The primers employed in the PCR assay were found to be highly specific for individual organisms, and evidenced no cross-reactivity with heterologous organisms. Additionally, the multiplex PCR assays also amplified some target genes from the four pathogens, and multiplex amplification was obtained from as little as 10 pg of DNA, thus illustrating the excellent specificity and high sensitivity of the assay. In conclusion, this PCR-based technique provides a sensitive and specific method for the detection of S. aureus, Salmonella spp., E. coli O157:H7, and B. cereus in meat and meat products.

• Journal of the Korean Society of Safety
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• v.31 no.6
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• pp.121-128
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• 2016

Social Media transformed the mass media based information traffic, and it has become a key resource for finding value in enterprises and public institutions. Particularly, in regards to disaster management, the necessity for public participation policy development through the use of social media is emphasized. National Disaster Management Research Institute developed the Social Big Board, which is a system that monitors social Big Data in real time for purposes of implementing social media disaster management. Social Big Board collects a daily average of 36 million tweets in Korean in real time and automatically filters disaster safety related tweets. The filtered tweets are then automatically categorized into 71 disaster safety types. This real time tweet monitoring system provides various information and insights based on the tweets, such as disaster issues, tweet frequency by region, original tweets, etc. The purpose of using this system is to take advantage of the potential benefits of social media in relations to disaster management. It is a first step towards disaster management that communicates with the people that allows us to hear the voice of the people concerning disaster issues and also understand their emotions at the same time. In this paper, Korean language text mining based Social Big Board will be briefly introduced, and disaster issue detection model, which is key algorithms, will be described. Disaster issues are divided into two categories: potential issues, which refers to abnormal signs prior to disaster events, and occurrence issues, which is a notification of disaster events. The detection models of these two categories are defined and the performance of the models are compared and evaluated.

• Journal of Information Processing Systems
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• v.10 no.1
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• pp.1-22
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• 2014

Recent technological advances provide the opportunity to use large amounts of multimedia data from a multitude of sensors with different modalities (e.g., video, text) for the detection and characterization of criminal activity. Their integration can compensate for sensor and modality deficiencies by using data from other available sensors and modalities. However, building such an integrated system at the scale of neighborhood and cities is challenging due to the large amount of data to be considered and the need to ensure a short response time to potential criminal activity. In this paper, we present a system that enables multi-modal data collection at scale and automates the detection of events of interest for the surveillance and reconnaissance of criminal activity. The proposed system showcases novel analytical tools that fuse multimedia data streams to automatically detect and identify specific criminal events and activities. More specifically, the system detects and analyzes series of incidents (an incident is an occurrence or artifact relevant to a criminal activity extracted from a single media stream) in the spatiotemporal domain to extract events (actual instances of criminal events) while cross-referencing multimodal media streams and incidents in time and space to provide a comprehensive view to a human operator while avoiding information overload. We present several case studies that demonstrate how the proposed system can provide law enforcement personnel with forensic and real time tools to identify and track potential criminal activity.

• Journal of Microbiology and Biotechnology
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• v.21 no.7
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• pp.719-727
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• 2011

Botulism is a neuroparalytic disease caused by Clostridium botulinum, which produces seven (A-G) antigenically diverse neurotoxins (BoNTs). BoNTs are the most poisonous substances known to humans, with a median lethal dose ($LD_{50}$) of approximately 1 ng/kg of body weight. Owing to their extreme potency and lethality, they have the potential to be used as a bioterrorism agent. The mouse bioassay is the gold standard for the detection of botulinum neurotoxins; however, it requires at least 3-4 days for completion. Attempts have been made to develop an ELISA-based detection system, which is potentially an easier and more rapid method of botulinum neurotoxin detection. The present study was designed using a synthetic gene approach. The synthetic gene encoding the catalytic domain of BoNT serotype B from amino acids 1-450 was constructed with PCR overlapping primers (BoNT/B LC), cloned in a pQE30 UA vector, and expressed in an E. coli M15 host system. Recombinant protein production was optimized at 0.5 mM IPTG final concentration, 4 h post induction, resulting in a maximum yield of recombinant proteins. The immunogenic nature of the recombinant BoNT/B LC protein was evaluated by ELISA. Antibodies were raised in BALB/c mice using various adjuvants. A significant rise in antibody titer (p<0.05) was observed in the Alum group, followed by the Titermax Classic group, Freund's adjuvant, and the Titermax Gold group. These developed high-titer antibodies may prove useful for the detection of botulinum neurotoxins in food and clinical samples.