• Environmental Mutagens and Carcinogens
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• v.23 no.2
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• pp.56-62
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• 2003

The liver progenitor cells could form a potential target cell population fore both tumor-initiating and -promoting chemicals. Induction of drug-metabolizing and antioxidant enzymes, including AhR-dependent CYP1A1, NQO-1 and AKR1C9, was detected in the rat liver epithelial WB-F344 "stem-like" cells. Additionally, WB-F344 cells express a functional, wild-type form of p53 protein, a biomarker of genotoxic events, and connexin 43, a basic structural unit of gap junctions forming an important type of intercellular communication. In this cellular model, two complementary assays have been established for detection of the modes of action associated with tumor promotion: inhibition of gap junctional intercellular communication (GJIC) and proliferative activity in confluent cells. We found that the PAHs and PCBs, which are AhR agonists, released WB-F344 cells from contact inhibition, increasing both DNA synthesis and cell numbers. Genotoxic effects of some PAHs that lead to apoptosis and cell cycle delay might interfere with the proliferative activity of PAHs. Contrary to that, the nongenotoxic low-molecular-weight PAHs and non-dioxin-like PCB congeners, abundant in the environment, did not significantly affect cell cycle and cell proliferation; however both groups of compounds inhibited GJIC in WB-F344 cells. The release from contact inhibiton by a mechanism that possibly involves the AhR activation, inhibition of GJIC and genotoxic events induced by environmental contaminants are three important modes of action that could play an important role in carcinogenic effects of toxic compounds. The relative potencies to inhibit GJIC, to induce AhR-mediated activity, and to release cells from contact inhibition were determined for a large series of PAHs and PCBs and their metabolites. In vitro bioassays based on detection of events on cellular level (deregulation of GJIC and/or proliferation) or determination of receptor-mediated activities in both ?$stem-like^{\circ}{\times}$ and hepatocyte-like liver cellular models are valuable tools for detection of modes of action of polyaromatic hydrocarbons. They may serve, together with concentration data, as a first step in their risk assessment.

• Journal of the Korea Society for Simulation
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• v.19 no.2
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• pp.17-28
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• 2010

Since a homing torpedo system consists of various subsystems, organic interactions of which dictate the performance of the torpedo system, it is necessary to estimate the effects of individual subsystems in order to obtain an optimized design of the overall system. This paper attempts to gain some insight into the detection mechanism of a torpedo run, and analyze the relative importance of various parameters of a torpedo system. A database for the analysis was generated using a simulation model based on the combined discrete event and discrete time architecture. Multiple search schemes, including the snake-search method, were applied to the torpedo model, and some parameters of the torpedo were found to be stochastic. We then analyzed the effectiveness of torpedo’s detection capability according to the torpedo speed, the target speed, and the maximum detection range.

• Journal of Veterinary Science
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• v.24 no.5
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• pp.70.1-70.14
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• 2023

Background: Mycobacterium avium subspecies paratuberculosis (MAP) causes a chronic and progressive granulomatous enteritis and economic losses in dairy cattle in subclinical stages. Subclinical infection in cattle can be detected using serum MAP antibody enzyme-linked immunosorbent assay (ELISA) and fecal polymerase chain reaction (PCR) tests. Objectives: To investigate the differences in blood parameters, according to the detection of MAP using serum antibody ELISA and fecal PCR tests. Methods: We divided 33 subclinically infected adult cattle into three groups: seronegative and fecal-positive (SNFP, n = 5), seropositive and fecal-negative (SPFN, n = 10), and seropositive and fecal-positive (SPFP, n = 18). Hematological and serum biochemical analyses were performed. Results: Although the cows were clinically healthy without any manifestations, the SNFP and SPFP groups had higher platelet counts, mean platelet volumes, plateletcrit, lactate dehydrogenase levels, lactate levels, and calcium levels but lower mean corpuscular volume concentration than the SPFN group (p < 0.017). The red blood cell count, hematocrit, monocyte count, glucose level, and calprotectin level were different according to the detection method (p < 0.05). The SNFP and SPFP groups had higher red blood cell counts, hematocrit and calprotectin levels, but lower monocyte counts and glucose levels than the SPFN group, although there were no significant differences (p > 0.017). Conclusions: The cows with fecal-positive MAP status had different blood parameters from those with fecal-negative MAP status, although they were subclinically infected. These findings provide new insights into understanding the mechanism of MAP infection in subclinically infected cattle.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.348-369
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• 2024

With the advancement of Industry 4.0 and Industrial Internet of Things (IIoT), manufacturing increasingly seeks automation and intelligence. Temperature and vibration monitoring are essential for machinery health. Traditional abnormal state detection methodologies often overlook the intricate frequency characteristics inherent in vibration time series and are susceptible to erroneously reconstructing temperature abnormalities due to the highly similar waveforms. To address these limitations, we introduce synergistic, end-to-end, unsupervised Frequency-Time Domain Memory-Enhanced Autoencoders (FTD-MAE) capable of identifying abnormalities in both temperature and vibration datasets. This model is adept at accommodating time series with variable frequency complexities and mitigates the risk of overgeneralization. Initially, the frequency domain encoder processes the spectrogram generated through Short-Time Fourier Transform (STFT), while the time domain encoder interprets the raw time series. This results in two disparate sets of latent representations. Subsequently, these are subjected to a memory mechanism and a limiting function, which numerically constrain each memory term. These processed terms are then amalgamated to create two unified, novel representations that the decoder leverages to produce reconstructed samples. Furthermore, the model employs Spectral Entropy to dynamically assess the frequency complexity of the time series, which, in turn, calibrates the weightage attributed to the loss functions of the individual branches, thereby generating definitive abnormal scores. Through extensive experiments, FTD-MAE achieved an average ACC and F1 of 0.9826 and 0.9808 on the CMHS and CWRU datasets, respectively. Compared to the best representative model, the ACC increased by 0.2114 and the F1 by 0.1876.

• Journal of Aerospace System Engineering
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• v.18 no.4
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• pp.43-52
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• 2024

This paper presents an anomaly detection system that uses an LSTM-Autoencoder model to identify early-stage anomalies in the blade pitch system of floating wind turbines. The sensor data used in power plant monitoring systems is primarily composed of multivariate time-series data for each component. Comprising two unidirectional LSTM networks, the system skillfully uncovers long-term dependencies hidden within sequential time-series data. The autoencoder mechanism, learning solely from normal state data, effectively classifies abnormal states. Thus, by integrating these two networks, the system can proficiently detect anomalies. To confirm the effectiveness of the proposed framework, a real multivariate time-series dataset collected from a wind turbine model was employed. The LSTM-autoencoder model showed robust performance, achieving high classification accuracy.

• Journal of Aerospace System Engineering
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• v.16 no.1
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• pp.49-55
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• 2022

The role of drones has been expanded to various fields such as agriculture, construction, and logistics. In particular, agriculture drones are emerging as an effective alternative to solve the problem of labor shortage and reduce the input cost. In this study therefore, we proposed the fruit recognition algorithm and harvesting mechanism for fruit harvesting drone system that can safely harvest fruits at high positions. In the fruit recognition algorithm, we employ "You-Only-Look-Once" which is a deep learning-based object detection algorithm and verify its feasibility by establishing a virtual simulation environment. In addition, we propose the fruit harvesting mechanism which can be operated by a single driving motor. The rotational motion of the motor is converted into a linear motion by the scotch yoke, and the opened gripper moves forward, grips a fruit and rotates it for harvesting. The feasibility of the proposed mechanism is verified by performing Multi-body dynamics analysis.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.8
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• pp.1551-1559
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• 2015

Recently, the performance of smart devices is almost similar to that of the existing PCs, thus the users of smart devices can perform similar works such as messengers, SNSs(Social Network Services), smart banking, etc. originally performed in PC environment using smart devices. Although the development of smart devices has led to positive impacts, it has caused negative changes such as an increase in security threat aimed at mobile environment. Specifically, the threats of mobile devices, such as leaking private information, generating unfair billing and performing DDoS(Distributed Denial of Service) attacks has continuously increased. Over 80% of the mobile devices use android platform, thus, the number of damage caused by mobile malware in android platform is also increasing. In this paper, we propose android based malware detection mechanism using time-series analysis, which is one of statistical-based detection methods.We use auto-regressive moving-average model which is extracting accurate predictive values based on existing data among time-series model. We also use fast and exact malware detection method by extracting possible malware data through Z-Score. We validate the proposed methods through the experiment results.

• Science of Emotion and Sensibility
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• v.15 no.1
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• pp.37-46
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• 2012

We tested the effects of endogenous attention and reorienting on the performance of detection task. In the classic detection paradigm of Posner and Cohen (1980), performance on target detection is measured, where target appears either on the same or difference spatial location of cue stimulus after brief period of SOA (stimulus onset asynchrony). In this study, we induced exogenous attention by manipulating predictability of cue for target, and also induced reorientation by inserting additional (reorienting) cue between initial cue and target. Experiment 1 had three conditions of reorienting speed: Early, middle, and late. Facilitation and IOR (inhibition of return) occurred in different forms depending on SOA and reorienting speed, but we were not able to discover interpretable pattern in the results. However, reanalysis of early reorienting condition revealed that facilitation and IOR occurred in a crossed manner where short SOA found facilitation and long SOA did IOR, the typical results of simple detection task. Experiment 2 collected additional data to replicate the results in early reorienting condition of experiment 1. The results obtained that facilitation occurred with short SOA and IOR with long SOA. These results contrast with those of Wright and Richard (2000) where they reported elimination of IOR when cue had predictability of target locations. These results suggest that additional cue (here, orienting cue), which rapidly appears before extinction of IOR by prior cue, brings about double IOR. The present research demonstrates that even when attention is allocated to certain location via endogenous mechanism, rapidly repeating cues in certain location maximizes IOR that offsets the effects of endogenous attention to the same location.

• Journal of the Korea Computer Graphics Society
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• v.28 no.3
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• pp.55-65
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• 2022

Recently, deep learning-based automated systems for identifying and detecting landmarks have been proposed. In order to train such a deep learning-based model without overfitting, a large amount of image and labeling data is required. Conventionally, an experienced reader manually identifies and labels landmarks in a patient's image. However, such measurement is not only expensive, but also has poor reproducibility, so the need for an automated labeling method has been raised. In addition, in the X-ray image, since various human tissues on the path through which the photons pass are displayed, it is difficult to identify the landmark compared to a general natural image or a 3D image modality image. In this study, we propose a geometric data augmentation technique that enables the generation of a large amount of labeling data in X-ray images. In addition, the optimal attention mechanism for landmark detection was presented through the implementation and application of various attention techniques to improve the detection performance of 16 major landmarks in the skull. Finally, among the major cranial landmarks, markers that ensure stable detection are derived, and these markers are expected to have high clinical application potential.

• Journal of Information Processing Systems
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• v.2 no.1
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• pp.48-51
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• 2006

To achieve security in wireless sensor networks (WSN), it is important to be able to encrypt messages sent among sensor nodes. We propose a new cryptographic key management protocol, which is based on the clustering scheme but does not depend on the probabilistic key. The protocol can increase the efficiency to manage keys since, before distributing the keys by bootstrap, the use of public keys shared among nodes can eliminate the processes to send or to receive keys among the sensors. Also, to find any compromised nodes safely on the network, it solves safety problems by applying the functions of a lightweight attack-detection mechanism.