• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.13 no.1
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• pp.48-55
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• 2003

Hidden Markov Model(HMM) has a doubly embedded stochastic process with an underlying stochastic process that can be observed through another set of stochastic processes. This structure of HMM is useful for modeling vector sequence that doesn't look like a stochastic process but has a hidden stochastic process. So, HMM approach has become popular in various areas in last decade. The increasing popularity of HMM is based on two facts : rich mathematical structure and proven accuracy on critical application. In this paper, we applied continuous HMM (CHMM) approach with AR coefficient to detect and predict the chatter of lathe bite and to diagnose the wear of oil Journal bearing using rotor shaft displacement. Our examples show that CHMM approach is very efficient method for machine health monitoring and prediction.

• Communications for Statistical Applications and Methods
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• v.25 no.6
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• pp.619-631
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• 2018

Functional data analysis continues to attract interest because advances in technology across many fields have increasingly permitted measurements to be made from continuous processes on a discretized scale. Particulate matter is among the most harmful air pollutants affecting public health and the environment, and levels of PM10 (particles less than 10 micrometers in diameter) for regions of California remain among the highest in the United States. The relatively high frequency of particulate matter sampling enables us to regard the data as functional data. In this work, we investigate the dominant modes of variation of PM10 using functional data analysis methodologies. Our analysis provides insight into the underlying data structure of PM10, and it captures the size and temporal variation of this underlying data structure. In addition, our study shows that certain aspects of size and temporal variation of the underlying PM10 structure are associated with changes in large-scale climate indices that quantify variations of sea surface temperature and atmospheric circulation patterns.

• International Journal of Safety
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• v.3 no.1
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• pp.27-31
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• 2004

Some of the petroleum products contain benzene which is well known as a confirmed human carcinogen. For example, gasoline products contain benzene ranging up to several percents by weight. High exposures to the benzene and other organic solvents would be likely to occur during intermittent tasks and or processes rather than continuous jobs such as sampling, repair, inspection, and loading/unloading jobs. The work time for these jobs is various. However, most of work time is very short and the representative time interval is 15 minutes. Thus, it is preferable to do exposure assessment for 15 minute time weighted average which is known as a short time exposure level(STEL) by ACGIH rather than for 8-hours TWA. It is particularly significant to the exposure monitoring for benzene since it has been known that the exposure rate plays an important role to provoke the leukemia. Due to the large variations, a number of processes/tasks, the traditional sampling technique for organic solvents with the use of the charcoal and sampling pumps is not appropriate. Limited number of samples can be obtained due to the shortage of sampling pumps. Passive samplers can eliminate these limitations. However, low sampling rates resulted in collection of small amount of the target analysts in the passive samplers. This is originated the nature of passive samplers. Field applications were made with use of passive samplers to compare with the charcoal tube methods for 15 minutes. Gasoline loading processes to the tank lorry trucks at the loading stations in the petroleum products storage area. Good agreements between the results of passive samplers and those of the charcoal tubes were achieved. However, it was found that special cautions were necessary during the analysis at very low concentration levels.

• Computers and Concrete
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• v.8 no.5
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• pp.563-581
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• 2011

The signal-based acoustic emission (AE) characterization of concrete fracture process utilizing home-programmed AE monitoring system was performed for three kinds of static loading tests (Cubic-splitting, Direct-shear and Pull-out). Each test was carried out to induce a distinct fracture mode of concrete. Apart from monitoring and recording the corresponding fracture process of concrete, various methods were utilized to distinguish the characteristics of detected AE waveform to interpret the information of fracture behavior of AE sources (i.e. micro-cracks of concrete). Further, more signal-based characters of AE in different stages were analyzed and compared in this study. This research focused on the relationship between AE signal characteristics and fracture processes of concrete. Thereafter, the mode of concrete fracture could be represented in terms of AE signal characteristics. By using cement-based piezoelectric composite sensors, the AE signals could be detected and collected with better sensitivity and minimized waveform distortion, which made the characterization of AE during concrete fracture process feasible. The continuous wavelet analysis technique was employed to analyze the wave-front of AE and figure out the frequency region of the P-wave & S-wave. Defined RA (rising amplitude), AF (average frequency) and P-wave & S-wave importance index were also introduced to study the characters of AE from concrete fracture. It was found that the characters of AE signals detected during monitoring could be used as an indication of the cracking behavior of concrete.

• Journal of Korean Society for Atmospheric Environment
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• v.20 no.2
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• pp.195-204
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• 2004

In this study, the concentrations of major reduced sulfur compounds (H$_2$S, $CH_3$SH, DMS, and DMDS) were determined from ambient air in a monitoring station located in the mid-eastern area of Seoul. Measurements of sulfur species were conducted by the combination of on -line air sampling, thermal desorption, and capillary GC/PFPD analysis. A total number of 143 hourly samples were collected in the two time periods set between June and July 2003. The mean concentrations of four sulfur species measured in the whole study period were found on the order: DMS (535$\pm$183) > H$_2$S (47$\pm$10) > DMDS (35$\pm$22) > $CH_3$SH (6.19$\pm$29.4 pptv). The results of this study show that the concentrations of DMS at the study area are generally higher than those reported previously in the oceanic environments, while those of other sulfur species are not easy to compare with due to the lack of data. The H$_2$S concentrations were generally higher during the daytime than the nighttime, whereas those of others generally exhibited a reversed diurnal pattern. The overall results of our study suggest that the distribution of major reduced S compounds should be controlled by diverse processes in the urban area.

• Advances in nano research
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• v.17 no.2
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• pp.109-124
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• 2024

In the era of smart manufacturing, precise prediction of springback-a common issue in ultra-thin sheet metal forming- and forming limits are critical for ensuring high-quality production and minimizing waste. This paper presents a novel approach that leverages the Internet of Things (IoT) and Artificial Neural Networks (ANN) to enhance springback and forming limits prediction accuracy. By integrating IoT-enabled sensors and devices, real-time data on material properties, forming conditions, and environmental factors are collected and transmitted to a central processing unit. This data serves as the input for an ANN model, which is trained with crystal plasticity simulations and experimental data to predict springback with high precision. Our proposed system not only provides continuous monitoring and adaptive learning capabilities but also facilitates real-time decision-making in manufacturing processes. Experimental results demonstrate significant improvements in prediction accuracy compared to traditional methods, highlighting the potential of IoT and ANN integration in advancing smart manufacturing. This approach promises to revolutionize quality control and operational efficiency in the industry, paving the way for more intelligent and responsive manufacturing systems.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1242-1242
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• 2022

Annually, several construction workers fall ill, are injured, or die due to heat-related exposure. The prevalence of work-related heat illness may rise and become an issue for workers operating in temperate climates, given the increase in frequency and intensity of heatwaves in the US. An increase in temperature negatively impacts physical exertion levels and mental state, thereby increasing the potential of accidents on the job site. To reduce the impact of heat stress on workers, it is critical to develop and implement measures for monitoring physical exertion levels and mental state in hot conditions. For this, limited studies have evaluated the utility of wearable biosensors in measuring physical exertion and mental workload in hot conditions. In addition, most studies focus solely on male participants, with little to no reference to female workers who may be exposed to greater heat stress risk. Therefore, this study aims to develop a process for objective and continuous assessment of worker physical exertion and mental workload using wearable biosensors. Physiological data were collected from eight (four male and four female) participants performing a simulated drilling task at 92oF and about 50% humidity level. After removing signal artifacts from the data using multiple filtering processes, the data was compared to a perceived muscle exertion scale and mental workload scale. Results indicate that biosensors' features can effectively detect the change in worker physical and mental state in hot conditions. Therefore, wearable biosensors provide a feasible and effective opportunity to continuously assess worker physical exertion and mental workload.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.9 no.3
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• pp.83-92
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• 2004

A continuous monitoring of textural characteristics of surface sediments, sedimentation rates and beach profile was carried out to investigate the seasonal variations of sedimentary processes in the Imjado beach, southwestern coast of Korea for two years. The beach profiles consist of steep beach face and relatively flat middle and low tide beaches. The slope of the beach face increases in summer and decreases in winter, in good accordance with the standard beach cycle. Ridge and runnel systems are well developed in the middle and low tide beaches during the summer, but these structures are replaced by mega-ripples during the winter. The sediments are fining southward as well as landward. The mean grain-size tends to be increasingly coarser during seasons of autumn and winter on the north beach and during seasons of winter and spring on the south one. In addition, the sediments are eroded on the north beach and accumulated on the south one as a whole. These are probably due to southward transportation of the sediments as long-shore current (NE-SW) runs around the coastal line of the beach. However, the seasonal variations in accumulation rates are very complex and irregular. It is considered that the Imjado beach represents in non-equilibrium state, as a result of coastal and submarine topographic changes by artificial agents and sea-level uprising associated with global warming.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.11
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• pp.127-133
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• 2014

In order to protect information asset from various malicious code, Honeypot system is implemented. Honeypot system is designed to elicit attacks so that internal system is not attacked or it is designed to collect malicious code information. However, existing honeypot system is designed for the purpose of collecting information, so it is designed to induce inflows of attackers positively by establishing disguised server or disguised client server and by providing disguised contents. In case of establishing disguised server, it should reinstall hardware in a cycle of one year because of frequent disk input and output. In case of establishing disguised client server, it has operating problem such as procuring professional labor force because it has a limit to automize the analysis of acquired information. To solve and supplement operating problem and previous problem of honeypot's hardware, this thesis suggested hybrid honeypot. Suggested hybrid honeypot has honeywall, analyzed server and combined console and it processes by categorizing attacking types into two types. It is designed that disguise (inducement) and false response (emulation) are connected to common switch area to operate high level interaction server, which is type 1 and low level interaction server, which is type 2. This hybrid honeypot operates low level honeypot and high level honeypot. Analysis server converts hacking types into hash value and separates it into correlation analysis algorithm and sends it to honeywall. Integrated monitoring console implements continuous monitoring, so it is expected that not only analyzing information about recent hacking method and attacking tool but also it provides effects of anticipative security response.

• JSTS:Journal of Semiconductor Technology and Science
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• v.11 no.3
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• pp.135-145
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• 2011

Accurate process characterization and optimization are the first step for a successful advanced process control (APC), and they should be followed by continuous monitoring and control in order to run manufacturing processes most efficiently. In this paper, process characterization and recipe optimization methods with multiple outputs are presented in high density plasma-chemical vapor deposition (HDP-CVD) silicon dioxide deposition process. Five controllable process variables of Top $SiH_4$, Bottom $SiH_4$, $O_2$, Top RF Power, and Bottom RF Power, and two responses of interest, such as deposition rate and uniformity, are simultaneously considered employing both statistical response surface methodology (RSM) and neural networks (NNs) based genetic algorithm (GA). Statistically, two phases of experimental design was performed, and the established statistical models were optimized using performance index (PI). Artificial intelligently, NN process model with two outputs were established, and recipe synthesis was performed employing GA. Statistical RSM offers minimum numbers of experiment to build regression models and response surface models, but the analysis of the data need to satisfy underlying assumption and statistical data analysis capability. NN based-GA does not require any underlying assumption for data modeling; however, the selection of the input data for the model establishment is important for accurate model construction. Both statistical and artificial intelligent methods suggest competitive characterization and optimization results in HDP-CVD $SiO_2$ deposition process, and the NN based-GA method showed 26% uniformity improvement with 36% less $SiH_4$ gas usage yielding 20.8 ${\AA}/sec$ deposition rate.