• The KIPS Transactions:PartB
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• v.9B no.5
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• pp.663-672
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• 2002

In this paper, Using an automatic tracking system, behavior of an aquatic insect, Chironomus sp. (Chironomidae), was observed in semi-natural conditions in response to sub-lethal treament of a carbamate insecticide, carbofuran. The fourth instar larvae were placed in an observation cage $(6cm\times{7cm}\times{2.5cm)}$ at temperature of $18^\circ{C}$ and the light condition of 10 time (light) : 14 time (dark). The tracking system was devised to detect the instant, partial movement of the insect body. Individual movement was traced after the treatment of carbofuran (0.1ppm) for four days 2days : before treatment, 2 days : after treatment). Along with the other irregular behaviors, "ventilation activity", appearing as a shape of "compressed zig-zag", was more frequently observed after the treatment of the insecticide. The activity of the test individuals was also generally depressed after the chemical treatment. In order to detect behavioral changes of the treated specimens, wavelet analysis was implemented to characterize different movement patterns. The extracted parameters based on Discrete Wavelet Transforms (DWT) were subsequently provided to artificial neural networks to be trained to represent different patterns of the movement tracks before and after treatments of the insecticide. This combined model of wavelets and artificial neural networks was able to point out the occurrence of characteristic movement patterns, and could be an alternative tool for automatically detecting presences of toxic chemicals for water quality monitoring. quality monitoring.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.275-290
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• 2021

Sulfur dioxide (SO2) in the atmosphere is mainly generated from anthropogenic emission sources. It forms ultra-fine particulate matter through chemical reaction and has harmful effect on both the environment and human health. In particular, ground-level SO2 concentrations are closely related to human activities. Satellite observations such as TROPOMI (TROPOspheric Monitoring Instrument)-derived column density data can provide spatially continuous monitoring of ground-level SO2 concentrations. This study aims to propose a 2-step residual corrected model to estimate ground-level SO2 concentrations through the synergistic use of satellite data and numerical model output. Random forest machine learning was adopted in the 2-step residual corrected model. The proposed model was evaluated through three cross-validations (i.e., random, spatial and temporal). The results showed that the model produced slopes of 1.14-1.25, R values of 0.55-0.65, and relative root-mean-square-error of 58-63%, which were improved by 10% for slopes and 3% for R and rRMSE when compared to the model without residual correction. The model performance by country was slightly reduced in Japan, often resulting in overestimation, where the sample size was small, and the concentration level was relatively low. The spatial and temporal distributions of SO2 produced by the model agreed with those of the in-situ measurements, especially over Yangtze River Delta in China and Seoul Metropolitan Area in South Korea, which are highly dependent on the characteristics of anthropogenic emission sources. The model proposed in this study can be used for long-term monitoring of ground-level SO2 concentrations on both the spatial and temporal domains.

• Tunnel and Underground Space
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• v.23 no.6
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• pp.480-492
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• 2013

After the introduction of geothermal power generation technology based on engineering reservoir creation that can be applied on non-volcanic region, industrial need for studies on the efficient and economic execution of costly deep-depth drilling work becomes manifest increasingly. However, since it is very difficult to predict duration and cost of boring work with acceptable reliability because of many uncertain events during the execution, efficient and organized work management for drilling is not easily achievable. Especially, the round trip that discretely occurs because of the abrasion of bit takes more time as the depth goes deeper and it has a great impact on the work performance. Therefore, a technology that can simulate the occurrence timing and depth of round trip in advance and therefore optimize them is essentially required. This study divided the abrasion state of bit into eight steps for simulation cases and developed a forecast algorithm, i.e., TOSA which can analyze the depth and timing of round trip occurrence. A methodology that can divide a unit section for simulation has been suggested; while the Bourgoyne and Young model has been used for the forecast of drilling rates and bit abrasion extent by section. Lastly, the designed algorithm has been systemized for the convenience of the user.

• Journal of KIISE:Information Networking
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• v.32 no.3
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• pp.370-381
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• 2005

Mobile IP provides an efficient and scalable mechanism for host mobility within the Internet. Using Mobile If, mobile nodes may change their point of attachment to the Internet without changing their If address. However, it would result in a high signaling cost. To reduce the signaling cost, we factor in the fact that mobile users will not be actively communicating much of the time. In fact, there Is no necessity to send a binding update message to the home agent when an mobile node does not communicates with others while moving. From this point of view, we propose a lazy update strategy for minimizing signaling cost using the forwarding pointer in hierarchical Mobile IPv6. In our proposal, binding updates are sent only when a mobile node is in a busy mode. If an mobile node is in a dormant mode, binding update messages are delayed until busy mode using the forwarding pointer. As a result, our proposal can reduce the total signaling cost by eliminating unnecessary binding update messages when a mobile node Is in a dormant mode. In addition to, our proposal reduces unnecessary location update cost resulting from ping-pong effect under mobile node's dormant mode. Analysis results using the discrete analytic model presented in this paper shows that our proposal can has superior performance than hierarchical Mobile nv6 when the call-to-mobility ratio is low and the length of the forwarding pointer chain K is low.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.11 no.2
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• pp.82-91
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• 2006

We investigated the possibility of using effluents from a municipal sewage treatment plant (STP) as tracers a tracer for hydrologic studies of rivers. The possibility was checked in a 12-km long reach downstream of Jeonju Municipal Sewage Treatment Plant (JSTP). Time-series monitoring of the water chemistry reveals that chemical compositions of the effluent from the JSTP are fluctuating within a relatively wide range during the sampling period. In addition, the signals from the plant were observed at the downstream stations consecutively with increasing time lags, especially in concentrations of the conservative chemical parameters (concentrations f3r chloride and sulfate, total concentration of major cations, and electric conductivity). Based on this observation, we could estimate the stream flow (Q), velocity (v), and dispersion coefficient (D). A 1-D nonreactive solute-transport model with automated optimization schemes was used for this study. The values of Q, v, and D estimated from this study varied from 6.4 to $9.0m^3/sec$ (at the downstream end of the reach), from 0.06 to 0.10 m/sec, and from 0.7 to $6.4m^2/sec$, respectively. The results show that the effluent from a large-scaled municipal STP frequently provides good, multiple natural tracers far hydrologic studies.