• Korean Journal of Clinical Laboratory Science
• /
• v.54 no.2
• /
• pp.157-162
• /
• 2022

The purpose of the direct cortical and subcortical stimulation technique is to prevent false positives caused by transcranial electrical motor evoked potentials (TceMEP) in surgery on patients with brain tumors that have occurred around the motor cortex and to preserve the correct mapping of motor areas during surgery and the corticospinal tract. In addition, it reduces the trial and error that occurs during the intraoperative neurophysiological monitoring (INM) process and minimizes the test time, so that accurate information is communicated to the surgeon with quick feedback on the test results. The most important factors of this technique are, first, examination at a stimulus threshold of a certain intensity, and second, maintaining anesthesia depth at an appropriate level to prevent false positives from occurring during surgery. The third is the installation of a multi-level channel recording electrode on the opposite side of the area of operation to measure the TceMEP waveform and the response to direct cortical and subcortical stimulation in as many muscles as possible. If these conditions are maintained, it is possible to predict causes that may occur in other factors, not false positives, from the INM test.

• Journal of Sensor Science and Technology
• /
• v.32 no.2
• /
• pp.110-117
• /
• 2023

Reconstructing underwater geometry in real time with forward-looking sonar is critical for applications such as localization, mapping, and path planning. Geometrical data must be repeatedly calculated and overwritten in real time because the reliability of the acoustic data is affected by various factors. Moreover, scattering of signal data during the coordinate conversion process may lead to geometrical errors, which lowers the accuracy of the information obtained by the sensor system. In this study, we propose a three-step data processing method with low computational cost for real-time operation. First, the number of data points to be interpolated is determined with respect to the distance between each point and the size of the data grid in a Cartesian coordinate system. Then, the data are processed with a nonlinear interpolation so that they exhibit linear properties in the coordinate system. Finally, the data are transformed based on variations in the position and orientation of the sonar over time. The results of an evaluation of our proposed approach in a simulation show that the nonlinear interpolation operation constructed a continuous underwater geometry dataset with low geometrical error.

• Journal of Internet of Things and Convergence
• /
• v.9 no.3
• /
• pp.61-67
• /
• 2023

In order to optimize the pulse electroforming copper process, a double hidden layer BP (Back Propagation) neural network is constructed. Through sample training, the mapping relationship between electroforming copper process conditions and target properties is accurately established, and the prediction of microhardness and tensile strength of the electroforming layer in the pulse electroforming copper process is realized. The predicted results are verified by electrodeposition copper test in copper pyrophosphate solution system with pulse power supply. The results show that the microhardness and tensile strength of copper layer predicted by "3-4-3-2" structure double hidden layer neural network are very close to the experimental values, and the relative error is less than 2.32%. In the parameter range, the microhardness of copper layer is between 100.3~205.6MPa and the tensile strength is between 112~485MPa.When the microhardness and tensile strength are optimal,the corresponding process conditions are as follows: current density is 2A-dm-2, pulse frequency is 2KHz and pulse duty cycle is 10%.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.5
• /
• pp.1339-1355
• /
• 2023

Localization is a hot research spot for many areas, especially in the mobile robot field. Due to the weak signal of the global positioning system (GPS), the alternative schemes in an indoor environment include wireless signal transmitting and receiving solutions, laser rangefinder to build a map followed by a re-localization stage and visual positioning methods, etc. Among all wireless signal positioning techniques, Wi-Fi is the most common one. Wi-Fi access points are installed in most indoor areas of human activities, and smart devices equipped with Wi-Fi modules can be seen everywhere. However, the localization of a mobile robot using a Wi-Fi scheme usually lacks orientation information. Besides, the distance error is large because of indoor signal interference. Another research direction that mainly refers to laser sensors is to actively detect the environment and achieve positioning. An occupancy grid map is built by using the simultaneous localization and mapping (SLAM) method when the mobile robot enters the indoor environment for the first time. When the robot enters the environment again, it can localize itself according to the known map. Nevertheless, this scheme only works effectively based on the prerequisite that those areas have salient geometrical features. If the areas have similar scanning structures, such as a long corridor or similar rooms, the traditional methods always fail. To address the weakness of the above two methods, this work proposes a coarse-to-fine paradigm and an improved localization algorithm that utilizes Wi-Fi to assist the robot localization in a geometrically similar environment. Firstly, a grid map is built by using laser SLAM. Secondly, a fingerprint database is built in the offline phase. Then, the RSSI values are achieved in the localization stage to get a coarse localization. Finally, an improved particle filter method based on the Wi-Fi signal values is proposed to realize a fine localization. Experimental results show that our approach is effective and robust for both global localization and the kidnapped robot problem. The localization success rate reaches 97.33%, while the traditional method always fails.

• Geomechanics and Engineering
• /
• v.34 no.3
• /
• pp.267-284
• /
• 2023

Landslides are one of the most dangerous phenomena and natural disasters. Landslides cause many human and financial losses in most parts of the world, especially in mountainous areas. Due to the climatic conditions and topography, people in the northern and western regions of Iran live with the risk of landslides. One of the measures that can effectively reduce the possible risks of landslides and their crisis management is to identify potential areas prone to landslides through multi-criteria modeling approach. This research aims to model landslide potential area in the Oshvand watershed using a support vector machine algorithm. For this purpose, evidence maps of seven effective factors in the occurrence of landslides namely slope, slope direction, height, distance from the fault, the density of waterways, rainfall, and geology, were prepared. The maps were generated and weighted using the continuous fuzzification method and logistic functions, resulting values in zero and one range as weights. The weighted maps were then combined using the support vector machine algorithm. For the training and testing of the machine, 81 slippery ground points and 81 non-sliding points were used. Modeling procedure was done using four linear, polynomial, Gaussian, and sigmoid kernels. The efficiency of each model was compared using the area under the receiver operating characteristic curve; the root means square error, and the correlation coefficient . Finally, the landslide potential model that was obtained using Gaussian's kernel was selected as the best one for susceptibility of landslides in the Oshvand watershed.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.11 no.2
• /
• pp.72-78
• /
• 2009

The demand for rainfall data in gridded digital formats has increased in recent years due to the close linkage between hydrological models and decision support systems using the geographic information system. One of the most widely used tools for digital rainfall mapping is the PRISM (parameter-elevation regressions on independent slopes model) which uses point data (rain gauge stations), a digital elevation model (DEM), and other spatial datasets to generate repeatable estimates of monthly and annual precipitation. In the PRISM, rain gauge stations are assigned with weights that account for other climatically important factors besides elevation, and aspects and the topographic exposure are simulated by dividing the terrain into topographic facets. The size of facet or grid cell resolution is determined by the density of rain gauge stations and a $5{\times}5km$ grid cell is considered as the lowest limit under the situation in Korea. The PRISM algorithms using a 270m DEM for South Korea were implemented in a script language environment (Python) and relevant weights for each 270m grid cell were derived from the monthly data from 432 official rain gauge stations. Weighted monthly precipitation data from at least 5 nearby stations for each grid cell were regressed to the elevation and the selected linear regression equations with the 270m DEM were used to generate a digital precipitation map of South Korea at 270m resolution. Among 1.25 million grid cells, precipitation estimates at 166 cells, where the measurements were made by the Korea Water Corporation rain gauge network, were extracted and the monthly estimation errors were evaluated. An average of 10% reduction in the root mean square error (RMSE) was found for any months with more than 100mm monthly precipitation compared to the RMSE associated with the original 5km PRISM estimates. This modified PRISM may be used for rainfall mapping in rainy season (May to September) at much higher spatial resolution than the original PRISM without losing the data accuracy.

• Journal of Intelligence and Information Systems
• /
• v.18 no.2
• /
• pp.19-28
• /
• 2012

After the internet era, we are moving to the ubiquitous society. Nowadays the people are interested in the multimodal interaction technology, which enables audience to naturally interact with the computing environment at the exhibitions such as gallery, museum, and park. Also, there are other attempts to provide additional service based on the location information of the audience, or to improve and deploy interaction between subjects and audience by analyzing the using pattern of the people. In order to provide multimodal interaction service to the audience at the exhibition, it is important to distinguish the individuals and trace their location and route. For the location tracking on the outside, GPS is widely used nowadays. GPS is able to get the real time location of the subjects moving fast, so this is one of the important technologies in the field requiring location tracking service. However, as GPS uses the location tracking method using satellites, the service cannot be used on the inside, because it cannot catch the satellite signal. For this reason, the studies about inside location tracking are going on using very short range communication service such as ZigBee, UWB, RFID, as well as using mobile communication network and wireless lan service. However these technologies have shortcomings in that the audience needs to use additional sensor device and it becomes difficult and expensive as the density of the target area gets higher. In addition, the usual exhibition environment has many obstacles for the network, which makes the performance of the system to fall. Above all these things, the biggest problem is that the interaction method using the devices based on the old technologies cannot provide natural service to the users. Plus the system uses sensor recognition method, so multiple users should equip the devices. Therefore, there is the limitation in the number of the users that can use the system simultaneously. In order to make up for these shortcomings, in this study we suggest a technology that gets the exact location information of the users through the location mapping technology using Wi-Fi and 3d camera of the smartphones. We applied the signal amplitude of access point using wireless lan, to develop inside location tracking system with lower price. AP is cheaper than other devices used in other tracking techniques, and by installing the software to the user's mobile device it can be directly used as the tracking system device. We used the Microsoft Kinect sensor for the 3D Camera. Kinect is equippedwith the function discriminating the depth and human information inside the shooting area. Therefore it is appropriate to extract user's body, vector, and acceleration information with low price. We confirm the location of the audience using the cell ID obtained from the Wi-Fi signal. By using smartphones as the basic device for the location service, we solve the problems of additional tagging device and provide environment that multiple users can get the interaction service simultaneously. 3d cameras located at each cell areas get the exact location and status information of the users. The 3d cameras are connected to the Camera Client, calculate the mapping information aligned to each cells, get the exact information of the users, and get the status and pattern information of the audience. The location mapping technique of Camera Client decreases the error rate that occurs on the inside location service, increases accuracy of individual discrimination in the area through the individual discrimination based on body information, and establishes the foundation of the multimodal interaction technology at the exhibition. Calculated data and information enables the users to get the appropriate interaction service through the main server.

• Journal of Korea Water Resources Association
• /
• v.56 no.3
• /
• pp.195-210
• /
• 2023

Additional deposition and erosion in large rivers in South Korea have continued to occur toward morphological stabilization after massive dredging through the four major river restoration project, subsequently requiring precise bathymetry monitoring. Hyperspectral bathymetry method has increasingly been highlighted as an alternative way to estimate bathymetry with high spatial resolution in shallow depth for replacing classical intrusive direct measurement techniques. This study introduced the conventional Optimal Band Ratio Analysis (OBRA) of hyperspectral bathymetry method, and evaluated the performance in a domestic large river in normal turbid and flow condition. Maximum measurable depth was estimated by applying correlation coefficient and root mean square error (RMSE) produced during OBRA with cascadedly applying cut-off depth, where the consequent hyperspectral bathymetry map excluded the region over the derived maximum measurable depth. Also non-linearity was considered in building relation between optimal band and depth. We applied the method to the Nakdong and Hwang River confluence as a large river case and obtained the following features. First, the hyperspectal method showed acceptable performance in morphological mapping for shallow regions, where the maximum measurable depth was 2.5 m and 1.25 m in the Nakdong and Hwang river, respectively. Second, RMSE was more feasible to derive the maximum measurable depth rather than the conventional correlation coefficient whereby considering various scenario of excluding range of in situ depths for OBRA. Third, highly turbid region in Hwang River did not allow hyperspectral bathymetry mapping compared with the case of adjacent Nakdong River, where maximum measurable depth was down to half in Hwang River.

• Investigative Magnetic Resonance Imaging
• /
• v.18 no.4
• /
• pp.303-313
• /
• 2014

Purpose : In-vivo conductivity reconstruction using transmit field ($B_1{^+}$) information of MRI was proposed. We assessed the accuracy of conductivity reconstruction in the presence of statistical noise in complex $B_1{^+}$ map and provided a parametric model of the conductivity-to-noise ratio value. Materials and Methods: The $B_1{^+}$ distribution was simulated for a cylindrical phantom model. By adding complex Gaussian noise to the simulated $B_1{^+}$ map, quantitative conductivity estimation error was evaluated. The quantitative evaluation process was repeated over several different parameters such as Larmor frequency, object radius and SNR of $B_1{^+}$ map. A parametric model for the conductivity-to-noise ratio was developed according to these various parameters. Results: According to the simulation results, conductivity estimation is more sensitive to statistical noise in $B_1{^+}$ phase than to noise in $B_1{^+}$ magnitude. The conductivity estimate of the object of interest does not depend on the external object surrounding it. The conductivity-to-noise ratio is proportional to the signal-to-noise ratio of the $B_1{^+}$ map, Larmor frequency, the conductivity value itself and the number of averaged pixels. To estimate accurate conductivity value of the targeted tissue, SNR of $B_1{^+}$ map and adequate filtering size have to be taken into account for conductivity reconstruction process. In addition, the simulation result was verified at 3T conventional MRI scanner. Conclusion: Through all these relationships, quantitative conductivity estimation error due to statistical noise in $B_1{^+}$ map is modeled. By using this model, further issues regarding filtering and reconstruction algorithms can be investigated for MREPT.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
• /
• v.21 no.1
• /
• pp.1-10
• /
• 2016

Currently available surface seawater partial pressure carbon dioxide ($pCO_2$) data sets in the East Sea are not enough to quantify statistically the carbon dioxide flux through the air-sea interface. To complement the scarcity of the $pCO_2$ measurements, we construct a neural network (NN) model based on satellite data to map $pCO_2$ for the areas, which were not observed. The NN model is constructed for the Ulleung Basin, where $pCO_2$ data are best available, to map and estimate the variability of $pCO_2$ based on in situ $pCO_2$ for the years from 2003 to 2012, and the sea surface temperature (SST) and chlorophyll data from the MODIS (Moderate-resolution Imaging Spectroradiometer) sensor of the Aqua satellite along with geographic information. The NN model was trained to achieve higher than 95% of a correlation between in situ and predicted $pCO_2$ values. The RMSE (root mean square error) of the NN model output was $19.2{\mu}atm$ and much less than the variability of in situ $pCO_2$. The variability of $pCO_2$ with respect to SST and chlorophyll shows a strong negative correlation with SST than chlorophyll. As SST decreases the variability of $pCO_2$ increases. When SST is lower than $15^{\circ}C$, $pCO_2$ variability is clearly affected by both SST and chlorophyll. In contrast when SST is higher than $15^{\circ}C$, the variability of $pCO_2$ is less sensitive to changes in SST and chlorophyll. The mean rate of the annual $pCO_2$ increase estimated by the NN model output in the Ulleung Basin is $0.8{\mu}atm\;yr^{-1}$ from 2003 to 2014. As NN model can successfully map $pCO_2$ data for the whole study area with a higher resolution and less RMSE compared to the previous studies, the NN model can be a potentially useful tool for the understanding of the carbon cycle in the East Sea, where accessibility is limited by the international affairs.