• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.11
• /
• pp.3950-3969
• /
• 2021

Radio Frequency (RF)-based indoor localization technologies play significant roles in various Internet of Things (IoT) services (e.g., location-based service). Most such technologies require that all the devices comply with a specified technology (e.g., WiFi, ZigBee, and Bluetooth). However, this requirement limits its application scenarios in today's IoT context where multiple devices complied with different standards coexist in a shared environment. To bridge the gap, in this paper, we propose a cross-technology localization approach, which is able to localize target nodes using a different type of devices. Specifically, the proposed framework reuses the existing WiFi infrastructure without introducing additional cost to localize Non-WiFi device (i.e., ZigBee). The key idea is to leverage the interference between devices that share the same operating frequency (e.g., 2.4GHz). Such interference exhibits unique patterns that depend on the target device's location, thus it can be leveraged for cross-technology localization. The proposed framework uses Principal Components Analysis (PCA) to extract salient features of the received WiFi signals, and leverages Dynamic Time Warping (DTW), Gradient Boosting Regression Tree (GBRT) to improve the robustness of our system. We conduct experiments in real scenario and investigate the impact of different factors. Experimental results show that the average localization accuracy of our prototype can reach 1.54m, which demonstrates a promising direction of building cross-technology technologies to fulfill the needs of modern IoT context.

• Journal of the Korea Society of Computer and Information
• /
• v.14 no.12
• /
• pp.75-87
• /
• 2009

This paper, which is about the method of creating the audio fingerprint and comparing with the audio data, presents how to distinguish music using the characteristics of audio data. It is a process of applying the Difference of Gaussian (DoG: generally used for recognizing images) to the audio data, and to extract the music that changes radically, and to define the location of fingerprint. This fingerprint is made insensitive to the changes of sound, and is possible to extract the same location of original fingerprint with just a portion of music data. By reducing the data and calculation of fingerprint, this system indicates more efficiency than the pre-system which uses pre-frequency domain. Adopting this, it is possible to indicate the copyrighted music distributed in internet, or meta information of music to users.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.11 no.4
• /
• pp.1342-1347
• /
• 2010

Various researches have been studied on WSN(wireless sensor network) for barbed wire entanglements surveillance applications such as industry facilities, security area, prison, military area, airport, etc. Currently, barbed wire entanglements surveillance is formed wire sensor network environment. Traditional wire sensor network guarantee high data transmission rate. Therefore, wire sensor network use fast fourier transform of data of high transmission rate for extraction of feature parameter. However, wireless sensor network in comparison with wire sensor network has very low data transmission rate. Therefore, wireless sensor network doesn't use fast fourier transform of wire sensor network for extraction of feature parameter. In this paper, proposed method use 1 level approximation coefficient of DTW(dynamic time-warped) algorithms based on DWT(discrete wavelet transform) for extraction of detection feature parameter and classification feature parameter for barbed wire entanglements surveillance. l level approximation coefficient have time information and frequency information of signal. Therefore, Dynamic time-warped algorithms based on discrete wavelet transform improve detection and classification of target rather than using energy of signal.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.20 no.2
• /
• pp.13-20
• /
• 2020

One of the major issues surrounding big data is the availability of massive time-based or telemetry data. Now, the appearance of low cost capture and storage devices has become possible to get very detailed time data to be used for further analysis. Thus, we can use these time data to get more knowledge about the underlying system or to predict future events with higher accuracy. In particular, it is very important to define custom tailored contract offers for many households and businesses having smart meter records and predict the future electricity usage to protect the electricity companies from power shortage or power surplus. It is required to identify a few groups with common electricity behavior to make it worth the creation of customized contract offers. This study suggests big data transformation as a side effect and clustering technique to understand the electricity usage pattern by using the open data related to smart meter and KNIME which is an open source platform for data analytics, providing a user-friendly graphical workbench for the entire analysis process. While the big data components are not open source, they are also available for a trial if required. After importing, cleaning and transforming the smart meter big data, it is possible to interpret each meter data in terms of electricity usage behavior through a dynamic time warping method.

• Proceedings of the Korean Vacuum Society Conference
• /
• 2012.02a
• /
• pp.239-240
• /
• 2012

With advancements in semiconductor device technologies, manufacturing processes are getting more complex and it became more difficult to maintain tighter process control. As the number of processing step increased for fabricating complex chip structure, potential fault inducing factors are prevail and their allowable margins are continuously reduced. Therefore, one of the key to success in semiconductor manufacturing is highly accurate and fast fault detection and classification at each stage to reduce any undesired variation and identify the cause of the fault. Sensors in the equipment are used to monitor the state of the process. The idea is that whenever there is a fault in the process, it appears as some variation in the output from any of the sensors monitoring the process. These sensors may refer to information about pressure, RF power or gas flow and etc. in the equipment. By relating the data from these sensors to the process condition, any abnormality in the process can be identified, but it still holds some degree of certainty. Our hypothesis in this research is to capture the features of equipment condition data from healthy process library. We can use the health data as a reference for upcoming processes and this is made possible by mathematically modeling of the acquired data. In this work we demonstrate the use of recurrent neural network (RNN) has been used. RNN is a dynamic neural network that makes the output as a function of previous inputs. In our case we have etch equipment tool set data, consisting of 22 parameters and 9 runs. This data was first synchronized using the Dynamic Time Warping (DTW) algorithm. The synchronized data from the sensors in the form of time series is then provided to RNN which trains and restructures itself according to the input and then predicts a value, one step ahead in time, which depends on the past values of data. Eight runs of process data were used to train the network, while in order to check the performance of the network, one run was used as a test input. Next, a mean squared error based probability generating function was used to assign probability of fault in each parameter by comparing the predicted and actual values of the data. In the future we will make use of the Bayesian Networks to classify the detected faults. Bayesian Networks use directed acyclic graphs that relate different parameters through their conditional dependencies in order to find inference among them. The relationships between parameters from the data will be used to generate the structure of Bayesian Network and then posterior probability of different faults will be calculated using inference algorithms.

• Proceedings of the Korean Vacuum Society Conference
• /
• 2012.02a
• /
• pp.241-241
• /
• 2012

Semiconductor industry has been taking the advantage of improvements in process technology in order to maintain reduced device geometries and stringent performance specifications. This results in semiconductor manufacturing processes became hundreds in sequence, it is continuously expected to be increased. This may in turn reduce the yield. With a large amount of investment at stake, this motivates tighter process control and fault diagnosis. The continuous improvement in semiconductor industry demands advancements in process control and monitoring to the same degree. Any fault in the process must be detected and classified with a high degree of precision, and it is desired to be diagnosed if possible. The detected abnormality in the system is then classified to locate the source of the variation. The performance of a fault detection system is directly reflected in the yield. Therefore a highly capable fault detection system is always desirable. In this research, time series modeling of the data from an etch equipment has been investigated for the ultimate purpose of fault diagnosis. The tool data consisted of number of different parameters each being recorded at fixed time points. As the data had been collected for a number of runs, it was not synchronized due to variable delays and offsets in data acquisition system and networks. The data was then synchronized using a variant of Dynamic Time Warping (DTW) algorithm. The AutoRegressive Integrated Moving Average (ARIMA) model was then applied on the synchronized data. The ARIMA model combines both the Autoregressive model and the Moving Average model to relate the present value of the time series to its past values. As the new values of parameters are received from the equipment, the model uses them and the previous ones to provide predictions of one step ahead for each parameter. The statistical comparison of these predictions with the actual values, gives us the each parameter's probability of fault, at each time point and (once a run gets finished) for each run. This work will be extended by applying a suitable probability generating function and combining the probabilities of different parameters using Dempster-Shafer Theory (DST). DST provides a way to combine evidence that is available from different sources and gives a joint degree of belief in a hypothesis. This will give us a combined belief of fault in the process with a high precision.

• Proceedings of the Korean Vacuum Society Conference
• /
• 2012.02a
• /
• pp.433-433
• /
• 2012

As the requirement in patterning geometry continuously shrinks down, the termination of etch process at the exact time became crucial for the success in nano patterning technology. By virtue of real-time optical emission spectroscopy (OES), etch end point detection (EPD) technique continuously develops; however, it also faced with difficulty in low open ratio etching, typically in self aligned contact (SAC) and one cylinder contact (OCS), because of very small amount of optical emission from by-product gas species in the bulk plasma glow discharge. In developing etching process, one may observe that coupon test is being performed. It consumes costs and time for preparing the patterned sample wafers every test in priority, so the coupon wafer test instead of the whole patterned wafer is beneficial for testing and developing etch process condition. We also can observe that etch open area is varied with the number of coupons on a dummy wafer. However, this can be a misleading in OES study. If the coupon wafer test are monitored using OES, we can conjecture the endpoint by experienced method, but considering by data, the materials for residual area by being etched open area are needed to consider. In this research, we compare and analysis the OES data for coupon wafer test results for monitoring about the conditions that the areas except the patterns on the coupon wafers for real-time process monitoring. In this research, we compared two cases, first one is etching the coupon wafers attached on the carrier wafer that is covered by the photoresist, and other case is etching the coupon wafers on the chuck. For comparing the emission intensity, we chose the four chemical species (SiF2, N2, CO, CN), and for comparing the etched profile, measured by scanning electron microscope (SEM). In addition, we adopted the Dynamic Time Warping (DTW) algorithm for analyzing the chose OES data patterns, and analysis the covariance and coefficient for statistical method. After the result, coupon wafers are over-etched for without carrier wafer groups, while with carrier wafer groups are under-etched. And the CN emission intensity has significant difference compare with OES raw data. Based on these results, it necessary to reasonable analysis of the OES data to adopt the pre-data processing and algorithms, and the result will influence the reliability for relation of coupon wafer test and whole wafer test.

• The Journal of Engineering Geology
• /
• v.22 no.2
• /
• pp.195-205
• /
• 2012

We evaluated the results of pumping tests, the amount of groundwater used by Protected Cultivation with Water Curtain (PCWC), and monthly depth to water table (DTW) at the Wangjeon-ri area, Nonsan City, to elucidate the cause of a decrease in pumping rate during the winter PCWC season. The transmissivity and storage coefficient at eight sites where the major aquifer is alluvium, vary from 119.9 to $388.1m^2/d$ and $1.5{\times}10^{-4}$ to $5.5{\times}10^{-4}$, respectively. The pumping rate for PCWC during three months (Dec. to Feb.) averaged about $8,100m^3/d$ and the maximum water level in the area varied by about 10 m. Groundwater levels had fully recovered by August-five months after pumping for PCWC had ceased. These observations indicate that the pumping rate during the winter PCWC season was excessive compared with groundwater productivity in the area. Groundwater level in the central PCWC area varied from -3.0 to 4.38 m, exceeding the water level of the Nosung Stream for only three months (Aug. to Oct.). This result indicates that Nosung Stream recharges the area during the period from November to July. To solve the problem of reduced pumping rate during the winter PCWC season, it would be necessary to reduce the amount of groundwater used for PCWC or to develop an artificial recharge system using recycled groundwater.

• The Korean Journal of Applied Statistics
• /
• v.33 no.6
• /
• pp.791-804
• /
• 2020

Residential electricity consumption can be predicted more accurately by utilizing the realtime household electricity consumption reference that can be collected by the AMI as the ICT developed under the smart grid circumstance. This paper studied the model that predicts residential power load using the ARIMA, TBATS, NNAR model based on the data of hour unit amount of household electricity consumption, and unlike forecasting the consumption of the whole households at once, it computed the anticipated amount of the electricity consumption by aggregating the predictive value of each established model of cluster that was collected by the households which show the similiar load profile. Especially, as the typical time series data, the electricity consumption data chose the clustering analysis method that is appropriate to the time series data. Therefore, Dynamic Time Warping and Periodogram based method is used in this paper. By the result, forecasting the residential elecrtricity consumption by clustering the similiar household showed better performance than forecasting at once and in summertime, NNAR model performed best, and in wintertime, it was TBATS model. Lastly, clustering method showed most improvements in forecasting capability when the DTW method that was manifested the difference between the patterns of each cluster was used.

• Korean Journal of Ecology and Environment
• /
• v.54 no.4
• /
• pp.390-397
• /
• 2021

The time series data of rotifer community focusing on the species number and total density were collected from 29 reservoirs located at Jeonnam Province from 2008 to 2016 quarterly. The reservoirs had similar weather condition during the study period, but their sizes and water qualities were different. To analyze the temporal dynamics of rotifer community, the medians, ranges, outliers and coefficient of variation (CV) value of rotifer species number and abundance were compared. For the temporal trend analysis, time series of each reservoir data were compared and clustered using the dynamic time warping function of the R package "dtwclust". Small-sized reservoirs showed higher variability in rotifer abundance with more frequent outliers than large-sized reservoirs. On the other hand, apparent pattern was not observed for the rotifer species number. For the temporal pattern of rotifer density, COD, phytoplankton abundance fluctuation, and cladoceran abundance fluctuation have been suggested as potential factor affecting the rotifer abundance dynamics.