• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.13 no.6
• /
• pp.461-469
• /
• 2020

In this paper, we propose a PCA-based feature feedback method for extracting important areas of handwritten numeric data sets and face data sets. A PCA-based feature feedback method is proposed by extending the previous LDA-based feature feedback method. In the proposed method, the data is reduced to important feature dimensions by applying the PCA technique, one of the dimension reduction machine learning algorithms. Through the weights derived during the dimensional reduction process, the important points of data in each reduced dimensional axis are identified. Each dimension axis has a different weight in the total data according to the size of the eigenvalue of the axis. Accordingly, a weight proportional to the size of the eigenvalues of each dimension axis is given, and an operation process is performed to add important points of data in each dimension axis. The critical area of the data is calculated by applying a threshold to the data obtained through the calculation process. After that, induces reverse mapping to the original data in the important area of the derived data, and selects the important area in the original data space. The results of the experiment on the MNIST dataset are checked, and the effectiveness and possibility of the pattern recognition method based on PCA-based feature feedback are verified by comparing the results with the existing LDA-based feature feedback method.

• Phonetics and Speech Sciences
• /
• v.15 no.4
• /
• pp.91-100
• /
• 2023

Reverberation time (T60) is a typical acoustic parameter that provides information about reverberation. Since the impacts of reverberation vary depending on the frequency bands even in the same space, frequency-dependent (FD) T60, which offers detailed insights into the acoustic environments, can be useful. However, most conventional blind T60 estimation methods, which estimate the T60 from speech signals, focus on fullband T60 estimation, and a few blind FDT60 estimation methods commonly show poor performance in the low-frequency bands. This paper introduces a modified approach based on Attentive pooling based Weighted Sum of Spectral Decay Rates (AWSSDR), previously proposed for blind T60 estimation, by extending its target from fullband T60 to FDT60. The experimental results show that the proposed method outperforms conventional blind FDT60 estimation methods on the acoustic characterization of environments (ACE) challenge evaluation dataset. Notably, it consistently exhibits excellent estimation performance in all frequency bands. This demonstrates that the mechanism of the AWSSDR method is valuable for blind FDT60 estimation because it reflects the FD variations in the impact of reverberation, aggregating information about FDT60 from the speech signal by processing the spectral decay rates associated with the physical properties of reverberation in each frequency band.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.17 no.1
• /
• pp.31-37
• /
• 2024

In this study, we propose a machine vision-based pedestrian signal detection algorithm that operates efficiently even in computing resource-constrained environments. This algorithm demonstrates high efficiency within limited resources and is designed to minimize the impact of ambient lighting by sequentially applying HSV color space-based image processing, binarization, morphological operations, labeling, and other steps to address issues such as light glare. Particularly, this algorithm is structured in a relatively simple form to ensure smooth operation within embedded system environments, considering the limitations of computing resources. Consequently, it possesses a structure that operates reliably even in environments with low computing resources. Moreover, the proposed pedestrian signal system not only includes pedestrian signal detection capabilities but also incorporates IoT functionality, allowing wireless integration with a web server. This integration enables users to conveniently monitor and control the status of the signal system through the web server. Additionally, successful implementation has been achieved for effectively controlling 50W LED pedestrian signals. This proposed system aims to provide a rapid and efficient pedestrian signal detection and control system within resource-constrained environments, contemplating its potential applicability in real-world road scenarios. Anticipated contributions include fostering the establishment of safer and more intelligent traffic systems.

• Journal of Sensor Science and Technology
• /
• v.33 no.1
• /
• pp.34-39
• /
• 2024

Energy harvesting technology that converts the wasted energy resources into electrical energy is emerging as a semipermanent power source for self-powered electronics and wireless low-power sensor systems. Among the various energy conversion techniques, flexible piezoelectric energy harvesters (f-PEHs), using materials with piezoelectric effects, have attracted significant interest because they can harvest a small mechanical energy into electrical signals without constraints of time and space in various environments. In this study, we used a flexible piezoelectric composite film fabricated by dispersing BaHf_xTi_(1-x)O₃ (x = 0, 0.01, 0.05, 0.1) piezoelectric powders inside a polymeric matrix to facilitate f-PEHs. The fabricated f-PEH with optimal Hf contents (x = 0.05) generated a maximum output voltage of 0.95 V and current signal of 130 nA with stable electrical/mechanical disabilities under periodically bending deformations. In addition, we demonstrated a cantilever-type f-PEH and investigated its potential as a sensor by characterizing the output performance under mechanical vibrations at various frequencies. This study provides the breakthrough for realizing self-powered energy harvesting and sensing systems by adopting the lead-free piezoelectric composites under vibrational environments.

• Journal of Korean Society of Forest Science
• /
• v.88 no.4
• /
• pp.510-522
• /
• 1999

The air pollutants ; $SO_2$, $SO{_4}^{-2}$, $NO{_3}^-$, $Cl^-$ are absorbed into soils through falling with dusts and rain from the atmosphere. The sources of heavy metal contaminants in the environments are agricultural and horticultural materials, sewage sludges, fossil fuel combustion, metallurgical industries, electronics and waste disposal etc.. The soils and hydrosphere can be polluted on the way of the circulation of these heavy metals. Studied pollutant anions are $SO{_4}^{-2}$, $NO{_3}^-$ and $Cl^-$ and heavy metals are Se, Mo, Zn, Cd, Pb, Mn, Cr, Co, V, As, Cu and Ni which are the elements to be concerned with the essentials for plants, with animal and human health. This study is with the aim of selecting the species of roadside trees and green space trees which have excellent absorption of air pollutants and heavy metals from the atmosphere and the soils in the urban area. Two areas are designated to carry out this study : urban area ; Kwangju city and rural area ; the yard of Forest Environment Institute of Chollanam-do, at Sanje-ri, Sampo-myum, Naju city, Chollanam-do (23km away from Kwangju). This study is carried out to understand the movement of anions and heavy metals from the soils to the trees in both areas, the absorption of anions and heavy metals from atmosphere into leaves and the amounts of anions and heavy metals in leaves and fine roots(< 1mm dia.) of roadside trees and green space trees in Kwangju and trees in the yard of Forest Environment Institute of Chollanam-do. The tree species selected for this study in both areas are Ginkgo biloba, Quercus acutissima, Cedrus deodara, Platanus occidentalis, Robinia pseudoacacia, Alnus japonica. Metasequoia glyptostroboides. Zekova serrata. Prunus serrulata var. spontanea, and Pinus densiflora. The results of the study are as follows : 1. $SO{_4}^{-2}$, $NO{_3}^-$ and $Cl^-$ concentrations are higher in the soils of the urban area than in those of the rural area, and $NO{_3}^-$ and $SO{_4}^{-2}$ are higher in the leaves than in the roots due to the absorption of the these pollutants through the stomata. 2. Ginkgo biloba, Robinia pseudoacacia. Zekova serrata, Quercus acutissima, and Platanus occidentalis can be adequated to the roadside trees and the environmental trees due to their good absorption of $NO{_3}^-$ and $SO{_4}^{-2}$. 3. Heavy metals in the soils of both areas are in the order of Mn > Zn > V > Cr > Pb > Ni > Cu > Mo> Cd, and in the leaves and roots of the trees in the both areas are in the order of Mn>Zn>Cr>Cu>V>Ni. Both orders are similar ones except V. There are more in the urban soils than in the rural soils in amount of Mn, Zn, Pb, V, Cu. 4. It is supposed that there is no antagonism between Mn and Zn in this study. 5. Se, Co and As are not detected in the soils, the leaves and the roots in both areas. Sn, Mo, Cd and Pb are also not detected in the leaves and roots in spite of considerable amount in the soils of both areas.

• Journal of Internet Computing and Services
• /
• v.16 no.2
• /
• pp.49-55
• /
• 2015

According to the statistics of traffic accidents over recent 5 years, traffic accidents during the night times happened more than the day times. There are various causes to occur traffic accidents and the one of the major causes is inappropriate or missing street lights that make driver's sight confused and causes the traffic accidents. In this paper, with smartphones, we designed and implemented a lane luminance measurement application which stores the information of driver's location, driving, and lane luminance into database in real time to figure out the inappropriate street light facilities and the area that does not have any street lights. This application is implemented under Native C/C++ environment using android NDK and it improves the operation speed than code written in Java or other languages. To measure the luminance of road, the input image with RGB color space is converted to image with YCbCr color space and Y value returns the luminance of road. The application detects the road lane and calculates the road lane luminance into the database sever. Also this application receives the road video image using smart phone's camera and improves the computational cost by allocating the ROI(Region of interest) of input images. The ROI of image is converted to Grayscale image and then applied the canny edge detector to extract the outline of lanes. After that, we applied hough line transform method to achieve the candidated lane group. The both sides of lane is selected by lane detection algorithm that utilizes the gradient of candidated lanes. When the both lanes of road are detected, we set up a triangle area with a height 20 pixels down from intersection of lanes and the luminance of road is estimated from this triangle area. Y value is calculated from the extracted each R, G, B value of pixels in the triangle. The average Y value of pixels is ranged between from 0 to 100 value to inform a luminance of road and each pixel values are represented with color between black and green. We store car location using smartphone's GPS sensor into the database server after analyzing the road lane video image with luminance of road about 60 meters ahead by wireless communication every 10 minutes. We expect that those collected road luminance information can warn drivers about safe driving or effectively improve the renovation plans of road luminance management.

• Journal of the Institute of Electronics Engineers of Korea TC
• /
• v.49 no.1
• /
• pp.31-46
• /
• 2012

The most critical technology to implement ubiquitous health care is Ubiquitous Sensor Network (USN) technology which makes use of various sensor technologies, processor integration technology, and wireless network technology-Radio Frequency Identification (RFID) and Wireless Sensor Network (WSN)-to easily gather and monitor actual physical environment information from a remote site. With the feature, the USN technology can make the information technology of the existing virtual space expanded to actual environments. However, although the RFID and the WSN have technical similarities and mutual effects, they have been recognized to be studied separately, and sufficient studies have not been conducted on the technical integration of the RFID and the WSN. Therefore, EPCglobal which realized the issue proposed the EPC Sensor Network to efficiently integrate and interoperate the RFID and WSN technologies based on the international standard EPCglobal network. The proposed EPC Sensor Network technology uses the Complex Event Processing method in the middleware to integrate data occurring through the RFID and the WSN in a single environment and to interoperate the events based on the EPCglobal network. However, as the EPC Sensor Network technology continuously performs its operation even in the case that the minimum conditions are not to be met to find complex events in the middleware, its operation cost rises. Moreover, since the technology is based on the EPCglobal network, it can neither perform its operation only for the sake of sensor data, nor connect or interoperate with each information system in which the most important information in the ubiquitous computing environment is saved. Therefore, to address the problems of the existing system, we proposed the design and implementation of USN integration management system. For this, we first proposed an integration system that manages RFID and WSN data based on Session Initiation Protocol (SIP). Secondly, we defined the minimum conditions of the complex events to detect unnecessary complex events in the middleware, and proposed an algorithm that can extract complex events only when the minimum conditions are to be met. To evaluate the performance of the proposed methods we implemented SIP-based integration management system.

• Journal of Broadcast Engineering
• /
• v.18 no.6
• /
• pp.895-910
• /
• 2013

Multi-screen service is a service that consumes more than one media in a number of terminals simultaneously or discriminately. This multi-screen service has become useful due to distribute of smart TV and terminals. Also, in case of hybrid broadcasting environment that is convergence of broadcasting and communication environment, it is able to provide various user experience through contents consumed by multiple screens. In hybrid broadcasting environment, scene composition technology can be used as an element technology for multi-screen service. Using scene composition technology, multiple media can be consumed complexly through the specified presentation time and space. Thus, multi-screen service based on the scene composition technology can provide spatial and temporal control and consumption of multiple media by linkage between the terminals. However, existing scene composition technologies are not able to use easily in hybrid broadcasting because of applicable environmental constraints, the difficulty in applying the various terminal and complexity. For this problems, HTML5 can be considered. HTML5 is expected to be applied in various smart terminals commonly, and provides consumption of diverse media. So, in this paper, it proposes the scene composition and multi-screen service technology based on HTML5 that is expected be used in various smart terminals providing hybrid broadcasting environment. For this, it includes the introduction in terms of HTML5 and multi-screen service, the method of providing information related with scene composition and multi-screen service through the extention of elements and attributes in HTML5, media signaling between terminals and the method of synchronization. In addition, the proposed scene composition and multi-screen service technology based on HTML5 was verified through the implementation and experiment.

• Journal of Intelligence and Information Systems
• /
• v.25 no.3
• /
• pp.239-251
• /
• 2019

Statistical methods such as moving averages, Kalman filtering, exponential smoothing, regression analysis, and ARIMA (autoregressive integrated moving average) have been used for stock market predictions. However, these statistical methods have not produced superior performances. In recent years, machine learning techniques have been widely used in stock market predictions, including artificial neural network, SVM, and genetic algorithm. In particular, a case-based reasoning method, known as k-nearest neighbor is also widely used for stock price prediction. Case based reasoning retrieves several similar cases from previous cases when a new problem occurs, and combines the class labels of similar cases to create a classification for the new problem. However, case based reasoning has some problems. First, case based reasoning has a tendency to search for a fixed number of neighbors in the observation space and always selects the same number of neighbors rather than the best similar neighbors for the target case. So, case based reasoning may have to take into account more cases even when there are fewer cases applicable depending on the subject. Second, case based reasoning may select neighbors that are far away from the target case. Thus, case based reasoning does not guarantee an optimal pseudo-neighborhood for various target cases, and the predictability can be degraded due to a deviation from the desired similar neighbor. This paper examines how the size of learning data affects stock price predictability through k-nearest neighbor and compares the predictability of k-nearest neighbor with the random walk model according to the size of the learning data and the number of neighbors. In this study, Samsung electronics stock prices were predicted by dividing the learning dataset into two types. For the prediction of next day's closing price, we used four variables: opening value, daily high, daily low, and daily close. In the first experiment, data from January 1, 2000 to December 31, 2017 were used for the learning process. In the second experiment, data from January 1, 2015 to December 31, 2017 were used for the learning process. The test data is from January 1, 2018 to August 31, 2018 for both experiments. We compared the performance of k-NN with the random walk model using the two learning dataset. The mean absolute percentage error (MAPE) was 1.3497 for the random walk model and 1.3570 for the k-NN for the first experiment when the learning data was small. However, the mean absolute percentage error (MAPE) for the random walk model was 1.3497 and the k-NN was 1.2928 for the second experiment when the learning data was large. These results show that the prediction power when more learning data are used is higher than when less learning data are used. Also, this paper shows that k-NN generally produces a better predictive power than random walk model for larger learning datasets and does not when the learning dataset is relatively small. Future studies need to consider macroeconomic variables related to stock price forecasting including opening price, low price, high price, and closing price. Also, to produce better results, it is recommended that the k-nearest neighbor needs to find nearest neighbors using the second step filtering method considering fundamental economic variables as well as a sufficient amount of learning data.