• The Journal of the Acoustical Society of Korea
• /
• v.41 no.5
• /
• pp.518-524
• /
• 2022

In this paper, we propose a method for combining convolutional neural networks and attention mechanism to improve the recognition performance of emotional stress from Electroencephalogram (EGG) signals. In the proposed method, EEG signals are decomposed into five frequency domains, and spatial information of EEG features is obtained by applying a convolutional neural network layer to each frequency domain. As a next step, salient frequency information is learned in each frequency band using a gate transformer-based attention mechanism, and complementary frequency information is further learned through inter-frequency mapping to reflect it in the final attention representation. Through an EEG stress recognition experiment involving a DEAP dataset and six subjects, we show that the proposed method is effective in improving EEG-based stress recognition performance compared to the existing methods.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.11
• /
• pp.1649-1654
• /
• 2021

In this paper, we introduce a regularization of long short-term memory (LSTM) based fall detection system using TensorFlow that can detect falls that can occur in the elderly. Fall detection uses data from a 3-axis acceleration sensor attached to the body of an elderly person and learns about a total of 7 behavior patterns, each of which is a pattern that occurs in daily life, and the remaining 3 are patterns for falls. During training, a normalization process is performed to effectively reduce the loss function, and the normalization performs a maximum-minimum normalization for data and a L2 regularization for the loss function. The optimal regularization conditions of LSTM using several falling parameters obtained from the 3-axis accelerometer is explained. When normalization and regularization rate λ for sum vector magnitude (SVM) are 127 and 0.00015, respectively, the best sensitivity, specificity, and accuracy are 98.4, 94.8, and 96.9%, respectively.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.11
• /
• pp.1696-1707
• /
• 2021

With the recent growth of information technology, various literacy such as digital literacy, data literacy, AI literacy is being studied. In this paper, we focus on data visualization literacy as visualization is an essential part of big data analysis and is used in several mobile apps. Visualization Literacy Assessment Test(VLAT) was developed in 2016 and we introduce how the test was developed and modified to a Korean version, K-VLAT. K-VLAT is consisted of 12 visualizations and 53 questions through a website. Additionally, to understand the research trend in visualization literacy we analyzed 81 papers that had cited the VLAT publication. We categorized the research into 4 categories with 11 sub-categories. The area of studies visualization literacy related to was understanding the relation with cognition, expanding the literacy measures, relation with education, utilization for developing user-centric dashboards or using the test to show effectiveness of visualizations. At last, we discuss about different ways to utilize K-VLAT for future research.

• KNOM Review
• /
• v.22 no.3
• /
• pp.25-30
• /
• 2019

For meteorological applications, meaningful results must be derived and provided within time and resource limits. Forecasts through numerous historical data are time-consuming and still have resource limitations in the case of disaster safety-related analyses/predictions such as local typhoon forecasts. Suitable forecasts should be provided without any problems caused by limited physical environmental conditions and when results are to be drawn under time constraints, such as typhoon forecasts and forecast services for flooded areas by road. In this paper, we analyze the application of weather and climate forecasting to provide a suitable forecasting service in both temporal and resource conditions. Through the analysis of execution time according to mesh sizes, it was confirmed that a mesh adjustment can cope with the case of the temporal constraint. In addition, by analyzing the execution time through memory resource control, we confirmed the minimum resource condition that does not affect the performance and the resource usage pattern of the application through the swap and mlock analysis.

• Journal of Information Technology Services
• /
• v.22 no.2
• /
• pp.1-21
• /
• 2023

Due to the convergence and complexity of the 4th Industrial Revolution, the boundaries between industries have become unclear and ambiguous. Consequently, there is a lack of research on how firms engaged in this industry are changing their location behavior. Recently, some attempts to classify the industrial groups of the 4th Industrial Revolution and their detail occupations have been made, and this study adopts the classification of Lee and Jung (2020) of the Korea Institute for Industrial Economics & Trade. In this study, the 18 detailed industries commonly included in multiple industrial groups are defined as 'core industries' and are classified into manufacturing and service industries to explore the spatial patterns of firms' location. Specifically, this study aims to examine how the location behavior of firms in core industries of the 4th Industrial Revolution has changed from 2010 to 2019 in the Seoul metropolitan area, using the 「National Business Survey」 data. We employed two methods based on spatial auto-correlation: (i) spatial kernel density estimation analysis and (ii) local Moran's I_i analysis. The results indicate that the core industry firms form more distinct and larger clusters in 2019 based on the clusters formed in 2010. Specifically, manufacturing industry firms tended to concentrate in the southern region of Gyeonggi and parts of Seoul, while serivce industry firms were more concentrated in Seoul. These core industries play a critical role in industries and are closely related to the ICT industries, which generate high-added value and increase productivity in the front and rear industries. This study reveals that the agglomeration of these industries in specific regions is intensifying and may exacerbate regional inequality.

• The Journal of the Acoustical Society of Korea
• /
• v.42 no.3
• /
• pp.214-220
• /
• 2023

In this paper, we propose a method to improve texture recognition performance from haptic acceleration signals representing the texture characteristics of object surface materials by using a Conformer model that combines the advantages of a convolutional neural network and a transformer. In the proposed method, three-axis acceleration signals generated by impact sound and vibration are combined into one-dimensional acceleration data while a person contacts the surface of the object materials using a tool such as a stylus , and the logarithmic Mel-spectrogram is extracted from the haptic acceleration signal similar to the audio signal. Then, Conformer is applied to the extracted the logarithmic Mel-spectrogram to learn main local and global frequency features in recognizing the texture of various object materials. Experiments on the Lehrstuhl für Medientechnik (LMT) haptic texture dataset consisting of 60 materials to evaluate the performance of the proposed model showed that the proposed method can effectively recognize the texture of the object surface material better than the existing methods.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.11
• /
• pp.1729-1740
• /
• 2022

Recently, a lot of data has been generated according to the diversity and utilization of information, and the importance of big data analysis to collect, store, process and predict data has increased, and the ability to collect only necessary information is required. More than half of the web space consists of text, and a lot of data is generated through the organic interaction of users. There is a crawling technique as a representative method for collecting text data, but many crawlers are being developed that do not consider web servers or administrators because they focus on methods that can obtain data. In this paper, we design and implement an improved dynamic web crawler that can efficiently fetch data by examining problems that may occur during the crawling process and precautions to be considered. The crawler, which improved the problems of the existing crawler, was designed as a multi-process, and the work time was reduced by 4 times on average.

• Information Systems Review
• /
• v.25 no.4
• /
• pp.205-231
• /
• 2023

The Information Security Management System (ISMS) is a protection procedure and process that keeps information assets confidential, flawless, and available at any time. ISMS-P in Korea and ISO/IEC 27001 overseas are the most representative ISMS certification systems. In this paper, in order to understand the relationship between ISMS certification and organizational characteristics, data were collected from Korea Internet & Security Agency (KISA), Ministry of Science and ICT, Information Security Disclosure System (ISDS), Financial Supervisory Service, Data Analysis, Retrieval and Transfer System (DART), and probit regression analysis was performed. In the probit analysis, the relationship with four independent variables was confirmed for three cases: ISMS-P acquisition, ISO/IEC 27001 acquisition, and both ISMS-P and ISO/IEC 27001 acquisition. As a result of the analysis, it was found that companies that acquired both ISMS-P and ISO/IEC 27001 had a positive correlation with the total number of employees and a negative correlation with business history. In addition, the improvement direction of the ISMS-P certification system and information security disclosure system could also be confirmed.

• Journal of the Society of Disaster Information
• /
• v.19 no.4
• /
• pp.968-975
• /
• 2023

Purpose: We would like to confirm that the false positive rate of flames/smoke is high when detecting fires. Propose a method and dataset to recognize and classify fire situations to reduce the false detection rate. Method: Using the video as learning data, the characteristics of the fire situation were extracted and applied to the classification model. For evaluation, the model performance of Yolov8 and Slowfast were compared and analyzed using the fire dataset conducted by the National Information Society Agency (NIA). Result: YOLO's detection performance varies sensitively depending on the influence of the background, and it was unable to properly detect fires even when the fire scale was too large or too small. Since SlowFast learns the time axis of the video, we confirmed that detects fire excellently even in situations where the shape of an atypical object cannot be clearly inferred because the surrounding area is blurry or bright. Conclusion: It was confirmed that the fire detection rate was more appropriate when using a video-based artificial intelligence detection model rather than using image data.

• Journal of Korean Society of Rural Planning
• /
• v.29 no.4
• /
• pp.137-152
• /
• 2023

Data is indispensable for digital transformation of agriculture with the development of innovative information and communication technology (ICT). In order to devise and prioritize strategies for enhancing data competitiveness in the agricultural sector, we employed an Analytic Hierarchy Process (AHP) analysis. Drawing from existing research on data competitiveness indicators, we developed a three-tier decision-making structure reflecting unique characteristics of the agricultural sector such as farmers'awareness of the data industry or awareness of agriculture among data workers. AHP survey was administered to experts from both agricultural and non-agricultural sectors with a high understanding of data. The overall composite importance, derived from the respondents, was rated in the following order: 'Employment Support', 'Data Standardization', 'R&D Support', 'Start-up Ecosystem Support', 'Relaxation of Regulations', 'Legislation', and 'Data Analytics and Utilization Technology'. In the case of experts in the agricultural sector, 'Employment Support' was ranked as the top priorities, and 'Legislation', 'Undergrad and Grad Education', and 'In-house Training' were also regarded as highly important. On the other hand, experts in the non-agricultural sector perceived 'Data Standardization' and 'Relaxation of Regulations' as the top two priorities, and 'Data Center' and 'Open Public Data' were also highly rated.