• International conference on construction engineering and project management
• /
• 2020.12a
• /
• pp.216-222
• /
• 2020

This research evaluated the construction market and project environment of nine nations within the ASEAN members. Quantitative data from global consulting firms and international organizations were identified and normalized for evaluation. The result of the analysis was that Indonesia was ranked highest for construction market growth while Singapore was ranked highest for stability of project environment. The research results can be utilized by construction companies that are planning on entering the construction market within the ASEAN members.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.17 no.6
• /
• pp.14-24
• /
• 2018

Pedestrian movement of passengers using smart card within stations can be divided into three types of activities - straight ride and alight, line transfer, and station transfer. Straight ride and alight is transfer activity for which the card terminal and embarking line are identical. In this case, straight ride occurs at the origin station and straight alight occurs at the destination station. Line transfer refers to activity in which the subway line embarked on by the passenger is different from that which is disembarked. Succinctly, line transfer is transfer at a middle station, rather than at origin or destination stations. Station transfer occurs when the card terminal line and embarking line are different. It appears when station transfer happens at the origin station as starting transfer, and at the destination station as destination transfer. In the case of Metropolitan smart card data, origin and destination station card terminal line number data is recorded, but subway line data does not exist. Consequently, transportation card data, as it exists, cannot adequately be used to analyze pedestrian movement as a whole in subway stations. This research uses the smart card data, with its constraints, to propose an analysis model for passenger pedestrian movement within subway stations. To achieve this, a path selection model is constructed, which links origin and destination stations, and then applied for analysis. Finally, a case study of the metropolitan subway is undertaken and pedestrian volume analyzed.

• Korea Trade Review
• /
• v.47 no.3
• /
• pp.57-73
• /
• 2022

The process and change of convergence in the economy and industry with the development of digital technology and combining with new technologies is called Digital Transformation. Specifically, it refers to innovating existing businesses and services by utilizing information and communication technologies such as big data analysis, Internet of Things, cloud computing, and artificial intelligence. Digital transformation is changing the shape of business and has a wide impact on businesses and consumers in all industries. Among them, the big data and analytics market is emerging as one of the most important growth drivers of digital transformation. Integrating intelligent data into an existing business is one of the key tasks of digital transformation, and it is important to collect and monitor data and learn from the collected data in order to efficiently operate a data-based business. In developed countries overseas, research on new business models using various data accumulated at the level of government and private companies is being actively conducted. However, although the trade and import/export data collected in the domestic public sector is being accumulated in various types and ranges, the establishment of an analysis and utilization model is still in its infancy. Currently, we are living in an era of massive amounts of big data. We intend to discuss the value of trade big data possessed from the past to the present, and suggest a strategy to activate trade big data for trade digital transformation and a new direction for future trade big data research.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.16 no.6
• /
• pp.1-7
• /
• 2016

As new information and communication technologies evolve, security threats are also becoming increasingly intelligent and advanced. In this paper, we analyze the time series data continuously entered through a series of periods from the network device or lightweight IoT (Internet of Things) devices by using the statistical technique and propose a system to detect abnormal behaviors of the device or abnormality based on the analysis results. The proposed system performs the first level abnormal detection by using previously entered data set, thereafter performs the second level anomaly detection according to the trust bound configured by using stored time series data based on time attribute or group attribute. Multi-level analysis is able to improve reliability and to reduce false positives as well through a variety of decision data set.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.19 no.2
• /
• pp.473-482
• /
• 2024

This study utilized virtual reality to construct a simulated public restroom environment to identify potential hazards. The objective was to discern actual risks in real-world public restrooms through this virtual setup. During the virtual restroom experience, data from the built-in 3-axis accelerometer and gyroscope sensors of testor's smart phones were collected. Analysis of this data helped in identifying spatio temporal factors impacting the users. The determination of these factors as risk elements was based on an evaluation algorithm grounded in data analysis.

• The KIPS Transactions:PartB
• /
• v.9B no.2
• /
• pp.147-154
• /
• 2002

In bulding Intelligent Agent Societies (IAS), it is very important to design and implement coordination in accordance with the known requirement and anticipated working conditions. Coordination consists of a set of mechanisms necessary for the effective operation of IAS. Currently, there is little theoretical support that could help in this research is to generate an empirically-based solving systems in which all agent share an identical goal structure and fully cooperate. And we developed a simulation model called "P-System" which produces basic data to be used for statistical analysis to generate a theory of coordination. Coordination among agent in the P-System is dependent on 23 control variables calld TEs(tweakable emtities.)And the level of coordination is represennted by an independent variabe called QMC (Quality Measure Coordination) expressed in numerical terms according tn the definiion of this study. Also, we have studied how to select unbiased subset from the huge total experimental space of the P-System and how to decide the scale of the subset.

• Convergence Security Journal
• /
• v.23 no.3
• /
• pp.21-28
• /
• 2023

The development of information and communication technology in the 21st century has increased operational efficiency by providing hyper-connectivity and hyper-intelligence in the control systems of major infrastructure, but is also increasing security vulnerabilities, exposing it to hacking threats. Among them, the electric power system that supplies electric power essential for daily life has become a major target of cyber-attacks as a national critical infrastructure system. Recently, in order to protect these power systems, various security systems have been developed and the stability of the power systems has been maintained through practical cyber battle training. However, as cyber-attacks are combined with advanced ICT technologies such as artificial intelligence and big data, it is not easy to defend cyber-attacks that are becoming more intelligent with existing security systems. In order to defend against such intelligent cyber-attacks, it is necessary to know the types and aspects of intelligent cyber-attacks in advance. In this study, we analyzed the evolution of cyber attacks combined with advanced ICT technology.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.11 no.6
• /
• pp.49-59
• /
• 2012

Recently, various alternatives for safety and efficiency of commercial vehicles have been considered, and one of the new alternatives is the application of a digital tachograph. In Korea, the installation of a digital tachograph to commercial vehicles was regulated from 2011 and Korea Transportation Safety Authority developed e-TAS to analyze the monitoring data from digital tachographs installed in the order of 100 commercial vehicles. This study performs the potential impact analysis of the DTG installation, which includes a trend of dangerous driving, a trend of traffic accidents and cost-effective analysis, a trend of fuel consumption and cost-effective analysis, a cost-effective analysis of social benefits using e-TAS data. Depending on the frequency of dangerous driving, the participants are divided into three groups; high-dangerous group, average-dangerous group and low-dangerous group. The high-dangerous driving group shows lower km/liter than the low-dangerous driving group by 15% for buses and taxis and by 30% for trucks. About $CO_2$ emission, the difference becomes bigger; 25%, 25% and 42% for buses, taxis and trucks, respectively. Although this study is a short-term period analysis, the methodology will be applicable for the long-term period analysis with larger data.

• Journal of Intelligence and Information Systems
• /
• v.17 no.4
• /
• pp.95-108
• /
• 2011

TThis is a study of the personalization method that intelligently adapts the level of clustering considering purchasing index of a customer. In the e-biz era, many companies gather customers' demographic and transactional information such as age, gender, purchasing date and product category. They use this information to predict customer's preferences or purchasing patterns so that they can provide more customized services to their customers. The previous Customer-Segmentation method provides customized services for each customer group. This method clusters a whole customer set into different groups based on their similarity and builds predictive models for the resulting groups. Thus, it can manage the number of predictive models and also provide more data for the customers who do not have enough data to build a good predictive model by using the data of other similar customers. However, this method often fails to provide highly personalized services to each customer, which is especially important to VIP customers. Furthermore, it clusters the customers who already have a considerable amount of data as well as the customers who only have small amount of data, which causes to increase computational cost unnecessarily without significant performance improvement. The other conventional method called 1-to-1 method provides more customized services than the Customer-Segmentation method for each individual customer since the predictive model are built using only the data for the individual customer. This method not only provides highly personalized services but also builds a relatively simple and less costly model that satisfies with each customer. However, the 1-to-1 method has a limitation that it does not produce a good predictive model when a customer has only a few numbers of data. In other words, if a customer has insufficient number of transactional data then the performance rate of this method deteriorate. In order to overcome the limitations of these two conventional methods, we suggested the new method called Intelligent Customer Segmentation method that provides adaptive personalized services according to the customer's purchasing index. The suggested method clusters customers according to their purchasing index, so that the prediction for the less purchasing customers are based on the data in more intensively clustered groups, and for the VIP customers, who already have a considerable amount of data, clustered to a much lesser extent or not clustered at all. The main idea of this method is that applying clustering technique when the number of transactional data of the target customer is less than the predefined criterion data size. In order to find this criterion number, we suggest the algorithm called sliding window correlation analysis in this study. The algorithm purposes to find the transactional data size that the performance of the 1-to-1 method is radically decreased due to the data sparity. After finding this criterion data size, we apply the conventional 1-to-1 method for the customers who have more data than the criterion and apply clustering technique who have less than this amount until they can use at least the predefined criterion amount of data for model building processes. We apply the two conventional methods and the newly suggested method to Neilsen's beverage purchasing data to predict the purchasing amounts of the customers and the purchasing categories. We use two data mining techniques (Support Vector Machine and Linear Regression) and two types of performance measures (MAE and RMSE) in order to predict two dependent variables as aforementioned. The results show that the suggested Intelligent Customer Segmentation method can outperform the conventional 1-to-1 method in many cases and produces the same level of performances compare with the Customer-Segmentation method spending much less computational cost.

• Journal of the Korean Geotechnical Society
• /
• v.37 no.9
• /
• pp.25-36
• /
• 2021

Recently, the intelligent compaction technology for quality control of earthworks has brought attention as a quality control standard for earthworks. In this study, intelligent compaction technology and earthwork quality control methods were investigated and earthwork quality control procedures using intelligent compaction technology were considered based on field tests. Through the field compaction test of the silty sand (SM) fill material, it was confirmed that CMV and bearing capcaity index from plate load tests increased as the number of compactions increased. Based on the field test data, the average CMV and quality control target CMV were derived. The target CMV (34.2) was calculated through the correlation with the bearing capacity index of the plate load test, and the target CMV (36.6) was calculated through the analysis of the CMV increase rate. In this paper, the on-site compaction quality management procedure and methodology using intelligent compaction technology were discussed, and an intelligent compaction quality management method was proposed to promote the applicability of the technology.