• ETRI Journal
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• v.44 no.2
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• pp.220-231
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• 2022

Building smart transportation services in urban cities has become a worldwide problem owing to the rapidly increasing global population and the development of Internet-of-Things applications. Traffic congestion and environmental concerns can be alleviated by sharing mobility, which reduces the number of vehicles on the road network. The taxi-parcel sharing problem has been considered as an efficient planning model for people and goods flows. In this paper, we enhance the functionality of a current people-parcel taxi sharing model. The adapted model analyzes the historical request data and predicts the current service demands. We then propose two novel online routing algorithms that construct optimal routes in real-time. The objectives are to maximize (as far as possible) both the parcel delivery requests and ride requests while minimizing the idle time and travel distance of the taxis. The proposed online routing algorithms are evaluated on instances adapted from real Cabspotting datasets. After implementing our routing algorithms, the total idle travel distance per day was 9.64% to 12.76% lower than that of the existing taxi-parcel sharing method. Our online routing algorithms can be incorporated into an efficient smart shared taxi system.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.4
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• pp.45-52
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• 2023

With the advent of deep learning, Artificial Intelligence (AI) technology has experienced rapid advancements, extending its application across various industrial sectors. However, the focus has shifted from the independent use of AI technology to its dispersion and proliferation through the open AI ecosystem. This shift signifies the transition from a phase of research and development to an era where AI technology is becoming widely accessible to the general public. However, as this dispersion continues, there is an increasing demand for the verification of outcomes derived from AI technologies. Causal AI applies the traditional concept of causal inference to AI, allowing not only the analysis of data correlations but also the derivation of the causes of the results, thereby obtaining the optimal output values. Causal AI technology addresses these limitations by applying the theory of causal inference to machine learning and deep learning to derive the basis of the analysis results. This paper analyzes recent cases of causal AI technology and presents the major tasks and directions of causal AI, extracting patterns between data using the correlation between them and presenting the results of the analysis.

• Journal of Information Technology Services
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• v.19 no.4
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• pp.49-61
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• 2020

Prior studies on technology predictions attempted to predict the emergence and spread of emerging technologies through the analysis of correlations and changes between data using objective data such as patents and research papers. Most of the previous studies predicted future technologies only from the viewpoint of technology development. Therefore, this study intends to conduct technical forecasting from the perspective of the consumer by using keyword search frequency of search portals such as NAVER before and after the introduction of emerging technologies. In this study, we analyzed healthcare technologies into three types : measurement technology, platform technology, and remote service technology. And for the keyword analysis on the healthcare, we converted the classification of technology perspective into the keyword classification of consumer perspective. (Blood pressure and blood sugar, healthcare diagnosis, appointment and prescription, and remote diagnosis and prescription) Naver Trend is used to analyze keyword trends from a consumer perspective. We also used the ARIMA model as a technology prediction model. Analyzing the search frequency (Naver trend) over 44 months, the final ARIMA models that can predict three types of healthcare technology keyword trends were estimated as "ARIMA (1,2,1) (1,0,0)", "ARIMA (0,1,0) (1,0,0)", "ARIMA (1,1,0) (0,0,0)". In addition, it was confirmed that the values predicted by the time series prediction model and the actual values for 44 months were moving in almost similar patterns in all intervals. Therefore, we can confirm that this time series prediction model for healthcare technology is very suitable.

• Journal of the Semiconductor & Display Technology
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• v.10 no.1
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• pp.33-41
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• 2011

Due to the recent quantum leaps forward in bio-, nano-, and information-technologies (BT, NT and IT), the precisionization and miniaturization of mechanical and electrical components are in high demand. In particular, the ITrelated equipments that take a great part in our domestic industry are in the area requiring high precision technologies. As a consequence, the researches on the development vibration isolation systems that diminish external disturbance or internal vibration are highly required. Among the components comprising the vibration isolation system, air spring has become on a focal point for the researchers due to its merits. This air spring is able to support heavy loads, keep a low natural frequency despite of having a lower value of stiffness, and control the performance of vibration isolation. However, sometimes the sole use of air spring is in demand due to some economic reasons. Under this circumstance, the damping effect of sole air spring may not enough to reduce sufficient amount of vibration. In this study, the air spring mount system connecting with an external chamber is proposed to increase or control the damping effect. To investigate its damping mechanism, the thermal and dynamic behaviors of the system is examined through a theoretical modeling approach in this part of research. In this approach, thermomechanical and Helmholtz resonator type models are to be employed for the air spring/external chambers and connecting tube system, respectively. The frequency response functions (FRFs) derived from the modeling effort are evaluated with physical parametric values and the effects of connecting tube length on these FRFs are identified through computer simulations.

• The Journal of Bigdata
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• v.7 no.2
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• pp.205-215
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• 2022

The current development of digital platform-based online retail business leads to the rapid growth of freight demand and service innovation. With the demand for converged logistics technologies, our government supports R&D projects to innovate the logistics service. The digital platforms are developed based on their own standard process and data formats. It may cause interoperability and connectivity-related problems. In this research, the standard data flow to connect the data among systems is defined based on the standard process. Especially, data for connectivity is categorized into three groups, common data, connected data, and management data. It may be possible to enhance the applicability to the practical logistics business.

• Korean Journal of Hazardous Materials
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• v.2 no.1
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• pp.18-26
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• 2014

We analyzed the demand of competent authorities requiring adequate technical information for initial investigation of chemical accidents. Reflecting technical reports on chemical accident response by environmental agencies in the U.S. and Canada, we presented information on environmental diffusion and toxic effects available for the first chemical accident response. Hydrogen fluoride may have the risk potential to corrode metals and cause serious burns and eye damages. In case of inhalation or intake, it could have severe health effects. The substance itself is inflammable, but once heated, it decomposes producing corrosive and toxic fume. In case of contact with water, it can produce toxic, corrosive, flammable or explosive gases and its solution, a strong acid, may react fiercely with a base. In case of hydrogen fluoride leak, the preventive measures are to decrease steam generation in exposed sites, prevent the transfer of vapor cloud and promptly respond using inflammable substances including calcium carbonate, sodium bicarbonate, ground limestone, dried soil, dry sand, vermiculite, fly ash and powder cement. The method for fire fighting is to suppress fire with manless hose stanchions or monitor nozzles by wearing the whole body protective clothing equipped with over-pressure self-contained breathing apparatus from distance. In case of transport accident accompanied with fire, evacuation distance is 1,600m radius. In cae of fire, fire suppression needs to be performed using dry chemicals, CO₂, water spray, water fog, and alcohol-resistance foam, etc. The major symptoms by exposure route are dyspnoea, bronchitis, chemical pneumonia and pulmonary edema for respiration, skin laceration, dermatitis, burn, frostbite and erythema for eyes, and nausea, diarrhea, stomachache, and tissue destruction for digestive organs. In atmosphere, its persistency is low, and its bioaccumulation in aquatic organism is also low.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.4
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• pp.1013-1023
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• 2016

Transborder data flow(TBDF) of personal information in Korea has been limited by current Privacy law which request data subject to give consent. As the IT industry is growing at an incredible rate, there is a need to review the existing law to cope with growing industrial demand and increasing numbers of international data transfer. The transfer of personal data overseas not only allow businesses providing IT services including finance, internet, e-commerce to thrive, but also impact every aspect of our lives which are increasingly depended on these technology. Transmitting personal data across borders raises serious questions of privacy protection and restriction of business operation. In ordrer to promote interoperability of personal data in international environment, a considerable amount of research and debate needs to be taken before implementing a sound policy. This paper presents a need for a sound TBDF policy in Korea by examine the main policy challenges associated with TBDF. Finally, the paper identify policy suggestions based on European Union's approach as they have successfully implemented TBDF policy that balanced data privacy and economic agenda.

• Journal of the Korean Society of Clothing and Textiles
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• v.31 no.5 s.164
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• pp.670-679
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• 2007

Contemporary consumers interested in fashion develop global tastes regarding consumption and senses on how much certain products cost in the global market place. Demand for foreign brands and products produced a new type of e-tailor called surrogate Internet shopping malls. Due to the unfamiliarity of such retailers, consumers may perceive different types of risks and may show different styles of seeking informations. The research interest of this study was to investigate the differences of risk perception and information search between surrogate e-mall shoppers and general e-mall shoppers. In addition, we examined the influence of these two variables on consumer satisfaction. A survey questionnaire was developed. Measures of three types of e-shopping risks (delivery, transaction, service), information search and satisfaction were included. Data from surrogate e-mall consumers and general e-mall consumers were statistically analyzed. Surrogate e-mall shoppers showed a higher level of product delivery risk and customer service risk than general e-mall shoppers. They also spend more time in seeking information before making purchases. Regression analysis showed that perceived risk had significant influence on information search and consumer satisfaction for surrogate e-mall shoppers, whereas for general e-mall shoppers, no significant influence was detected. The findings should assist marketers and academics in their understanding of the surrogate e-shopping malls.

• Journal of the Korean Society for information Management
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• v.35 no.4
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• pp.263-284
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• 2018

Academic libraries need to select and purchase essential collections to support students and faculty in education and research. Therefore, libraries reflect patrons' information needs on collection development through patron purchase requests. This study analyzed the pattern and efficiency of patron purchase requests in a longer-term perspective; for over a decade. Patron purchase requests show different tendencies depending on academic characteristics, which enabled libraries to identify the users' information needs in various subjects. Typically users contributed to collection development by expressing information needs in their fields of study through purchase requests. In the meantime, users in certain fields showed interest in other subject areas besides their own to select general books on various topics. Through this study, it became evident that a major portion of library collections were affected by active purchase requests from a small number of users. However those books were proven to be in demand in terms of effectiveness. Patron-driven acquisition is being implemented as an effective collection development policy.

• Journal of Technology Innovation
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• v.23 no.2
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• pp.33-54
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• 2015

Supercomputing performance demand forecasting at the national level is an important information to the researchers in fields of the computational science field, the specialized agencies which establish and operate R&D infrastructure, and the government agencies which establish science and technology infrastructure. This study derived the factors affecting the scientific and technological capability through the analysis of supercomputing performance prediction research, and it proposed a hybrid forecasting model of applying the super-computer technology trends. In the cross-sectional analysis, multiple regression analysis was performed using factors with GDP, GERD, the number of researchers, and the number of SCI papers that could affect the supercomputing performance. In addition, the supercomputing performance was predicted by multiplying in the cross-section analysis with technical progress rate of time period which was calculated by time series analysis using performance(Rmax) of Top500 data. Korea's performance scale of supercomputing in 2016 was predicted using the proposed forecasting model based on data of the top500 supercomputer and supercomputing performance demand in Korea was predicted using a cross-sectional analysis and technical progress rate. The results of this study showed that the supercomputing performance is expected to require 15~30PF when it uses the current trend, and is expected to require 20~40PF when it uses the trend of the targeting national-level. These two results showed significant differences between the forecasting value(9.6PF) of regression analysis and the forecasting value(2.5PF) of cross-sectional analysis.