• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.375-383
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• 2021

Due to COVID-19, people are building new lifestyles such as online shopping, online travel, and video conferencing by limiting going out and gatherings. Such rapid social change is causing new problems and deepening existing problems at the same time. In particular, as online consumption increases significantly, traffic congestion, air pollution, and the heavy workload of delivery drivers are deepening in the daily logistics industry, and face-to-face delivery is emerging as a new problem. With the advent of the 4th industrial revolution, unmanned delivery using drones, artificial intelligence, and autonomous driving is emerging as an alternative to the existing logistics industry. However, space for logistics facilities and securing additional logistics sites due to drone flight are emerging as new problems to be solved. Therefore, it is intended to link additional services such as logistics movement, storage, and delivery by utilizing the existing transportation business, the subway, as a space for a logistics facility for drones that can solve existing problems and new problems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.3950-3969
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• 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 Society of Preventive Korean Medicine
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• v.26 no.2
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• pp.1-9
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• 2022

Objectives : The purpose of this study is to identify priorities for the 4th Comprehensive Plan for Korean Medicine Development using Delphi and AHP techniques. Methods : This study uses Delphi-AHP method to first, select the target priority policy based on the policy content of the 4th Comprehensive Plan. In addition, two surveys on the priorities were conducted to reach consensus between experts. The main results of the first survey were also provided to experts participating in the second survey to help form expert consensus. Finally, the final policy priority was chosen based on the second survey result. Results : Survey results showed that of the 39 policies in the 4th Comprehensive Plan, "improve the accessibility of Korean medicines," was the most important goal. This was followed by "support for Korean medicine R&D from clinical research to industrialization," "provide foundation for a pilot project that provides customized medical services" and "strengthen the public medicine function of Korean medicine by expanding the its infrastructure in national and public hospitals." Conclusion : The results showed that capacity building of Korean medicine in primary care, improvement of the health insurance system, and research centered on industrialization are relatively more important goals, while the need to enhance global competitiveness was much less important. These key points can serve as a reference when formulating the 5th Comprehensive Plan for Korean Medicine Development in the future.

• Journal of Internet Computing and Services
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• v.23 no.2
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• pp.71-77
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• 2022

In this paper, a database is collected for extending the speech synthesis model to a model that synthesizes speech according to emotions and generating facial expressions. The database is divided into male and female data, and consists of emotional speech and facial expressions. Two professional actors of different genders speak sentences in Korean. Sentences are divided into four emotions: happiness, sadness, anger, and neutrality. Each actor plays about 3300 sentences per emotion. A total of 26468 sentences collected by filming this are not overlap and contain expression similar to the corresponding emotion. Since building a high-quality database is important for the performance of future research, the database is assessed on emotional category, intensity, and genuineness. In order to find out the accuracy according to the modality of data, the database is divided into audio-video data, audio data, and video data.

• Journal of Internet Computing and Services
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• v.23 no.6
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• pp.87-96
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• 2022

This paper is an empirical analysis as a reference model that can predict up to the maximum number of elementary school student care needs in local governments across the country. This study analyzed and predicted the characteristics of the region based on machine learning to predict the demand for elementary care in a new apartment complex. For this purpose, a total of 292 variables were used, including data related to apartment structure, such as number of parking spaces per household, and building-to-land ratio, environmental data around apartments such as distance to elementary schools, and population data of administrative districts. The use of various variables is of great significance, and it is meaningful in complex analysis. It is also an empirical case study that increased the reliability of the model through comparison with the actual value of the basic local government.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1890-1897
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• 2021

People counting is an important technology to provide application services such as smart home, smart building, smart car, etc. Due to the social distancing of COVID-19, the people counting technology attracted public attention. People counting system can be implemented in various ways such as camera, sensor, wireless, etc. according to service requirements. People counting system using WiFi AP uses WiFi CSI data that reflects multipath information. This technology is an effective solution implementing indoor with low cost. The conventional WiFi CSI-based people counting technologies have low accuracy that obstructs the high quality service. This paper proposes a deep learning people counting system based on WiFi CSI data. Data preprocessing using auto-encoder, data augmentation that transform WiFi CSI data, and a proposed deep learning model improve the accuracy of people counting. In the experimental result, the proposed approach shows 89.29% accuracy in 6 subjects.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.6
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• pp.189-196
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• 2023

Today, in the era of the 4th industrial revolution based on the paradigm of hyper-connectivity, super-intelligence, and superconvergence, the remote work environment is becoming central based on technologies such as mobile, cloud, and big data. This remote work environment has been accelerated by the demand for non-face-to-face due to COVID-19. Since the remote work environment can perform various tasks by accessing services and resources anytime and anywhere, it has increased work efficiency, but has caused a problem of incapacitating the traditional boundary-based network security model by making the internal and external boundaries ambiguous. In this paper, we propse a method to improve the limitations of the traditional boundary-oriented security strategy by building a security model centered on core components and their relationships based on the zero trust idea that all actions that occur in the network beyond the concept of the boundary are not trusted.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.212-217
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• 2009

Strategic alliance is a proactive management process that integrates and optimizes value-added services of each party to best achieve business objectives of all parties within the relationship. Under the current competitive global environment, strategic alliance can produce a "Win-Win" situation and thus change paradigm that has resided in the construction industry. While many studies revealed the significance of alliance relationship in the industry, its impact on project performance has rarely been analyzed. Using the data obtained from 661 construction projects in the Construction Industry Institute database (359 projects from 38 owners and 302 projects from 29 contractors), this study first diagnoses the implementation status of strategic alliance at both project and company levels. Then, its impact on project performance is quantified and discussed. The descriptive analysis performed in this study revealed that an average of 79% of owner companies and 69% of contractor companies have ever implemented strategic alliance into at least one of their projects. However, both owner and contractor companies did not always use the strategy for all or their projects. Only 33% and 30% of projects reported by owners and contractors have been completed under alliance relationship, respectively. Analyzing the alliance impact on project performance, this study also establishes that strategic alliance positively affects project performance of both owners and contractors while owners should consider and control the level of its use for their projects. Recognizing and understanding the benefits from strategic alliance will be a starting point to produce mutual success among project participants, ultimately allowing the construction industry to go forward to a sustainable industry that transfers success from one project to the other.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2030-2052
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• 2023

Analysis of a bipartite (two-mode) network is a significant research area to understand the formation of social communities, economic systems, drug side effect topology, etc. in complex information systems. Most of the previous works talk about a projection-based model or latent feature model, which predicts the link based on singular similarity. The projection-based models suffer from the loss of structural information in the projected network and the latent feature is hardly present. This work proposes a novel method for link prediction in the bipartite network based on an ensemble of composite similarities, overcoming the issues of model-based and latent feature models. The proposed method analyzes the structure, neighborhood nodes as well as latent attributes between the nodes to predict the link in the network. To illustrate the proposed method, experiments are performed with five real-world data sets and compared with various state-of-art link prediction methods and it is inferred that this method outperforms with ~3% to ~9% higher using area under the precision-recall curve (AUC-PR) measure. This work holds great significance in the study of biological networks, e-commerce networks, complex web-based systems, networks of drug binding, enzyme protein, and other related networks in understanding the formation of such complex networks. Further, this study helps in link prediction and its usability for different purposes ranging from building intelligent systems to providing services in big data and web-based systems.

• Journal of Internet Computing and Services
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• v.24 no.2
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• pp.37-45
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• 2023

In line with the 4th Industrial Revolution, facility maintenance technologies for building smart factories are receiving attention and are being advanced. In addition, technology is being applied to smart farms and smart fisheries following smart factories. Among them, in the case of a recirculating aquaculture system, there is a motor pump that circulates water for a stable quality environment in the tank. Motor pump maintenance activities for recirculating aquaculture system are carried out based on preventive maintenance and data obtained from vibration sensor. Preventive maintenance cannot cope with abnormalities that occur before prior planning, and vibration sensors are affected by the external environment. This paper proposes an anomaly detection algorithm that utilizes ADTK, a Python open source, for motor pump anomaly detection based on data collected through current sensors that are less affected by the external environment than noise, temperature and vibration sensors.