• The Journal of the Convergence on Culture Technology
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• v.10 no.5
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• pp.195-200
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• 2024

The purpose of this study is to propose optimized cognitive warfare strategies for the Korean Peninsula by analyzing recent war case studies. Through the analysis of the Armenia-Azerbaijan war, the Israel-Palestine conflict, the Ukraine-Russia war, and the Israel-Hamas conflict, it was found that the following aspects are crucial in conducting cognitive warfare: 1) applying methods according to objectives, 2) organizing and structuring appropriately to the objectives and means, and 3) utilizing various means from both civilian and military sectors. Based on these findings, cognitive warfare strategies optimized for the operational environment of the Korean Peninsula were suggested in terms of the three elements of military innovation. From the aspect of methodology, it is recommended to develop cognitive warfare scenarios based on legitimacy and legality, and to integrate roles according to the level of warfare. Regarding organization and structuring, the establishment of a national-level control tower and the construction of an integrated response system involving civilians, government, military, and police based on legislation are proposed. In terms of means, it is suggested to utilize various tools from the civilian, government, military, and police sectors, such as North Korean defectors, psychological warfare broadcasts against North Korea, social media, and cyber operations, for auditory, visual, and message delivery. In future battlefields characterized by hyper-connectivity and hyper-intelligence, the execution of cognitive warfare will become increasingly important. Therefore, it is necessary to continuously develop optimized cognitive warfare strategies for the Korean Peninsula through comprehensive national efforts.

• Journal of Korean Society of Forest Science
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• v.113 no.3
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• pp.292-307
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• 2024

This study aimed to develop an index to assess the level of revitalization in mountain villages by utilizing big data and to verify its applicability in such areas. To achieve this, four key indices related to mountain villages were developed to evaluate the degree of revitalization, namely, Settlement Index, Workplace Index, Learning Index, and Leisure Index. These indices enable users to compare the revitalization levels of different mountain villages by establishing living zones, assigning data weights, extracting comparative data, and generating results in both map and report formats. The revitalization index developed in this study was applied to five mountain villages (A, B, C, D, E) located in Jeollabuk-do. Results showed that Village C had the highest comprehensive score of 320 points, while Village E had the lowest score of 141 points. In the mountain village indices of Jeollabuk-do, the Workplace Index generally showed higher scores, whereas the Learning Index had relatively lower scores on average. The development of these indices provides a practical means to identify which areas should be prioritized for support to enhance revitalization in specific mountain villages and offers a clear comparison of the revitalization levels across different regions and individual villages. The mountain village revitalization index developed in this study is expected to serve as valuable foundational information for formulating mountain village revitalization policies.

• The Journal of the Convergence on Culture Technology
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• v.10 no.5
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• pp.725-733
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• 2024

This study aims to develop an artificial intelligence-based model for predicting the causes of chemical accidents, utilizing data on 865 chemical accident situations and cases provided by the Chemical Safety Agency under the Ministry of Environment from January 2014 to January 2024. The research involved training the data using six artificial intelligence models and compared evaluation metrics such as accuracy, precision, recall, and F1 score. Based on 356 chemical accident cases from 2020 to 2024, additional training data sets were applied using chemical accident cause investigations and similar accident prevention measures suggested by the Chemical Safety Agency from 2021 to 2022. Through this process, the Multi-Layer Perceptron (MLP) model showed an accuracy of 0.6590 and a precision of 0.6821. the Multi-Layer Perceptron (MLP) model showed an accuracy of 0.6590 and a precision of 0.6821. The Logistic Regression model improved its accuracy from 0.6647 to 0.7778 and its precision from 0.6790 to 0.7992, confirming that the Logistic Regression model is the most effective for predicting the causes of chemical accidents.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.4
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• pp.201-214
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• 2024

This study derived an effective way to expose information on product reviews by analyzing how the accumulation of information on reviews of online shopping malls, which are receiving a lot of attention amid the rapid increase in non-face-to-face transactions with small and medium-sized venture companies with insufficient resources, affects product review ratings. Hypotheses were derived based on the main theory of behavioral economics and the theory of consumer expectation inconsistency, and for empirical research, the effect of the accumulation of information on product reviews were analyzed from a short and long-term perspective using Amazon's product reviews and seller information big data. For the empirical study, 9,092,480 reviews written for 378,411 products of Amazon were used, and the hypotheses were verified through hierarchical regression analysis. As a result of the analysis, it was found that the average rating decreased as the number of reviews increased. It was found that the product with a large number of recent reviews had a high rating. The characteristics of the product showed a moderating effect on these effects. This study will provide a new theoretical basis for research related to product review, and will help small and medium-sized venture companies that focus on sales through online shopping malls due to lack of resources to increase sales performance by appropriately utilizing review information. It will also provide empirical insights into effective product review information exposure measures for online shopping mall managers.

• Journal of the Korean Regional Science Association
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• v.40 no.3
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• pp.109-126
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• 2024

The central government has been promoting innovation cities since the early 2000s for the balanced development of the country in order to solve the problems of overcrowding in the metropolitan area and underdevelopment of the country due to rapid urbanization since the 1980s. The 10 innovation cities built through the regional relocation of public institutions were expected to have positive effects such as economic revitalization and population influx, but uncertainties and various externalities still exist. Therefore, this study aims to analyze innovation cities from the perspective of urban resilience to create sustainable cities. To this end, the research analyzed urban factors in the physical, natural, social, economic, and institutional dimensions of Ulsan Ujeong Innovation City, and applied the Adaptive Cycle and Panarchy to comprehensively analyze them. As a result of the analysis, it was found that the built, socio-economic, and natural environments are currently undergoing the adaptive cycle stages of preservation, reorganization, and growth, respectively, and the interaction and structural causal relationships between Korea, Ulsan Metropolitan City, and Ulsan Ujeong Innovation City were identified. The study concluded that Ulsan Ujeong Innovation City can be sustainable by utilizing opportunities such as the central government's Innovation City Season 2 and financial support, construction of Janghyeon Advanced Industrial Complex, and fostering local innovation clusters according to local conditions.

• Informatization Policy
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• v.31 no.3
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• pp.48-71
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• 2024

In the modern era of digital sophistication, effective public administration warrants a citizen-centric approach that not only anticipates the needs of public service users but also comprehends their behaviors in undertaking proactive measures to deliver public services as needed. This study adopts a typological perspective by viewing e-government users as distinct consumer groups with individualized demands, behavioral tendencies, and perceptual attributes. Utilizing data from a 2021 survey on e-government service utilization, a two-step cluster analysis was conducted to delineate user typology through an empirical study. The analysis incorporated variables such as the purpose of using e-government, selected e-government channels, subjective perceptions of technological risk, and personal innovativeness. Accordingly, e-government users were classified into five distinct typological groups labeled "Unilateral Active Users Geared to Social Media," "Versatile Power Users," "Unilateral Pragmatic Active Users," "Occasional Passive Users," and "Minimal Users." This typological differentiation of e-government user groups is intended to help identify unique user demands and characteristics so as to facilitate the delivery of tailored e-government services and informed policy decisions catering to the diverse needs of users.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.5
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• pp.53-59
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• 2024

This study presents the design and implementation of a camera-based 6DoF (6 Degrees of Freedom) tracking and pose estimation system. In particular, we propose a method for accurately estimating the positions and orientations of all fingers of a user utilizing a 6DoF robotic arm. The system is developed using the Python programming language, leveraging the Mediapipe and OpenCV libraries. Mediapipe is employed to extract keypoints of the fingers in real-time, allowing for precise recognition of the joint positions of each finger. OpenCV processes the image data collected from the camera to analyze the finger positions, thereby enabling pose estimation. This approach is designed to maintain high accuracy despite varying lighting conditions and changes in hand position. The proposed system's performance has been validated through experiments, evaluating the accuracy of hand gesture recognition and the control capabilities of the robotic arm. The experimental results demonstrate that the system can estimate finger positions in real-time, facilitating precise movements of the 6DoF robotic arm. This research is expected to make significant contributions to the fields of robotic control and human-robot interaction, opening up various possibilities for future applications. The findings of this study will aid in advancing robotic technology and promoting natural interactions between humans and robots.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.1307-1307
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• 2024

Recent research on heavy equipment has been conducted for the purposes of enhanced safety, productivity improvement, and carbon neutrality at construction sites. A sensor-based approach is being explored to monitor the location and movements of heavy equipment in real time. However, it poses significant challenges in terms of time and cost as multiple sensors should be installed on numerous heavy equipment at construction sites. In addition, there is a limitation in identifying the collaboration or interference between two or more heavy equipment. In light of this, a vision-based deep learning approach is being actively conducted to effectively respond to various working conditions and dynamic environments. To enhance the performance of a vision-based activity recognition model, it is essential to secure a sufficient amount of training datasets (i.e., video datasets collected from actual construction sites). However, due to safety and security issues at construction sites, there are limitations in adequately collecting training dataset under various situations and environmental conditions. In addition, the videos feature a sequence of multiple activities of heavy equipment, making it challenging to clearly distinguish the boundaries between preceding and subsequent activities. To address these challenges, this study proposed a domain adaptation in vision-based transfer learning for automated excavator activity recognition utilizing 3D ResNet (residual deep neural network). Particularly, this study aimed to identify the optimal depth of 3D ResNet (i.e., the number of layers of the feature extractor) suitable for domain adaptation via fine-tuning process. To achieve this, this study sought to evaluate the activity recognition performance of five 3D ResNet models with 18, 34, 50, 101, and 152 layers, which used two consecutive videos with multiple activities (5 mins, 33 secs and 10 mins, 6 secs) collected from actual construction sites. First, pretrained weights from large-scale datasets (i.e., Kinetic-700 and Moment in Time (MiT)) in other domains (e.g., humans, animals, natural phenomena) were utilized. Second, five 3D ResNet models were fine-tuned using a customized dataset (14,185 clips, 60,606 secs). As an evaluation index for activity recognition model, the F1 score showed 0.881, 0.689, 0.74, 0.684, and 0.569 for the five 3D ResNet models, with the 18-layer model performing the best. This result indicated that the activity recognition models with fewer layers could be advantageous in deriving the optimal weights for the target domain (i.e., excavator activities) when fine-tuning with a limited dataset. Consequently, this study identified the optimal depth of 3D ResNet that can maintain a reliable performance in dynamic and complex construction sites, even with a limited dataset. The proposed approach is expected to contribute to the development of decision-support systems capable of systematically managing enhanced safety, productivity improvement, and carbon neutrality in the construction industry.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.1285-1285
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• 2024

In process plant construction, the implementation of design automation technologies is pivotal in reducing the timeframes associated with the design phase and in enabling the generation and evaluation of a variety of design alternatives, thereby facilitating the identification of optimal solutions. These technologies can play a crucial role in ensuring the successful delivery of projects. Previous research in the domain of design automation has primarily focused on parametric design in architectural contexts and on the automation of equipment layout and pipe routing within plant engineering, predominantly employing rule-based algorithms. Nevertheless, these studies are constrained by the limited flexibility of their models, which narrows the scope for generating alternative solutions and complicates the process of exploring comprehensive solutions using nonlinear optimization techniques as the number of design and engineering parameters increases. This research introduces a framework for automating plant design through the use of generative neural network models to overcome these challenges. The framework is applicable to the layout problems of process plants, covering the equipment necessary for production processes and the facilities for essential resources and their interconnections. The development of the proposed Neural-network (NN) based Generative Design Model unfolds in four stages: (a) Rule-based Model Development: This initial phase involves the development of rule-based models for layout generation and evaluation, where the generation model produces layouts based on predefined parameters, and the evaluation model assesses these layouts using various performance metrics. (b) Neural Network Model Development: This phase transitions towards neural network models, establishing a NN-based layout generation model utilizing Generative Adversarial Network (GAN)-based methods and a NN-based layout evaluation model. (c) Model Optimization: The third phase is dedicated to optimizing the models through Bayesian Optimization, aiming to extend the exploration space beyond the limitations of rule-based models. (d) Inverse Design Model Development: The concluding phase employs an inverse design method to merge the generative and evaluative networks, resulting in a model that outputs layout designs to meet specific performance objectives. This study aims to augment the efficiency and effectiveness of the design process in process plant construction, transcending the limitations of conventional rule-based approaches and contributing to the achievement of successful project outcomes.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.5
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• pp.1-9
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• 2024

With the emergence of various next-generation wireless networks, the traditional store and forward(SF) method at network nodes has faced limitations in efficiently utilizing network capacity. To overcome these limitations, various network coding techniques based on the decode and forward(DF) method have been proposed. However, these techniques have primarily focused on traffic environments with asymmetric packet lengths between relay nodes, limiting their applicability when different modulation and coding schemes(MCS) are applied to relay nodes. This paper proposes a relay network coding scheme that supports high frequency efficiency while simultaneously enabling bidirectional relaying using DF, considering asymmetric MCS traffic that reflects different transmission data and wireless channel conditions between individual nodes for efficient utilization of wireless network capacity. Additionally, this paper demonstrates the possibility of cooperative communication at the relay and examines the effect of increased communication distance. Subsequently, computer simulations are conducted to verify the performance gains of the proposed technique in terms of network coding for each source node with asymmetric information lengths. This proposed technique shows additional bit error rate(BER) performance gains by adopting an incremental redundancy(IR) scheme that follows network coding, even in mobile node environments where direct link transmission between source nodes is possible.