• Journal of Advanced Marine Engineering and Technology
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• v.38 no.3
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• pp.292-301
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• 2014

This paper describes development of distributed type independent electro-hydraulic ballast valve remote control system with diagnostic function based on fuzzy inference using redundant Modbus communication and ethernet Modbus TCP/IP. Diagnostic function estimate degradation of system components and diagnose system faults, which results in shortage of fault maintenance time and improvement of system safety. Slave devices which control each valve and master device which command, monitor and diagnose slave system are developed. Slave devices are connected to master device with redundant Modbus networks and master device is connected to ship's integrated control system with Modbus TCP/IP. Also this paper describes development of simulator to test and confirm whether developed system can be integrated with ship's integrated control and monitoring system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.11
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• pp.5400-5418
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• 2016

Femtocells are envisioned as a key solution to embrace the ever-increasing high data rate and thus are extensively deployed. However, the dense and random deployments of femtocell access points (FAPs) induce severe intercell inference that in turn may degrade the performance of spectral efficiency. Hence, unrestrained proliferation of FAPs may not acquire a net throughput gain. Besides, given that numerous FAPs deployed in ultra-dense networks (UDNs) lead to significant energy consumption, the amount of FAPs deployed is worthy of more considerations. Nevertheless, little existing works present an analytical result regarding the optimal FAP density for a given User Equipment (UE) density. This paper explores the realistic scenario of randomly distributed FAPs in UDN and derives the coverage probability via Stochastic Geometry. From the analytical results, coverage probability is strictly increasing as the FAP-to-UE ratio increases, yet the growing rate of coverage probability decreases as the ratio grows. Therefore, we can consider a specific FAP-to-UE ratio as the point where further increasing the ratio is not cost-effective with regards to the requirements of communication systems. To reach the optimal FAP density, we can deploy FAPs in line with peak traffic and randomly switch off FAPs to keep the optimal ratio during off-peak hours. Furthermore, considering the unbalanced nature of traffic demands in the temporal and spatial domain, dynamically and carefully choosing the locations of active FAPs would provide advantages over randomization. Besides, with a huge FAP density in UDN, we have more potential choices for the locations of active FAPs and this adds to the demand for a strategic sleeping policy.

• The Transactions of the Korea Information Processing Society
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• v.4 no.9
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• pp.2280-2288
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• 1997

An agent is a software element that, by making use of knowledge and inference, performs tasks on behalf of the user. In general, an agent has the properties of autonomy, social ability, reactivity, and durability. Many researches on agents are more and more aiming at the multiagent systems since it is not sufficient to let a single agent do the whole things, especially in a real world where tasks require many diverse activities. However, the multiagent frameworks still have some limitations in the processing of user queries that are often ambiguous and goal-oriented. Also, a series of procedures or plans could not be generated from a single query directly. In order to give more intelligence to the multiagent framework, we propose a method of extending the framework with a plan generation module. The open agent architecture (OAA), which is a multiagent framework that we developed, is integrated with UCPOP, which is a AI planner. A travel schedule management agent (TSMA) system is implemented to explore the effects of the method. The extended system enables the user to only specify goal-oriented queries, and the plans and procedures to satisfy these goals are generated automatically. Also, this system provides a cooperative and knowledge-sharing environment that integrates several knowledge-based systems and planning systems that are distributed and used independently.

• Journal of the Korea Society of Computer and Information
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• v.25 no.3
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• pp.191-198
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• 2020

In this paper we propose a for high school students who are attending a nationwide city with experience in Internet pornography, we would like to find out the impact of Internet pornography experience and self-regulation on sex crime harmful behavior. For this study, an Internet panel survey was conducted using a purposeful method of significant allocation inference. During the period, 246 copies of the questionnaire were distributed for about a month from May to June 2018 and 210 parts were analyzed except for 36 parts with no experience of pornographic material, and further analysis was conducted on 85 respondents with experience in harmful behavior of sexual violence. To this end, analysis tools used the SPS WIN 20.0 program version. The research results are as follows. First, we could find that Internet pornography has a negative effect on teenagers. This shows the probability of developing sexual violence into behavior as people can experience pornographic material regardless of their will due to the high Internet access. Second, the self-regulation of sexual violence behavior is found to have no direct impact. This is not just the adolescent's will to do so, but it is affected by the external environment. Third, self-regulation has proven its role as a modulator to mitigate negative perceptions of Internet pornography. Based on this, the proposal for limiting current prices was discussed.

• Journal of Korean Society of Forest Science
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• v.109 no.1
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• pp.62-71
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• 2020

With the aim of restoring slow-moving landslide areas, this study collected fundamental data from tree-ring analysis of curved trees in these areas. We collected both upper and lower stem disks to measure the azimuth angles of six trees with growth curvature caused by tension cracks. Additionally, we analyzed various factors in the slow moving landslide area. The geological strata and main constitutive rocks in the study area were anorthosite-formed in the Precambrian period; moreover, there were no intrusive rocks, other geological strata, geological folds, or faults. The talus with weathered rocks was distributed in the upper zone of the slow-moving landslide area. According to annual-ring analysis of curved trees and terrain analysis by satellite imagery, slow-moving landslide occurred from the top to the bottom end of the slope between 1999 and 2011. There was a significant relationship (P < 0.01) between the azimuth angle of cracks caused by the slow-moving landslide and the angle of the curved trees. These results suggest that the occurrence of slow-moving landslides could be confirmed through analysis of annual-rings of curved trees, underground water levels, and terrain (by satellite imagery).

• Journal of Intelligence and Information Systems
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• v.14 no.1
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• pp.35-45
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• 2008

In order to constructan ubiquitous computing environment, it is necessary to develop a technology that can recognize users and circumstances. In this regard, the question of recognizing and expressing user Context regardless of computer and language types has emerged as an important task under the heterogeneous distributed processing system. As a means to solve this task of representing user Context in the ubiquitous environment, this paper proposes to describe user Context as the most similar form of human thinking by using semantic web and fuzzy concept independentof language and computer types. Because the conventional method of representing Context using an usual collection has some limitations in expressing the environment of the real world, this paper has chosen to use Fuzzy OWL language, a fusion of fuzzy concept and standard web ontology language OWL. Accordingly, this paper suggests the following method. First we represent user contacted environmental information with a numerical value and states, and describe it with OWL. After that we transform the converted OWL Context into Fuzzy OWL. As a last step, we prove whether the automatic circumstances are possible in this procedure when we use fuzzy inference engine FiRE. With use the suggested method in this paper, we can describe Context which can be used in the ubiquitous computing environment. This method is more effective in expressing degree and status of the Context due to using fuzzy concept. Moreover, on the basis of the stated Context we can also infer the user contacted status of the environment. It is also possible to enable this system to function automatically in compliance with the inferred state.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.17-23
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• 2016

In the future network such as Internet of Things (IoT), the number of computing devices are expected to grow exponentially, and each of the things communicates with the others and acquires information by itself. Due to the growing interest in IoT applications, the broadcasting in Opportunistic ad-hoc networks such as Machine-to-Machine (M2M) is very important transmission strategy which allows fast data dissemination. In distributed networks for IoT, the energy efficiency of the nodes is a key factor in the network performance. In this paper, we propose a fuzzy logic based probabilistic multi-hop broadcast (FPMCAST) algorithm which statistically disseminates data accordingly to the remaining energy rate, the replication density rate of sending node, and the distance rate between sending and receiving nodes. In proposed FPMCAST, the inference engine is based the fuzzy rule base which is consists of 27 if-then rules. It maps input and output parameters to membership functions of input and output. The output of fuzzy system defines the fuzzy sets for rebroadcasting probability, and defuzzification is used to extract a numeric result from the fuzzy set. Here Center of Gravity (COG) method is used to defuzzify the fuzzy set. Then, the performance of FPMCAST is evaluated through a simulation study. From the simulation, we demonstrate that the proposed FPMCAST algorithm significantly outperforms flooding and gossiping algorithms. Specially, the FPMCAST algorithm has longer network lifetime because the residual energy of each node consumes evenly.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1243-1244
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• 2022

In recent years, the growing interest in off-site construction has led to factories scaling up their manufacturing and production processes in the construction sector. Consequently, continuous large-scale site monitoring in low-variability environments, such as prefabricated components production plants (precast concrete production), has gained increasing importance. Although many studies on computer vision-based site monitoring have been conducted, challenges for deploying this technology for large-scale field applications still remain. One of the issues is collecting and transmitting vast amounts of video data. Continuous site monitoring systems are based on real-time video data collection and analysis, which requires excessive computational resources and network traffic. In addition, it is difficult to integrate various object information with different sizes and scales into a single scene. Various sizes and types of objects (e.g., workers, heavy equipment, and materials) exist in a plant production environment, and these objects should be detected simultaneously for effective site monitoring. However, with the existing object detection algorithms, it is difficult to simultaneously detect objects with significant differences in size because collecting and training massive amounts of object image data with various scales is necessary. This study thus developed a large-scale site monitoring system using edge computing and a small-object detection system to solve these problems. Edge computing is a distributed information technology architecture wherein the image or video data is processed near the originating source, not on a centralized server or cloud. By inferring information from the AI computing module equipped with CCTVs and communicating only the processed information with the server, it is possible to reduce excessive network traffic. Small-object detection is an innovative method to detect different-sized objects by cropping the raw image and setting the appropriate number of rows and columns for image splitting based on the target object size. This enables the detection of small objects from cropped and magnified images. The detected small objects can then be expressed in the original image. In the inference process, this study used the YOLO-v5 algorithm, known for its fast processing speed and widely used for real-time object detection. This method could effectively detect large and even small objects that were difficult to detect with the existing object detection algorithms. When the large-scale site monitoring system was tested, it performed well in detecting small objects, such as workers in a large-scale view of construction sites, which were inaccurately detected by the existing algorithms. Our next goal is to incorporate various safety monitoring and risk analysis algorithms into this system, such as collision risk estimation, based on the time-to-collision concept, enabling the optimization of safety routes by accumulating workers' paths and inferring the risky areas based on workers' trajectory patterns. Through such developments, this continuous large-scale site monitoring system can guide a construction plant's safety management system more effectively.