• International conference on construction engineering and project management
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• 2024.07a
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• pp.1073-1080
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• 2024

The imperative for real-time automatic construction progress monitoring (ACPM) to avert project delays is widely acknowledged in construction project management. Current ACPM methodologies, however, face a challenge as they rely on collecting data from construction sites and processing it offline for progress analysis. This delayed approach poses a risk of late identification of critical construction issues, potentially leading to rework and subsequent project delays. This research introduces a real-time construction progress monitoring framework that integrates cutting-edge semantic Simultaneous Localization and Mapping (SLAM) techniques. The innovation lies in the framework's ability to promptly identify structural components during site inspections conducted through a robotic system. Incorporating deep learning models, specifically those employing semantic segmentation, enables the system to swiftly acquire and process real-time data, identifying specific structural components and their respective locations. Furthermore, by seamlessly integrating with Building Information Modeling (BIM), the system can effectively evaluate and compare the progress status of each structural component. This holistic approach offers an efficient and practical real-time progress monitoring solution for construction projects, ensuring timely issue identification and mitigating the risk of project delays.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.1
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• pp.60-65
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• 2010

This paper describes the results of 2D mapping, feature detection and matching to create the surrounding environment in the mounted stereo camera on Mobile robot. Extract method of image's feature in real-time processing for quick operation uses the edge detection and Sum of Absolute Difference(SAD), stereo matching technique can be obtained through the correlation coefficient. To estimate the location of a mobile robot using ZigBee beacon and encoders mounted on the robot is estimated by Kalman filter. In addition, the merged gyro scope to measure compass is possible to generate map during mobile robot is moving. The Simultaneous Localization and Mapping (SLAM) of mobile robot technology with an intelligent robot can be applied efficiently in human life would be based.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.10
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• pp.2068-2079
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• 2015

Recently, interesting in flexible working has been increased, and some central government and large companies are conducting flexible working. With the conducting of flexible working, workers are able to get a convenience but the cost of managing is increased. In this paper, we suggest a method of reducing the cost and managing efficiently. Our study determines the position of the workers using indoor localization techniques through the Wi-Fi fingerprint and smartphone, and records the office hours. The previous time and attendance systems have to install attendance recording device(e.g. Fingerprint Attendance System, RFID Card Attendance System) and are needed to manipulate manually. Our system doesn't need to install extra devices, and also doesn't need to manipulate manually. Our system automatically records the office hours. Also most of previous time and attendance systems have another weakness. They only record data when workers start and stop work. But our system exactly records office hours for each workplaces. In this paper, We introduce an effective time and attendance system in variety flexible working. Our experiments shows that our system workers' detected office hours accurately. The accuracy of our time and attendance system was 98.7%.

• The Journal of the Convergence on Culture Technology
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• v.10 no.5
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• pp.189-193
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• 2024

As cyber threats increase in the defense sector, the security of weapon system software is becoming increasingly important. Currently, most of the embedded software installed in domestic weapon systems operates based on foreign real-time operating systems(RTOS) that have no security. As a result, the localization and security enhancement of embedded software for weapon systems have emerged as urgent tasks. This study aims to propose the application and development strategies of secure RTOS for weapon systems. To this end, we examined the technological trends of domestic and foreign RTOS and secure RTOS, and analyzed the problems of current embedded software in weapon systems. The results revealed major issues such as low localization, vulnerability to cyber attacks, difficulty in maintenance, increased costs, and loss of opportunities for accumulating technological capabilities. An investigation of the current status of embedded software applied to existing weapon systems found that embedded SW are in operation across all fields, including maneuver, firepower, protection, command and control, communication, naval vessels, and aircraft. Among them, 99% rely on foreign RTOS such as VxWorks. A review of the core functions and applicability of secure RTOS to weapon systems suggests that it can be applied to key areas requiring real-time performance and security, such as fire control, navigation devices, and flight control in existing and future weapon systems. However, ensuring performance and reliability, securing verification and compatibility, and systematic government support were raised as prerequisites.

• Proceedings of the KIEE Conference
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• 2002.11c
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• pp.370-373
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• 2002

This paper proposes a new measurement system determine the localization and the type of object which use only three ultrasonic sensors, one the transmitter, one the receiver and one transduce doing both transmitter and receiver. this system can classifies the type and determines the pose of the target object. it used the method of Pseudoamplitude Scan. So it significantly simple the sensing system and reduce the signal processing time so that the working environment can be recognized in real time.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1934-1935
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• 2011

In order for a service robot to perform several tasks, basically autonomous navigation technique such as localization, mapping, and path planning is required. The localization (estimation robot's pose) is fundamental ability for service robot to navigate autonomously. In this paper, we propose a new system for point pattern matching based visual global localization using spot lightings in ceiling. The proposed algorithm us suitable for system that demands high accuracy and fast update rate such a guide robot in the exhibition. A single camera looking upward direction (called ceiling vision system) is mounted on the head of the mobile robot and image features such as lightings are detected and tracked through the image sequence. For detecting more spot lightings, we choose wide FOV lens, and inevitably there is serious image distortion. But by applying correction calculation only for the position of spot lightings not whole image pixels, we can decrease the processing time. And then using point pattern matching and least square estimation, finally we can get the precise position and orientation of the mobile robot. Experimental results demonstrate the accuracy and update rate of the proposed algorithm in real environments.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.9
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• pp.570-577
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• 2014

Range-based real time localization systems require superior localization techniques as well as accurate ranging algorithms for better performance. To evaluate the ranging accuracy between two nodes in practical environments, this paper does not only present a qualitative analysis by computing a distance equation under SDS-TWR measurement model of no symmetry assumption, but also executes a quantitative evaluation by doing experiments after building up a test network employing the developed sensor node. Experimental results show that the ranging accuracy of the proposed implementation of IEEE 802.15.4a software stack is superior with smaller average error rate by 60% to one of the commercial Nanotron's reference development kit.

• KIISE Transactions on Computing Practices
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• v.22 no.3
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• pp.145-150
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• 2016

Finding the exact location of faults in a program requires enormous time and effort. Several fault localization methods based on control flows of a program have been studied for decades. Unfortunately, these methods are not applicable to programs based on data-flow languages. A recently proposed mutation-based fault localization method is applicable to data-flow languages, as well as control-flow languages. However, there are no studies on the effectiveness of the mutation-based fault localization method for data-flow based programs. In this paper, we provided an experimental case study to evaluate the effectiveness of mutation-based fault localization on programs implemented in Function Block Diagram (FBD), a widely used data-flow based language in safety-critical systems implementation. We analyzed several real faults in the implementation of FBD programs of a nuclear reactor protection system, and evaluated the mutation-based fault localization effectiveness for each fault.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.4940-4957
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• 2019

Compared with the localization methods in the static sensor networks, node localization in dynamic sensor networks is more complicated due to the mobility of the nodes. Dynamic Sampling Localization Algorithm Based on Virtual Anchor (DSLA) is proposed in this paper to localize the unknown nodes in dynamic sensor networks. Firstly, DSLA algorithm predicts the speed and movement direction of nodes to determine a sector sampling area. Secondly, a method of calculating the sampling quantity with the size of the sampling area dynamically changing is proposed in this paper. Lastly, the virtual anchor node, i.e., the unknown node that got the preliminary possible area (PLA), assists the other unknown nodes to reduce their PLAs. The last PLA is regarded as a filtering condition to filter out the conflicting sample points quickly. In this way, the filtered sample is close to its real coordinates. The simulation results show that the DSLA algorithm can greatly improve the positioning performance when ensuring the execution time is shorter and the localization coverage rate is higher. The localization error of the DSLA algorithm can be dropped to about 20%.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.2
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• pp.132-151
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• 2022

Vehicle localization is one of the core technologies for autonomous driving. Image-based localization provides location information efficiently, and various related studies have been conducted. However, the image-based localization methods using feature points or lane information has a limitation that positioning accuracy may be greatly affected by road and driving environments. In this study, we propose a line segment matching framework for accurate vehicle localization. The proposed framework consists of four steps: line segment extraction, merging, overlap area detection, and MSLD-based segment matching. The proposed framework stably performed line segment matching at a sufficient level for vehicle positioning regardless of vehicle speed, driving method, and surrounding environment.