• International conference on construction engineering and project management
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• 2009.05a
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• pp.242-249
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• 2009

High-rise construction sites, especially those situated in spatially constrained urban areas, have difficulties in timely delivery of materials. Modern techniques such as Just-in-time delivery, and use of information technology such as Project Management Information System (PMIS), are targeted to improve the efficiency of the logistics. Such IT-driven management techniques can be further benefited from state-of-the-art devices such as Radio Frequency Identification (RFID) tags and Ubiquitous Sensor Networks (USN), which has resulted in notable achievements in automated logistics management at the construction sites. Based on those achievements, this research develops USN hardware toolkits for construction lifts, which aims to be automated the vertical material delivery by sensing the material information and routing it automatically to the right place. The gathered information from the sensors can also be used for monitoring the overall status. The developed system will be tested in the actual high-rise construction sites to assess the system's feasibility. The proposed system is being implemented using Zigbee communication modules and RFID sensor networks which will communicate with the intelligent palette system (previously developed by the authors). To support the system, a lift-mountable intelligent toolkit is under development. Its feasibility test will be conducted by applying the implemented system to a test bed and then analyzing efficiency of the system and the toolkit. The collected test data will be provided as a basis of autonomous vertical transport equipment development. From this research, efficient management of the material lift is expected with increased accuracy, as well as better management of overall construction schedule benefited from the system. Further research will be expected to develop a smart construction lift, which will eliminate the need for human supervision, thus enabling a real 'autonomous' operation of the system.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.1
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• pp.101-110
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• 2004

The robots that will be needed in the near future are human-friendly robots that are able to coexist with humans and support humans effectively. To realize this, humans and robots need to be in close proximity to each other as much as possible. Moreover, it is necessary for their interactions to occur naturally. It is desirable for a robot to carry out human following, as one of the human-affinitive movements. The human-following robot requires several techniques: the recognition of the moving objects, the feature extraction and visual tracking, and the trajectory generation for following a human stably. In this research, a predictable intelligent space is used in order to achieve these goals. An intelligent space is a 3-D environment in which many sensors and intelligent devices are distributed. Mobile robots exist in this space as physical agents providing humans with services. A mobile robot is controlled to follow a walking human using distributed intelligent sensors as stably and precisely as possible. The moving objects is assumed to be a point-object and projected onto an image plane to form a geometrical constraint equation that provides position data of the object based on the kinematics of the intelligent space. Uncertainties in the position estimation caused by the point-object assumption are compensated using the Kalman filter. To generate the shortest time trajectory to follow the walking human, the linear and angular velocities are estimated and utilized. The computer simulation and experimental results of estimating and following of the walking human with the mobile robot are presented.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.64-88
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• 2024

Security and reliability are the utmost importance facts in intelligent networked vehicles. Stochastic Petri Net and Z (SPZN) as an excellent formal verification tool for modeling concurrent systems, can effectively handles concurrent operations within a system, establishes relationships among components, and conducts verification and reasoning to ensure the system's safety and reliability in practical applications. However, the application of a system with numerous nodes to Petri Net often leads to the issue of state explosion. To tackle these challenges, a refinement and abstraction method based on SPZN is proposed in this paper. This approach can not only refine and abstract the Stochastic Petri Net but also establish a corresponding relationship with the Z language. In determining the implementation rate of transitions in Stochastic Petri Net, we employ the interval average and weighted average method, which significantly reduces the time and space complexity compared to alternative techniques and is suitable for expert systems at various levels. This reduction facilitates subsequent comprehensive system analysis and module analysis. Furthermore, by analyzing the properties of Markov Chain isomorphism in the case study, recommendations for minimizing system risks in the application of intelligent parking within the intelligent networked vehicle system can be put forward.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11a
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• pp.973-975
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• 2005

With the continuous growth of e-commerce there is a huge amount of products information available online. Shop managers expect to apply information techniques to increase profit and perfect service. Hence many e-commerce systems use association rule mining to further refine their management. However previous association rule algorithms have two limitations. Firstly, they only use the number to weight item's essentiality and ignore essentiality of item profit. Secondly, they did not consider the relationship between number and profit of item when they do mining. We address a novel algorithm, profit-based association rule algorithm that uses profit-based technique to generate 1-itemsets and the multiple minimum supports mining technique to generate N-items large itemsets.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.2
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• pp.122-129
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• 2014

Effective information gathering and retrieval of the most relevant web documents on the topic of interest is difficult due to the large amount of information that exists in various formats. Current information gathering and retrieval techniques are unable to exploit semantic knowledge within documents in the "big data" environment; therefore, they cannot provide precise answers to specific questions. Existing commercial big data analytic platforms are restricted to a single data type; moreover, different big data analytic platforms are effective at processing different data types. Therefore, the development of a common big data platform that is suitable for efficiently processing various data types is needed. Furthermore, users often possess more than one intelligent device. It is therefore important to find an efficient preference profile construction approach to record the user context and personalized applications. In this way, user needs can be tailored according to the user's dynamic interests by tracking all devices owned by the user.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.676-678
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• 2003

Microtunneling techniques play a crucial role in the construction of pipelines. This paper shows the automatic tunneling algorithm of microtunneling system using fuzzy logic technology to assist operators to assure the quality of microtunneling construction. To have effective output value of fuzzy controller, we slightly modified the conventional defuzzification methods. The proposed automatic tunneling algorithm shows good tunneling results comparable with those of experts.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.470-472
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• 2006

Sensor networks are the results of convergence of very important technologies such as wireless communication and micro electromechanical systems. In recent years, sensor networks found a wide applicability in various fields such as environment and health, industry scene system monitoring, etc. A very important step for these many applications is pattern classification and recognition of data collected by sensors installed or deployed in different ways. But, pattern classification and recognition are sometimes difficult to perform. Systematic approach to pattern classification based on modem learning techniques like Multivariate Gaussian mixture models, can greatly simplify the process of developing and implementing real-time classification models. This paper proposes a new recognition system which is hierarchically composed of many sensor nodes having the capability of simple processing and wireless communication. The proposed system is able to perform context classification of sensed data using the Multivariate Gaussian function. In order to verify the usefulness of the proposed system, it was applied to intelligent dust collecting system.

• Proceedings of the KSR Conference
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• 2004.06a
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• pp.494-500
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• 2004

The purpose of this thesis is to know and understand the user requirements to provide with intelligent railway information service in response to the Ubiquitous environment. The railroad specially has a disadvantage of door-to-door service, however by using the information and communication techniques, we can provide the passengers who are using the intermodal transportation means with the realtime information, therefore we can overcome the above disadvantage. To deduce the passenger's needs for providing the future-type railway information service, we introduce the results of the questionnaire. In chapter 2, we studies on the concepts of intelligent railway information service, and in chapter 3, we investigate the cases of Japan and China, and in chapter 4, we study the passager's satisfaction by the questionnaire concerning the railway information service and suggest the needs for information offering.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.3
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• pp.1722-1741
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• 2017

Intrusion detection techniques based on virtual machine introspection (VMI) provide high temper-resistance in comparison with traditional in-host anti-virus tools. However, the presence of semantic gap also leads to the performance and compatibility problems. In order to map raw bits of hardware to meaningful information of virtual machine, detailed knowledge of different guest OS is required. In this work, we present VDSM, a lightweight and general approach based on driver separation mechanism: divide semantic view reconstruction into online driver of view generation and offline driver of semantics extraction. We have developed a prototype of VDSM and used it to do intrusion detection on 13 operation systems. The evaluation results show VDSM is effective and practical with a small performance overhead.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.21-25
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• 2004

This paper suggests the techniques in determining the values of the steady-state equivalent circuit parameters of a three-phase squirrel-cage induction machine using immune algorithm. The parameter estimation procedure is based on the steady state phase current versus slip and input power versus slip characteristics. The proposed estimation algorithm is of a nonlinear kind based on clonal selection in immune algorithm. The machine parameters are obtained as the solution of a minimization of least-squares cost function by immune algorithm. Simulation shows better results than the conventional approaches.