• International Journal of Advanced Culture Technology
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• v.2 no.2
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• pp.15-18
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• 2014

This paper presents a sensor location decision making method respect to Container-Grown Seedlings in view of precision agriculture (PA) when sensors involved in tree container measure received signal strength (RSS) or time-of-arrival (TOA) between themselves and neighboring sensors. A small fraction of sensors in the container-grown seedlings system have a known location, whereas the remaining locations must be estimated. We derive Rao-Cramer bounds and maximum-likelihood estimators under Gaussian and log-normal models for the TOA and RSS measurements, respectively.

• Proceedings of the Korea Information Processing Society Conference
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• 2001.04a
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• pp.263-266
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• 2001

상호 연결망에서 임의의 두 노드 사이에 존재하는 노드 중복 없는 경로들의 집합을 Container라고 하는데, 본 논문에서는 계층적 하이퍼큐브 연결망의 Container가 n+1임을 보이고, 그 결과를 통하여 계층적 하이퍼큐브 연결망의 고장지름이 dia(HCN(n,n))+4 이하임을 보인다. 이러한 Container는 노드간에 메시지를 전송하는 시간을 줄일 수 있으며, 연결망의 노드 몇 개가 고장이 발생해도 통신지연시간이 발생하지 않음을 의미한다.

• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.57-65
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• 2022

International attempts to smuggle narcotics and explosives using ship or aircraft cargoes are on the rise. With the recent increase in the number of detection cases of narcotics and explosives in Korea, it is important to detect dangerous material (narcotics and explosives) through container searches at ports and airports, which are the main routes. This paper proposes a technique to detect dangerous material in cargo containers using the sampled output signal of ion mobility spectroscopy (IMS). The proposed technique estimates parameters such as a threshold, a window length, and a noise level for ion detection of the target dangerous material by using known materials in the initialization stage. The estimated parameters are used to detect the ions of the dangerous target material inside the containers. The proposed technique can be applied when the peak value of the IMS signal and the ion mobility are varying due to container environments.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1327-1332
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• 2003

In this paper, an ATC(Automated Transfer Crane) control system is required rapid transportation to get highest productivity with low cost. Therefore, the container paths should be built in terms of the least time and least sway when container is transferred from the initial coordinate to the finial coordinate. So we applied the best-first search method for forming the container path, and calculated the anti-collision path for avoiding collision in its movement to the finial coordinate. And we constructed the neural network two degree of freedom PID (TDOFPID) controller to control the precise navigation. For simulation, we constructed the container profiles so that we analyzed the state of formed path and the performance of TDOFPID controller to the formatted path. Then we compared the performance of ES-tuned PID controller with our proposed controller in terms of trolley position, anti-sway, path change, disturbance, and the load of containers. The computer simulation results show that the proposed controller has better the other on the various conditions.

• Journal of Navigation and Port Research
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• v.28 no.6
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• pp.549-555
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• 2004

The purpose of this study is to estimate the required number of yard tractor on port container terminal. The number of yard tractor is the bottleneck factor on the efficiency of container terminal. Due to the change in travel speed and travel distance, the efficiency is difficult to estimate. The efficiency of yard tractor is estimated by the proposed simulation model that developed considering the queueing network between container crane and transfer crane. The number of yard tractor per container crane is estimated by the alternative analysis. And to determine the number of yard tractor per container crane, the performance measure such as the distance between berth and yard, the speed of yard tractor are simulated.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.721-723
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• 2007

Recently, the size and location of the acquired container image while the container is loading and unloading in Harbors is not fixed. And it is difficult to get a good image for recognition because of the variation of external environment as those the size of container and where the yard-tractor stop is. In this paper, we estimate where the container ISO-code set is using Top-hat transform from realtime images and get an image to recognize container ISO-code using PAN/TILT/ZOOM camera. We extract the container ISO-code using Top-hat transform and Histogram projection. After binarization, we extract each character from complex background using labeling. We use BP(Backpropagation Network) to recognize extracted characters.

• Journal of Power System Engineering
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• v.12 no.6
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• pp.18-23
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• 2008

This paper describes development of real-time condition monitoring system to observe state of a container crane in a port. To analyze the state of a crane, the strength and the direction of wind are measured with sensors along with the load resulted a crane at the moment. The measured signals are processed by especially developed conditioning board and converted into digital data. Measured data are analyzed to define the state of the crane at an indicator. For transmission of these data to the indicator, we implemented wireless sensor network based on IEEE 802.15.4 MAC(Media Access Control) protocol and Bluetooth network protocol. To extend the networking distance between the indicator and sensor nodes, the shortest path routing algorithm was applied for WSN(Wireless Sensor Network) networks. The indicator sends the state information of the crane to monitoring server through IEEE 802.11 b wireless LAN(Local Area Network). Monitoring server decides whether alarm should be issued or not. The performance of developed WSN and Bluetooth network were evaluated and analyzed in terms of communication delay and throughput.

• Korean Management Science Review
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• v.17 no.2
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• pp.87-95
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• 2000

This paper presents two analytical approaches to determine the vehicle fleet size for container shuttle service. The shuttle service can be defined as the repetitive travel between the designated places during working period. In the first approach, the transportation model is adopted in order to determine the number of vehicles required. Its advantages and disadvantages in practical application are also discussed. In the second approach, a logical network which is oriented on job is transformed from a physical network which is focused on demand site. Nodes on the logical network represent jobs which include loaded travel, loading and unloading and arcs represent empty travel for the next jobs which include loaded travel, loading and unloading and arcs represent empty travel for the next job. Then a mathematical formulation is constructed similar to the multiple traveling salesman problem (TSP). A solution procedure is carried out based on the well-known insertion heuristic with the real world data.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1907-1908
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• 2006

Recently, an automatic crane control system is required with high speed and rapid transportation. Therefore, when container is transferred from the initial coordinate to the finial coordinate, the container paths should be built in terms of the least time and without sway. Therefore, we calculated the anti-collision path for avoiding collision in its movement to the finial coordinate in this paper. And we constructed the neural network predictive two degree of freedom PID controller to control the precise navigation. The proposed predictive control system is composed of the neural network predictor, two degree of freedom PID controller, neural network self-tuner which yields parameters of two degree of freedom PID. We analyzed crane system through simulation, and proved excellency of control performance over the conventional controllers.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.6
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• pp.1162-1169
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• 2007

In this paper, we proposed a container identifier recognition method for containers used in harbors. After converting a real container image to a gray image, edges are detected from the gray image applying Prewitt mask and candidate identifier area is extracted using morphological features of individual identifier for identifying containers. Because noises are included in the extracted candidate identifier area, noises are eliminated and each identifier is separated using 4-directional edge tracking algorithm and Grassfire algorithm. Each identifier in the noise-free candidate identifier area is recognized using FCM-based row RBF network for discriminating containers. We used 300 real container images for experiment to evaluate the performance of the proposed method, and we could verify the proposed method is better than a conventional method.