• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.53 no.3
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• pp.256-265
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• 2017

Seafood is attracting attention as a future food industry. In recent years, the demand for fishery equipment of mechanization, automation, and unmanned was increased due to the environment affected by seafood processing, stricter regulations on safety, decline and aging of fishery worker. Ark shell (Scapharca subcrenata) was being produced in many steps in the production process. The process has been made such as collection-landing-washing-first sort (goods/non-goods)-transports-second sort (size). It was undergone first and second steps by delivering to the consumer. Here, the first step is to sort goods to collection and the second step is to sort by size. The fishery workers need ten people in first step and six people in second step. The workload of one hour per kg is 4,247 kg/h in first step and 2,213 kg/h in second step. In addition, the goods ratio by work process was 79% in first step and 98% in the second step. In this process, a lot of fishery worker and working time is needed. Therefore, this study developed elemental techniques for an automated size sorting system considering the working process problem, time and situation for washing and sorting of ark shell.

• IE interfaces
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• v.14 no.3
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• pp.247-254
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• 2001

As current postal automation is limited to dispatch and arrival sorting, delivery sequence sorting is performed manually by each postman. It not only acts as a bottleneck process in the overall mailing process but is expensive operation. To cope with this problem effectively, delivery sequence sorting automation is required. The important components of delivery sequence sorting automation system are sequence sorter and Hangul OCR which function is to extract the address of delivery point. DSI database will be interfaced to both Hangul OCR and sequence sorter for finding the accurate delivery sequence number and stacker number. The objectives of this research are to develop DSI(Delivery Sequence Information) database prototype and client application for managing information effectively. For database requirements collection and analysis, we draw all possible sorting plans, and apply the AHP(Analytic Hierarchy Process) method to determine the optimal one. And then, we design DSI database schema based on the optimal one and implement it using Oracle RDBMS. In addition, as address information in DIS database consist of hierarchical structure which has its correspondence sequence number, so it is important to reorganize sequence information accurately when address information is inserted, deleted or updated. To increase delivery accuracy, we reflect this point in writing application.

• Journal of Communications and Networks
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• v.15 no.5
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• pp.486-495
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• 2013

In this article, we suggest a two-dimensional genetic algorithm (GA) method that applies a cognitive radio (CR) decision engine which determines the optimal transmission parameters for multicarrier communication systems. Because a CR is capable of sensing the previous environmental communication information, CR decision engine plays the role of optimizing the individual transmission parameters. In order to obtain the allowable transmission power of multicarrier based CR system demands interference analysis a priori, for the sake of efficient optimization, a two-dimensionalGA structure is proposed in this paper which enhances the computational complexity. Combined with the fitness objective evaluation standard, we focus on two multi-objective optimization methods: The conventional GA applied with the multi-objective fitness approach and the non-dominated sorting GA with Pareto-optimal sorting fronts. After comparing the convergence performance of these algorithms, the transmission power of each subcarrier is proposed as non-interference emission with its optimal values in multicarrier based CR system.

• Journal of the Korean Society of Visualization
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• v.6 no.2
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• pp.20-27
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• 2008

The color sorting technique utilizing the image processing method is very applicable tool to analyze motion of a free-falling object in many agricultural and industrial research fields. In the present study, we have developed an image processing system and algorithm to sort good quality rice grains effectively from the bad ones. The system employs a high speed rate line-scan CCD camera with 2K-pixels and worked with a high speed DSP and FPGA in-line. It can accumulate acquired line-scan image data and visualize each grain image clearly. As a result, we can easily calculate the number of pixels occupied by grain(=grain size), gray level and its correct position by visualizing grain images rapidly.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.1339-1343
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• 2004

This paper presents a development of the control algorithm and Pick & Placer. The Pick & Placer provides a powerful multi-task system that includes both graphical and remote interface. Users can easily set up sorting parameters and record important data including wafer number, data, and operator information. This System sets up a dustproof device and massively machined components to provide an extremely stable sorting environment. Precise resolution and accuracy result from using machine vision, a pneumatic slide drive and close -looped positioning.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.6
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• pp.3023-3031
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• 2013

In this paper, we propose a robot control data generation system applying context aware mechanism in order to control the robot manipulator which automatically sorts parcels. The context aware mechanism generates intelligent information to control a robot using context data such as the parcel shape, weight, location and barcodes. The proposed system collects context data of the parcel and generates robot control data to pick up and drop parcels. Then a robot manipulator, which receives control data of picking-up and dropping, processes the automated sorting of parcels according to delivery persons and delivery routes. It will contribute not only to save much time and cost but also to reduce the industrial accidents.

• Agricultural and Biosystems Engineering
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• v.4 no.1
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• pp.8-15
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• 2003

In Korea, quality evaluation of dried oak mushrooms are done first by classifying them into more than 10 different categories based on the state of opening of the cap, surface pattern, and colors. And mushrooms of each category are further classified into 3 or 4 groups based on its shape and size, resulting into total 30 to 40 different grades. Quality evaluation and sorting based on the external visual features are usually done manually. Since visual features of mushroom affecting quality grades are distributed over the entire surface of the mushroom, both front (cap) and back (stem and gill) surfaces should be inspected thoroughly. In fact, it is almost impossible for human to inspect every mushroom, especially when they are fed continuously via conveyor. In this paper, considering real time on-line system implementation, image processing algorithms utilizing artificial neural network have been developed for the quality grading of a mushroom. The neural network based image processing utilized the raw gray value image of fed mushrooms captured by the camera without any complex image processing such as feature enhancement and extraction to identify the feeding state and to grade the quality of a mushroom. Developed algorithms were implemented to the prototype on-line grading and sorting system. The prototype was developed to simplify the system requirement and the overall mechanism. The system was composed of automatic devices for mushroom feeding and handling, a set of computer vision system with lighting chamber, one chip microprocessor based controller, and pneumatic actuators. The proposed grading scheme was tested using the prototype. Network training for the feeding state recognition and grading was done using static images. 200 samples (20 grade levels and 10 per each grade) were used for training. 300 samples (20 grade levels and 15 per each grade) were used to validate the trained network. By changing orientation of each sample, 600 data sets were made for the test and the trained network showed around 91 % of the grading accuracy. Though image processing itself required approximately less than 0.3 second depending on a mushroom, because of the actuating device and control response, average 0.6 to 0.7 second was required for grading and sorting of a mushroom resulting into the processing capability of 5,000/hr to 6,000/hr.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.25 no.5
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• pp.329-336
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• 2015

This paper presents a sorting method of mode vectors and natural frequencies about a rotor-journal bearing system. The rotor is solved by the finite element method, the bearing stiffness and damping coefficient are solved by the finite difference method. At any rotation speed section through the eigenvalue analysis of the system, mode vectors and natural frequencies not sorted are confirmed via the Campbell diagram and the MAC(modal assurance criterion). To sort mode vectors and natural frequencies of the section, a mode sorting method is presented through a method of rearranging the MAC of the mode vectors. Finally, the mode vectors and the natural frequencies are sorted by using the presented method, these are verified through the MAC.

• Journal of the Korean Society for Nondestructive Testing
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• v.35 no.1
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• pp.1-11
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• 2015

In this study, an online seed potato sorting system using a visible and near infrared (40 1100 nm) transmittance spectral technique and statistical model was evaluated for the nondestructive determination of infected and sound seed potatoes. Seed potatoes that had been artificially infected with Pectobacterium atrosepticum, which is known to cause a soil borne disease infection, were prepared for the experiments. After acquiring transmittance spectra from sound and infected seed potatoes, a determination algorithm for detecting infected seed potatoes was developed using the partial least square discriminant analysis method. The coefficient of determination($R^2_p$) of the prediction model was 0.943, and the classification accuracy was above 99% (n = 80) for discriminating diseased seed potatoes from sound ones. This online sorting system has good potential for developing a technique to detect agricultural products that are infected and contaminated by pathogens.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1993.10a
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• pp.1243-1253
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• 1993

Visual features of a mushroom(Lentinus Edodes L) are critical in sorting and grading as most agricultural products are. Because of its complex and various visual features, grading and sorting of mushrooms have been done manually by the human expert. Though actions involved in human grading looks simple, a decision making undereath the simple action comes form the results of the complex neural processing of the visual image. And processing details involved in the visual recognition of the human brain has not been fully investigated yet. Recently, however, an artificial neural network has drawn a great attention because of its functional capability as a partial substitute of the human brain. Since most agricultural products are not uniquely defined in its physical properties and do not have a well defined job structure, a research of the neuro-net based human like information processing toward the agricultural product and processing are widely open and promising. In this pape , neuro-net based grading and sorting system was developed for a mushroom . A computer vision system was utilized for extracting and quantifying the qualitative visual features of sampled mushrooms. The extracted visual features and their corresponding grades were used as input/output pairs for training the neural network and the trained results of the network were presented . The computer vision system used is composed of the IBM PC compatible 386DX, ITEX PFG frame grabber, B/W CCD camera , VGA color graphic monitor , and image output RGB monitor.