• International Journal of Computer Science & Network Security
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• 제24권6호
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• pp.89-98
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• 2024

Due to the increase in population and consequently the increase in the production of plastic waste, recovery of this part of the waste is an undeniable necessity. On the other hand, the recycling of plastic waste, if it is placed in a systematic process and controlled, can be effective in creating jobs and maintaining environmental health. Waste collection in many large cities has become a major problem due to lack of proper planning with increasing waste from population accumulation and changing consumption patterns. Today, waste management is no longer limited to waste collection, but waste collection is one of the important areas of its management, i.e. training, segregation, collection, recycling and processing. In this study, a systematic method based on machine vision for sorting plastic bottles in different colors for recycling purposes will be proposed. In this method, image classification and segmentation techniques were presented to improve the performance of plastic bottle classification. Evaluation of the proposed method and comparison with previous works showed the proper performance of this method.

• Agricultural and Biosystems Engineering
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• 제4권1호
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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.

• 한국통신학회논문지
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• 제16권11호
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• pp.1143-1153
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• 1991

본 논문은 입력과 출력 사이에 다중경로가 제공될 수 있는 GAMMA 네트워크를 이용한 새로운 ATM 스위치의 구조를 제안한 것이다. 제안된 구조는 BANYAN 네트워크를 기본으로 한 여타 ATM 스위치와는 달리 블러킹 문제 해결을 위한 Sorting 네트워크의 필요성을 배제시킨 구조로서 전체 스위치 구조가 축소될 수 있는 스위치이다 또한 시뮬레이션 기법에 의한 제안된 스위치의 성능 평가를 통해 본 연구의 타당성과 그 효율성을 검증하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.275-280
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• 1990

A neural network method is applied for recognizing the orientation o f individual parts being fed from a bowl feeder. The system is designed in such a way that a part can be discriminated and sorting according to every possible stable orientation without implementing any a mechanical tooling. The operation of the bowl feeder is based on a 2D image obtained from an array of fiber optic sensor located on the feeder track. The acquired binary image of a moving and vibrating part is used as input to a neural network which, in turn, determines t he orientation of the part. The main task of the neural network, here is to synthesize the appropriate internal discriminant functions for the part orientation using the part features. A series of the experiments reveals several promising points on performance. Since the operation of the feeder is highly programmable, it is well suited for feeding and sorting small parts prior to small batch assembly work.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 1993년도 Proceedings of International Conference for Agricultural Machinery and Process Engineering
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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.

• Journal of Biosystems Engineering
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• 제26권2호
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• pp.147-154
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• 2001

In Korea and Japan, dried oak mushrooms are classified into 12 to 16 different categories based on its external visual quality. And grading used to be done manually by the human expert and is limited to the randomly sampled oak mushrooms. Visual features of dried oak mushrooms dominate its quality and are distributed over both sides of the gill and the cap. The 2nd prototype computer vision based automatic grading and sorting system for dried oak mushrooms was developed based on the 1st prototype. Sorting function was improved and overall system for grading was simplified to one stage grading instead of two stage grading by inspecting both front and back sides of mushrooms. Neuro-net based side(gill or cap) recognition algorithm of the fed mushroom was adopted. Grading was performed with both images of gill and cap using neural network. A real time simultaneous discharge algorithm, which is good for objects randomly fed individually and for multi-objects located along a series of discharge buckets, was developed and implemented to the controller and the performance was verified. Two hundreds samples chosen from 10 samples per 20 grade categories were used to verify the performance of each unit such as feeding, reversing, grading, and discharging unites. Test results showed that success rates of one-line feeding, reversing, grading, and discharging functions were 93%, 95%, 94%, and 99% respectively. The developed prototype revealed successful performance such as the approximate sorting capability of 3,600 mushrooms/hr per each line i.e. average 1sec/mushroom. Considering processing time of approximate 0.2 sec for grading, it was desired to reduce time to reverse a mushroom to acquire the reversed surface image.

• 한국통신학회논문지
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• 제28권4B호
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• pp.282-287
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• 2003

통신망 스위칭 장치들을 상호 연결하여 구성한다. 스위칭 장치는 경로배정 정보의 의하여 입력포트와 출력포트 사이를 연결하는 역할을 한다. 그런데 반얀 망을 사용하는 대부분의 스위치 망은 두개의 셀 들이 같은 길을 사용하려고 시도할 때 내부 충돌이 발생한다. 본 논문은 입력과 출력사이에 목적지가 같은 두 개의 셀 들이 충돌이 없도록 동시에 두 개 경로를 설정하여 셀 처리율을 향상시키는 개선된 정렬 반얀 망을 제안하고 설계하였다. 하드웨어 설계는 2개의 $4{\times}4$ 정렬 부 블록들과 1개의 ${8\times}8$ 스위칭 망 블록으로 구성하였는데 기존의 정렬망인 베쳐 반얀 망보다 스위치의 셀 처리능력은 4% 향상되었고 정렬 부의 하드웨어 복잡도는 반으로 감소하였다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2002년도 추계학술발표논문집 (상)
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• pp.377-380
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• 2002

신경회로망은 문자인식, 자동제어 등의 여러 분야에 널리 쓰이는 방식이다. 그러나 신경회로망을 구현하는데는 연산량이 많아서 실시간으로 구현하기에 어려움이 많이 따른다. 본 논문은 신경회로망을 구현하는데 필요한 연산을 살펴보고 그 연산을 구현하는 방법을 비교 분석하였다. 신경회로망을 구현하기 위해 DSP(Digital Signal Processor), PC의 FPU(Floating Point Unit), Intel사의 Pentium 계열 프로세서에서 지원하는 SIMD(Single Instruction Multiple Data) 기술을 사용하여 결과를 비교 분석 하였다. 신경회로망의 핵심인 MLP(Multi Layer Perceptron) 연산에 대해 실험한 결과 SIMD 기술을 이용하는 방법이 다른 방법에 비해 2배이상 좋은 결과를 나타내었다.

• Journal of Information Technology Applications and Management
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• 제9권4호
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• pp.13-34
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• 2002

This paper considers a postal transportation planning problem in the transportation network of the form of hub and spoke Given mail sorting centers and an exchange center, available vehicles and amount of mails to be transported between mail sorting centers, postal transportation planning is to make a transportation plan without violating various restrictions. The objective is to minimize the total transportation cost. To solve the problem, a tabu search algorithm is proposed. The algorithm is composed of a route construction procedure and a route improvement procedure to improve a solution obtained by the route construction procedure using a tabu search. The tabu search uses the best-admissible strategy, BA, and the first-best-admissible strategy, FBA. The algorithm was tested on problems consisting of 11, 16 and 21 mail sorting centers including one exchange center. Solutions of the problems consisting of 11 mail sorting centers including one exchange center were compared with optimal solutions On average, solutions using BA strategy were within 0.287% of the optimum and solutions using FBA strategy were within 0.508% of the optimum. Computational results show that the proposed algorithm can solve practically sized problems within a reasonable time and the quality of the solution is very good.

• ETRI Journal
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• 제35권5호
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• pp.748-756
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• 2013

This paper addresses the problem of online hop timing detection and frequency estimation of multiple frequency-hopping (FH) signals with antenna arrays. The problem is deemed as a dynamic one, as no information about the hop timing, pattern, or rate is known in advance, and the hop rate may change during the observation time. The technique of particle filtering is introduced to solve this dynamic problem, and real-time frequency and direction of arrival estimates of the FH signals can be obtained directly, while the hop timing is detected online according to the temporal autoregressive moving average process. The problem of network sorting is also addressed in this paper. Numerical examples are carried out to show the performance of the proposed method.