In the anchovy boat seine fishing boat, it is necessary to select other aquatic organisms other than live anchovies, which are the target species of catch. By making a rotating roller sorter using hydraulic pressure, the anchovy sorting amount was compared and the sorting accuracy of the rotary roller sorter, and the discharge speed of butter fish and jerry fish according to the number of roller revolutions were analyzed. The rotating roller sorter increases the weight of the sorted raw anchovy by 54%, 74% and 91.5% compared to the round bar fixed type, so it can reduce the required time by an average of 73.2%. As a result of converting the sorting accuracy to the weight of pure anchovies excluding the catch weight, the round bar fixed type was 89%; however, the average of the rotating roller sorter was 97.7%. Thus, the sorting accuracy of the rotary roller sorter was further improved by about 8.7%. The roller speed moved 7% at 300 rpm, 7.5% at 600 rpm, and 16% at 900 rpm, so butter fish were discharged overboard 10% faster than jelly fish on average. In addition, the average feed speed of butter fish and jelly fish is 1,400 mm/s when the roller rotation speed is 300 rpm, 1,480 mm/s at 600 rpm, and 1,850 mm/s at 900 rpm. A Φ58 mm roller rotates once it moved about 1.23 mm. In the future, a follow-up study of quantitative evaluation is needed targeting more non-target fish species of anchovy boat seine.
Recently, transfer learning techniques with a base convolutional neural network (CNN) model have widely gained acceptance in early detection and classification of crop diseases to increase agricultural productivity with reducing disease spread. The transfer learning techniques based classifiers generally achieve over 90% of classification accuracy for crop diseases using dataset of crop leaf images (e.g., PlantVillage dataset), but they have ability to classify only the pre-trained diseases. This paper provides with an evaluation scheme on selecting an effective base CNN model for crop disease transfer learning with regard to the accuracy of trained target crops as well as of untrained target crops. First, we present transfer learning models called CDC (crop disease classification) architecture including widely used base (pre-trained) CNN models. We evaluate each performance of seven base CNN models for four untrained crops. The results of performance evaluation show that the DenseNet201 is one of the best base CNN models.
Collaborative filtering, one of the most widely used techniques to build recommender systems, is based on the idea that users with similar preferences can help one another find useful items. Credit card user behavior analytics show that most customers hold three or less credit cards without duplicates. This behavior is one of the most influential factors to data sparsity. The ‘cold-start’ problem caused by data sparsity prevents recommender system from providing recommendation properly in the personalized credit card recommendation scenario. We propose a personalized credit card recommender system to address the cold-start problem, using multiple user profiles. The proposed system consists of a training process and an application process using five user profiles. In the training process, the five user profiles are transformed to five user networks based on the cosine similarity, and an integrated user network is derived by weighted sum of each user network. The application process selects k-nearest neighbors (users) from the integrated user network derived in the training process, and recommends three of the most frequently used credit card by the k-nearest neighbors. In order to demonstrate the performance of the proposed system, we conducted experiments with real credit card user data and calculated the F1 Values. The F1 value of the proposed system was compared with that of the existing recommendation techniques. The results show that the proposed system provides better recommendation than the existing techniques. This paper not only contributes to solving the cold start problem that may occur in the personalized credit card recommendation scenario, but also is expected for financial companies to improve customer satisfactions and increase corporate profits by providing recommendation properly.
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.
\멍게껍질에 함유된 기능성 물질을 효소 및 열수로 추출하여 면역활성 및 항암 효과를 평가하였다. 세포독성을 검정하기 위하여 RAW 264.7 세포주에 추출물을 10∼200 ㎍/㎖ 농도로 처리한 결과 독성을 나타내지 않았다. 따라서 다양한 농도에서 효소 및 열수 추출물의 생리기능성을 평가하기 위하여 NO생성량을 측정한 결과 열수 추출물과 효소 추출물은 높은 NO 생성량을 보였다(123.0~161.7%). 위암 및 대장암 억제효과를 파악하기 위하여 다양한 농도에서 평가하였지만 최대 200 ㎍/㎖ 농도에서도 위암세포 AGS와 대장암세포 HT-29의 세포생장 억제에는 영향을 미치지 못하였다.