• Journal of Biosystems Engineering
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• 제34권6호
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• pp.425-433
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• 2009

The developed final shattering machine for labor-saving mechanization of shattering of sesame consisted of input part, shattering part, re-shattering part for unshattered pod and pneumatic sorter. The bundle of sesame was held as upside down and fed into the machine continuously. Then, the fed bundle of sesame was shattered by side shock and agitation. The performance of shattering for the sun dried bundle of sesame of conventional manual work and final shattering machine was compared. Since the shattering ratio measured by the final machine was 97.2% at the first operation, in case of fully dried sesame by drying stand, the harvest of sesame can be completed by only one time shattering operation. The work hour per area of 10 a for the mechanical work and the manual work were 0.3 hour and 13.9 hour, respectively. The total shattering ratio of the final machine with vertical feedings of bundle of sesames was 97.2%.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2007년도 춘계학술대회 논문집
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• pp.749-750
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• 2007

• 산업경영시스템학회지
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• 제42권2호
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• pp.94-103
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• 2019

Self Organizing Map (SOM) is a neural network that is effective in classifying patterns that form the feature map by extracting characteristics of the input data. In this study, we propose an algorithm to determine the cell formation and the machine layout within the cell for the cell formation problem with operation sequence using the SOM. In the proposed algorithm, the output layer of the SOM is a one-dimensional structure, and the SOM is applied to the parts and the machine in two steps. The initial cell is formed when the formed clusters is grouped largely by the utilization of the machine within the cell. At this stage, machine cell are formed. The next step is to create a flow matrix of the all machine that calculates the frequency of consecutive forward movement for the machine. The machine layout order in each machine cell is determined based on this flow matrix so that the machine operation sequence is most reflected. The final step is to optimize the overall machine and parts to increase machine layout efficiency. As a result, the final cell is formed and the machine layout within the cell is determined. The proposed algorithm was tested on well-known cell formation problems with operation sequence shown in previous papers. The proposed algorithm has better performance than the other algorithms.

• Korean Circulation Journal
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• 제53권10호
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• pp.677-689
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• 2023

Background and Objectives: There is limited evidence regarding machine-learning prediction for the recurrence of atrial fibrillation (AF) after electrical cardioversion (ECV). This study aimed to predict the recurrence of AF after ECV using machine learning of clinical features and electrocardiograms (ECGs) in persistent AF patients. Methods: We analyzed patients who underwent successful ECV for persistent AF. Machine learning was designed to predict patients with 1-month recurrence. Individual 12-lead ECGs were collected before and after ECV. Various clinical features were collected and trained the extreme gradient boost (XGBoost)-based model. Ten-fold cross-validation was used to evaluate the performance of the model. The performance was compared to the C-statistics of the selected clinical features. Results: Among 718 patients (mean age 63.5±9.3 years, men 78.8%), AF recurred in 435 (60.6%) patients after 1 month. With the XGBoost-based model, the areas under the receiver operating characteristic curves (AUROCs) were 0.57, 0.60, and 0.63 if the model was trained by clinical features, ECGs, and both (the final model), respectively. For the final model, the sensitivity, specificity, and F1-score were 84.7%, 28.2%, and 0.73, respectively. Although the AF duration showed the best predictive performance (AUROC, 0.58) among the clinical features, it was significantly lower than that of the final machine-learning model (p<0.001). Additional training of extended monitoring data of 15-minute single-lead ECG and photoplethysmography in available patients (n=261) did not significantly improve the model's performance. Conclusions: Machine learning showed modest performance in predicting AF recurrence after ECV in persistent AF patients, warranting further validation studies.

• 산업경영시스템학회지
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• 제39권3호
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• pp.83-89
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• 2016

The group formation problem of the machine and part is a critical issue in the planning stage of cellular manufacturing systems. The machine-part grouping with alternative process plans means to form machine-part groupings in which a part may be processed not only by a specific process but by many alternative processes. For this problem, this study presents an algorithm based on self organizing neural networks, so called SOM (Self Organizing feature Map). The SOM, a special type of neural networks is an intelligent tool for grouping machines and parts in group formation problem of the machine and part. SOM can learn from complex, multi-dimensional data and transform them into visually decipherable clusters. In the proposed algorithm, output layer in SOM network had been set as one-dimensional structure and the number of output node has been set sufficiently large in order to spread out the input vectors in the order of similarity. In the first stage of the proposed algorithm, SOM has been applied twice to form an initial machine-process group. In the second stage, grouping efficacy is considered to transform the initial machine-process group into a final machine-process group and a final machine-part group. The proposed algorithm was tested on well-known machine-part grouping problems with alternative process plans. The results of this computational study demonstrate the superiority of the proposed algorithm. The proposed algorithm can be easily applied to the group formation problem compared to other meta-heuristic based algorithms. In addition, it can be used to solve large-scale group formation problems.

• 문화기술의 융합
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• 제8권6호
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• pp.637-646
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• 2022

본 논문은 한국어의 겹받침 발음에 대하여 베트남인 한국어 학습자와 한국인을 비교 연구하였다. 언어학적인 연구를 통하여 조사하고 분석한 겹받침 발음에 관한 여러 오류와 제시한 교육 방법에 대하여 공학적 특히 음성 신호처리의 분석 방법을 활용하여서 이런 연구 결과를 확인하였고 이에 우리는 본 논문에서 새로운 교육 방법을 제시하였다. 인공지능의 기계 학습에 많이 활용되고 있는 서포팅 벡터 머신 (supporting vector machine, SVM)을 사용하여서 베트남인 학습자의 발음과 한국인의 발음을 비교하였다. SVM의 초결정 평면을 구할 수 있다는 것은 베트남인 학습자의 겹받침 발음이 한국인의 발음과 차이를 보인다는 것이고, 그 반대라면 발음을 잘하고 있다는 것이다. 본 논문에서 우리가 제시한 새로운 교육 방법은 쓰기와 듣기로만 구성하는 것이 아닌 음성 신호의 시간 영역에서 파형과 그것에 대응하는 신호의 에너지 등과 같은 피교육자에게 보일 수 있는 것들을 포함하는 효율적인 발음 교육 방법이다.

• 대한기계학회논문집A
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• 제21권8호
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• pp.1339-1347
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• 1997

The automation of final inspection and tuning process in the manufacturing of electric products is hot issue now, because it is the only part that has not been wholey automized yet, mainly due to the difficulties to handle so small size of VR which is the final tuning point in the most of electric products. For the automation of this process, at first the accurate measurement of position and orientation of SMD VR on PCB in real time is strongly needed. In this paper, a new image processing algorithm to detect the center position and orientation of target VR by using machine vision is proposed for automatic final tuning of the 8mm camcoder's performance. In the method, the outline feature of object is used actively. The usefulness of the proposed methods were tested by several experiments, and the results showed enough accuracy for both of position and orientation. Additatively, we discussed about the total visual system construction and preprocessing of image.

• 대한산업공학회지
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• 제33권2호
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• pp.254-264
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• 2007

This paper considers a scheduling problem to minimize the total weighted completion time in the two-stage assembly-type flowshop. The system is composed of multiple fabrication machines in the first stage and a final-assembly machine in the second stage. Each job consists of multiple components, each component is machined on the fabrication machine specified in advance. The manufactured components of each job are subsequently assembled into a final product on the final-assembly machine. The objective of this paper is to find the optimal schedule minimizing the total weighted completion time of jobs. Three lower bounds are derived and tested in a branch-and-bound (B&B) Procedure. Also, three heuristic algorithms are developed based on the greedy strategies. Computational results show that the proposed B&B procedure is more efficient than the previous work which has considered the same problem as this paper.

• Industrial Engineering and Management Systems
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• 제3권1호
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• pp.52-58
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• 2004

This report examines the issue of designing an efficient production system by increasing several types of flexibility. Increasing manufacturing flexibility is a key strategy for efficiently improving market responsiveness in the face of uncertain market demand for final products. The manufacturing system comprises multiple plants, of which individual plants have multiple manufacturing lines that are designed to produce limited types of products in accordance with their size and materials. Imbalance in the workload occurs among plants as well as among manufacturing lines because of fluctuations in market demand for final products. Thereby, idleness of some manufacturing lines and longer lead times in some manufacturing lines occur as a result of the high workload. We clarify how these types of flexibility affect manufacturing performance by improving only one type of flexibility or by improving multiple types of flexibility simultaneously. The average lead time and the imbalance in workload are adopted as measures of manufacturing performance. Three types of manufacturing flexibility are interrelated: machine flexibility, routing flexibility, and process flexibility. Machine flexibility refers to the various types of operations that a machine can perform without requiring the prohibitive effort of switching from one order to another. Routing flexibility is the capability of processing a given set of part types using more than one line (alternative line) in the plant. Process flexibility results from being able to build different types of final products at the same plant.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2007년도 춘계학술대회A
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• pp.1548-1552
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• 2007

The roll forming machines currently used in industries require manual change of individual rolls taking 30 to 60 minutes of operation shutdown, This in turn reduces the operational efficiency by considerable margin and has one of the major negative effect on the overall productivity. To improve the operational efficiency of the existing roll forming machine, current manual roll changing process needs automatation to save considerable amount of time. In this study, TRIZ is adopted in the development of new roll forming machine. The Ideal Final Result (IFR) was set up initially and the fundamental causes were examined by Root Cause Analysis. The final proposed concept was drawn from the application of 40 invention principles of TRIZ.