• Tribology and Lubricants
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• 제17권3호
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• pp.228-235
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• 2001

EHL analysis of connecting rod bearing is proposed which includes effects of temperature variation in lubrication film. Lubrication film temperature is treated as a time-dependent, two-dimensional variable which is averaged over the film thickness, while connecting rod big end temperature is assumed to be time-independent and three-dimensional. It is assumed that a portion of the heat generated by viscous dissipation in the lubrication film is absorbed by the film itself, and the remainder flows into the bearing surface. Mass-conserving cavitation algorithm is applied and the effect of variable viscosity is included to solve the Reynolds equation. Simulation results of the connecting rod bearing in internal combustion engine are presented. It is shown that the temperature variation has remarkable effects on the bearing performance. It is concluded that the EHL analysis considering effects of the temperature variation is strongly recommended to predict the connecting rod bearing performance in internal combustion engine.

• 산업경영시스템학회지
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• 제22권50호
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• pp.127-137
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• 1999

This study is concerned with the loading problems in flexible manufacturing system(FMS). The loading problem in FMS is a complex one, when the number of machine and job is increased. It may be time-consuming and even impossible to achieve an optimal solution about this problem mathematically. Thus, a heuristic method is recommended in order to gain near-optimal solutions in a practically acceptable time. A new loading algorithm is developed with a multi-criterion objective of considering the workload unbalance, and maximizing the machine utilization, throughput for critical resources such as the number of tool slots and the number of working hours in a scheduling period and so on. The results of SAS analysis indicated that true average throughput of proposed heuristic loading statistically exceeds that of Shanker and Srinivasulus loading algorithm at the significance level of 0.1.

• 대한전기학회논문지:전기물성ㆍ응용부문C
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• 제52권1호
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• pp.25-34
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• 2003

Gas insulated switchgear(GIS) is an important equipment in a substation. It is highly desirable to measure a partial discharge(PD) in GIS which is a symptom before insulation breakdown occurs. The issue is that the PD signal is weak and sensitive to external noise. In this paper, the algorithm for detection of PD in GIS using wavelet transform is proposed. The wavelet transform provides a direct quantitative measure of spectral content, "dynamic spectrum", in the time-frequency domain. The recommended mother wavelet is 'Daubechies' wavelet. 'db4', the most commonly applied mother wavelet in the power quality analysis, can be used most properly in disturbance phenomena which occurs rapidly for a short time. Through the procedure of wavelet transform, noise extraction and reconstruction, the signal is Analyzed to determine the magnitude of PD in GIS. In experimental results, we can know that partial discharge is exactly detected in combination of Dl and D2 using wavelet transform.transform.

• 대한원격탐사학회지
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• 제19권5호
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• pp.403-409
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• 2003

Since SPOT4 satellite contained VEGETATION 1 sensor launched, the noise in VEGETATION data was occasionally arisen a difficulty for the data traitement. Blind line noise types were studied in VEGETATION-l short wave infrared channel(SWIR). In order to provide a precis product, the procedure for removing this noise is strongly recommended. In the case that the blind values are clearly distinguished from contamination-free values a simple threshold method was applied, while a changeable threshold method was used for the blind value mixed with contamination-free values. New algorithm presented in this study is consists of two method for each type of SWIR blind. After removing blind line, there were again some residual pixels of blind, because the threshold is not determinated sufficiently low. Lower threshold could remove the blind line as well as the contamination-free pixels. Nevertheless, the results showed a good qualitative improvement as compared with other algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권9호
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• pp.4684-4705
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• 2019

Collaborative filtering recommender systems are vulnerable to shilling attacks in which malicious users may inject biased profiles to promote or demote a particular item being recommended. To tackle this problem, many robust collaborative recommendation methods have been presented. Unfortunately, the robustness of most methods is improved at the expense of prediction accuracy. In this paper, we construct a robust Bayesian probabilistic matrix factorization model for collaborative filtering recommender systems by incorporating the detection of user anomaly rating behaviors. We first detect the anomaly rating behaviors of users by the modified K-means algorithm and target item identification method to generate an indicator matrix of attack users. Then we incorporate the indicator matrix of attack users to construct a robust Bayesian probabilistic matrix factorization model and based on which a robust collaborative recommendation algorithm is devised. The experimental results on the MovieLens and Netflix datasets show that our model can significantly improve the robustness and recommendation accuracy compared with three baseline methods.

• Journal of Multimedia Information System
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• 제8권1호
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• pp.69-74
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• 2021

This paper accumulates the product structure data such as bond obligation ratio and investment ratio for variable insurance using crawling from the insurance company's API, also accumulates variable insurance income and project expenses for variable insurance using crawling from the API of life insurance association. From these accumulated data, the correlation coefficient between fund product and customer preference is calculated with an investment algorithm, and variable insurance funds by customer investment preference and product structure are recommended according to market conditions. From the simulation results, it is shown that the proposed variable insurance management system properly recommends and manages variable insurance according to customer preferences.

• 한국통신학회논문지
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• 제24권10A호
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• pp.1501-1506
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• 1999

DSR(Dedicated Short Communications)는 ITS 서비스를 지원하기 위한 이동 통신 방식으로서 교통 정보 서비스에 적합하다. 그러나 ITS에서 일반적인 데이터 서비스를 지원할 경우에는 기존의WAC (Wide Area wireless Communications) 방식을 고려할 필요가 있다. 특히 이동통신 망에서의 무선 인터넷 서비스 지원을 위한 활발한 연구를 고려할 때, ITS에서의 인터넷 서비스 지원은 고려할 가치가 있다. DSRC의 무선 구간 에러 및 흐름 제어를 담당하는 LLC(Logical Link Control) 프로토콜은 교통 정보 서비스를 지원하기에는 적합하나, 영상, 지리정보, 인터넷과 같은 서비스에는 효율적이지 못하다. 그 이유는 서비스에 따라 데이터의 트래픽 특성이 달라지기 때문이다. 따라서 본 논문에서는 DSRC에서 교통 정보 서비스뿐만 아니라 인터넷 웹 서비스까지 효율적으로 지원할 수 있는 LLC 프로토콜을 제안하고 그 성능을 분석한다. 성능 분석의 결과, 제안된 알고리즘이 교통 정보 서비스에 대해서는 일본과 유럽에서 제안된 DSRC LLC 프로토콜과 동일한 성능을, 인터넷 웹 서비스에 대해서는 우월한 성능을 나타냄을 확인하였다.

• 대한불안의학회지
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• 제12권1호
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• pp.47-55
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• 2016

30% of patients with panic disorder (PD) show treatment-resistant and chronic waxing and waning course. Therefore, adequate treatment strategies for PD by evidence based pharmacotherapy and combined cognitive behavioral therapy (CBT) are recommended. Regarding how and why CBT for PD works, three hypotheses include the cognitive theory, anxiety control theory, and behavioral theory were discussed. The recent findings that the altered activation in frontal lobe is normalized after CBT, suggest a reduction of an altered top-down fear processing in the neural correlates of CBT in PD. In order to improve accessibility to CBT, brief CBT and internet based CBT for PD were suggested. Despite limitations of sample sizes and study design, most of studies suggest that brief CBT is more effective than control conditions, and even as equally effective as standard CBT. The evidences suggest that internet based CBT may not be significantly different from face-to-face CBT in reducing anxiety. Several advances within the field of third-wave CBT for PD have led to the development of new techniques based on mindfulness, such as mindfulness-based cognitive therapy and acceptance and commitment therapy. Based on Korean algorithm project for panic disorder, especially the psychological education and cognitive reconstruction components were recommended in CBT with PD.

• 한국정밀공학회지
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• 제23권11호
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• pp.58-67
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• 2006

Due to the variety of signal processing and complicated mathematical analysis, it is not easy to accomplish 3D bin-picking with non-contact sensor. To solve this difficulties the reliable signal processing algorithm and a good sensing device has been recommended. In this research, 3D laser scanner and CCD camera is applied as a sensing device respectively. With these sensor we develop a two-step bin-picking method and reliable algorithm for the recognition of 3D bin object. In the proposed bin-picking, the problem is reduced to 2D intial recognition with CCD camera at first, and then 3D pose detection with a laser scanner. To get a good movement in the robot base frame, the hand eye calibration between robot's end effector and sensing device should be also carried out. In this paper, we examine auto-calibration technique in the sensor calibration step. A new thinning algorithm and constrained hough transform is also studied for the robustness in the real environment usage. From the experimental results, we could see the robust bin-picking operation under the non-aligned 3D hole object.

• 디지털산업정보학회논문지
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• 제6권3호
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• pp.19-29
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• 2010

A marketing model is changed from a customer acquisition to customer retention and it is being moved to a way that enhances the quality of customer interaction to add value to our customers. Such personalization is emerging from this background. The Web site is accelerate the adoption of a personalization, and in contrast to the rapid growth of data, quantitative analytical experience is required. For the automated analysis of large amounts of data and the results must be passed in real time of personalization has been interested in technical problems. A recommendation algorithm is an algorithm for the implementation of personalization, which predict whether the customer preferences and purchasing using the database with new customers interested or likely to purchase. As recommended number of users increases, the algorithm increases recommendation time is the problem. In this paper, to solve this problem, a recommendation system based on clustering and dimensionality reduction is proposed. First, clusters customers with such an orientation, then shrink the dimensions of the relationship between customers to low dimensional space. Because finding neighbors for recommendations is performed at low dimensional space, the computation time is greatly reduced.