In this paper, we address how to reduce the length of tool retraction in a zigzag pocket machining. Tool retraction, in a zigzag pocket machining, is a non-cutting operation in which the tool moves to any remaining regions for machining. We developed an algorithm of generating tool retraction length in convex or concave polygonal shapes including islands. In the algorithm, we consider concave areas of cutting direction in the polygonal shape. Considering concave areas of cutting direction, the polygonal shape is decomposed to subregions which do not need any tool retraction. Using the proposed algorithm, we calculated the shortest length of tool retraction in cutting direction. Examples are shown to verify the validity of the algorithm.

The third generation mobile communication needs to provide multimedia service with increased data rates. Thus an efficient allocation of radio and network resources is very important. This paper models the 'burst switching' as an efficient radio resource allocation scheme and the performance is compared to the circuit and packet switching. In burst switching, radio resource is allocated to a call for the duration of data bursts rather than an entire session or a single packet as in the case of circuit and packet switching. After a stream of data burst, if a packet does not arrive during timer2 value ($\tau_{2}$), the channel of physical layer is released and the call stays in suspended state. Again if a packet does not arrive for timerl value ($\tau_{1}$) in the suspended state, the upper layer is also released. Thus the two timer values to minimize the sum of access delay and queuing delay need to be determined. In this paper, we focus on the decision of $\tau_{2}$ which minimizes the access and queueing delay with the assumption that traffic arrivals follow Poison process. The simulation, however, is performed with Pareto distribution which well describes the bursty traffic. The computational results show that the delay and the packet loss probability by the burst switching is dramatically reduced compared to the packet switching.

In novelty detection, one attempts to discriminate abnormal patterns from normal ones. Novelty detection is quite difficult since, unlike usual two class classification problems, only normal patterns are available for training. Auto-Associative Multi-Layer Perceptron (AAMLP) has been shown to provide a good performance based upon the property that novel patterns usually have larger auto-associative errors. In this paper, we give a mathematical analysis of 2-layer AAMLP's output characteristics and empirical results of 2-layer and 4-layer AAMLPs. Various activation functions such as linear, saturated linear and sigmoid are compared. The 2-layer AAMLPs cannot identify non-linear boundaries while the 4-layer ones can. When the data distribution is multi-modal, then an ensemble of AAMLPs, each of which is trained with pre-clustered data is required. This paper contributes to understanding of AAMLP networks and leads to practical recommendations regarding its use.

Detecting straight lines in an image is frequently required for various machine vision applications such as restoring CAD drawings from scanned images and object recognition. The standard Hough transform has been dominantly used to that purpose. However, massive storage requirement and low precision in estimating line parameters due to the quantization of parameter space are the major drawbacks of the Hough transform technique. In this paper, to overcome the drawbacks, an iterative algorithm based on a self-organizing map is presented. The self-organizing map can be adaptively learned such that image points are clustered by prominent lines. Through the procedure of the algorithm, a set of lines are sequentially detected one at a time. The algorithm can produce highly precised estimates of line parameters using very small amount of storage memory. Computational results for synthetically generated images are given. The promise of the algorithm is also demonstrated with its application to two natural images of inserts.

In this paper, we propose a new data fusion method to improve the performance of individual prediction models for time series data. Individual models used are ARIMA and neural network and their results are combined based on the weight reflecting the inverse of EWMA of squared prediction error of each individual model. Monte Carlo simulation is used to identify the situation where the proposed approach can take a vintage point over typical fusion methods which utilize MSE for weight. Study results indicate the following: EWMA performs better than MSE fusion when the data size is large with a relatively big amplitude, which is often observed in intra-cranial pressure data. Additionally, EWMA turns out to be a best choice among MSE fusion and the two individual prediction models when the data size is large with relatively small random noises, often appearing in tax revenue data.

The objective of this study is to evaluate impact of computer-based man-machine interfaces of Korea Next Generation Reactor (KNGR) on the operator mental workload. Empirical experiments were conducted to measure the operator mental workloads of KNGR and Yong-Gwang Unit 3 and 4, respectively. A comparison analysis based on a NASA TLX revealed that Yong-Gwang Unit 3 and 4 were superior to KNGR in terms of the mental workload. Post-hoc analyses showed that the mental workload of senior reactor operators was significantly higher than those of reactor and turbine operators, regardless of plant types. The implications of the findings were discussed in detail.

In this paper we consider an M/G/1 queueing system with vacation. The length of vacation period may be controlled by the waiting time of the first customer. The server goes on vacation as soon as the system is empty, and resumes service either when the waiting time of the leading customer reaches a predetermined value, or when the vacation period is expired, whichever comes first. We consider two types of vacation, say, multiple vacation type and N-policy type. We derive the steady-state distributions of the number of customers at arbitrary time and arbitrary customer's waiting time by means of decomposition property. Also, the mean lengths of busy period, idle period and a cycle time are given.

A study was conducted to investigate the availability of Fitts' Law to Hangul input systems on mobile phones. Three different Hangul input systems were experimented to measure the performance time to evaluate the physical interface of all. The measured performance time was found to be well fitted with the modified Fitts' Law by Hangul input systems on mobile phones. As a result, the physical interfaces for Hangul input systems could be evaluated quantitatively with the prediction of the performance time by Fitts' Law.

In this paper, we use a structural equation model to diagnose success factors on commercialization of information and communication technology considering the characteristics of commercialization process in terms of the type of technology, technology provider and technology receiver. The results of our study indicated the following. The transferor's research ability has significant effects on the commercialization by small businesses with low R&D expenditure to sales ratio, and the business management ability plays an important role in the commercialization by the relatively large (over 100 employees) companies with low R&D expenditure ratio. During the commercialization of the technology independently researched by government run institute or university, the transferee's business management ability is crucial to success. To those companies which run the existing business with high R&D expenditure ratio, their ability to utilize technology is essential. When the independently researched technology is used in the new business, the companies' business management ability and the role of technology transfer center are the important factors on the commercialization. Based on these findings, we suggest some strategies to effectively promote R&D efforts for successful commercialization.

The ergonomic human model can be considered as a tool for the evaluation of ergonomic factors in vehicle design process. The proper anthropometric data on driver's postures are needed in order to apply a human model to vehicle design. Although studies on driver's posture have been carried out for the last few decades, there are still some problems for the posture data to be applied directly to the human model due to the lack of fitness because such studies were not carried out under the conditions for the human model application. In the traditional researches, the joint angles were evaluated by the categorized data, which are not appropriate for the human model application because it is so extensive that it can not explain the posture evaluation data in detail. And the human models require whole-body posture evaluation data rather than joint evaluation data. In this study a postural evaluation function was developed not by category data but by the concept of the loss function in quality engineering. The loss was defined as the discomfort in driver's posture and measured by the magnitude estimation technique in the experiment using a seating buck. Four loss functions for the each joint - knee, hip, shoulder, and elbow were developed and a whole-body postural evaluation function was constructed by the regression analysis using these loss functions as independent factors. The developed postural evaluation function shows a good prediction power for the driver's posture discomfort in validation test. It is expected that the driver's postural evaluation function based on the loss function can be used in the human model application to the vehicle design process.

Recently, work-related musculoskeletal disorders(WMSDs) have rapidly increased and have been a major issue in the field of industrial safety. Of several physical risk factors for WMSDs, which include postures, vibration, repetitive work, speed or acceleration of movements, etc., awkward postures have been known as one of the major causes of WMSDs. For reducing the potential for injury as a result of postures, cost effective quantification of the magnitude for physical exposure to poor working postures is important and needed. To do this, several postural classification schemes have been developed and used in industrial sites. It is known that perceived discomfort for joint motions and muscle strength for females were much less than those for males. However, the existing postural classification schemes were developed without considering these gender effects. This study aims to develop a new postural classification scheme for female workers, based on the perceived discomfort for joint motions. The result showed that there was significant difference between the schemes for female and male. It was also found that when compared with OWAS, RULA and REBA, postural load was quantified more precisely with the developed scheme. It is recommended that different schemes according to gender of workers involved in work be used in order to accurately evaluate postural load of work postures.