• International Journal of Quality Innovation
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• v.7 no.1
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• pp.90-97
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• 2006

The process of producing software differs in many aspects from that of traditional manufacturing. Software is not manufactured in the classical sense. Development of software more closely resembles the development effort that goes into design new product [1-3]. In this article, we first describe the foundations of process improvement, which all processes can share. The process improvement differences between software and manufacturing process are then discussed, and a defect driven process inspection and improvement is introduced. Based on the discussion, two experiments were designed and the results of the results were collected. Through the comparison, we found that some efficient quality improvement approaches can be easily adapted in the software improvement and that the inspection efficiency is also significant.

• Journal of the military operations research society of Korea
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• v.28 no.2
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• pp.116-124
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• 2002

In just-in-time production systems, U-shaped production lines rather than traditional straight lines are often adopted since they have some advantages. The advantages of U-lines over straight lines are that the workstations required can be reduced and the necessary number of workers can be easily adjusted when the demand rates are changed. In this paper, we present a new genetic algorithm(GA) to minimize the number of workstations primarily and improve the work relatedness secondarily in the U-line production systems. Also, a new heuristic method is presented according to the work related factors and characteristics of U-line balancing. Some major aspects of the proposed GA are discussed, with emphasis on representation, decoding and evaluation function. Extensive experiments are carried out on well-known test-bed problems in the literature to verify the performance of our algorithm . The computational results show that our algorithm is a promising alternative to existing heuristics.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.12
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• pp.3197-3218
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• 2012

Compared with traditional itinerary planning, intention-oriented itinerary recommendations can provide more flexible activity planning without requiring the user's predetermined destinations and is especially helpful for those in unfamiliar environments. The rank and classification of points of interest (POI) from location-based social networks (LBSN) are used to indicate different user intentions. The mining of vehicles' physical trajectories can provide exact civil traffic information for path planning. This paper proposes a POI category-based itinerary recommendation framework combining physical trajectories with LBSN. Specifically, a Voronoi graph-based GPS trajectory analysis method is utilized to build traffic information networks, and an ant colony algorithm for multi-object optimization is implemented to locate the most appropriate itineraries. We conduct experiments on datasets from the Foursquare and GeoLife projects. A test of users' satisfaction with the recommended items is also performed. Our results show that the satisfaction level reaches an average of 80%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.5
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• pp.2142-2161
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• 2018

Distance dynamics model is an excellent model for uncovering the community structure of a complex network. However, the model has poor robustness. To improve the robustness, we design an enhanced distance dynamics model based on Ego-Leader and propose a corresponding community detection algorithm, called E-Attractor. The main contributions of E-Attractor are as follows. First, to get rid of sensitive parameter ${\lambda}$, Ego-Leader is introduced into the distance dynamics model to determine the influence of an exclusive neighbor on the distance. Second, based on top-k Ego-Leader, we design an enhanced distance dynamics model. In contrast to the traditional model, enhanced model has better robustness for all networks. Extensive experiments show that E-Attractor has good performance relative to several state-of-the-art algorithms.

• Korean journal of food and cookery science
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• v.10 no.3
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• pp.242-248
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• 1994

The purpose of this study was to investigate effect of the amounts of rice flour, glutinous-rice flour supplementation, Chinese radish, and the kinds and amount of sweetner on the sensory and objective characteristics of Moo-dduk which is korean traditional cake supplemented with jullienne Chinese radish. The advisable recipe for Moo-dduk was obtained through the various experiments. i) The Moo-dduk blended with rice flour. rice flour 300 g Chinese radish 210 g(70% of rice flour) sugar 30 g(10% of rice flour) salt 3 g(1% of rice flour) ii) The Moo-dduk blended with rice flour and glutinous-rice flour. rice flour 225 g, glutinous-rice flour 75 g(25% of rice flour), Chinese radish 210 g(70% of rice flour) sugar 30 g(10% of rice flour) salt 3 g(1% of rice flour)

• Structural Engineering and Mechanics
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• v.59 no.1
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• pp.187-207
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• 2016

Practical ambient excitations of engineering structures usually do not comply with the stationary-white-noise assumption in traditional operational modal analysis methods due to heavy traffic, wind guests, and other disturbances. In order to eliminate spurious modes induced by non-white noise inputs, the improved stochastic subspace identification based on a delay index is proposed in this paper for a representative kind of stationary non-white noise ambient excitations, which have nonzero autocorrelation values near the vertical axis. It relaxes the stationary-white-noise assumption of inputs by avoiding corresponding unqualified elements in the Hankel matrix. Details of the improved stochastic subspace identification algorithms and determination of the delay index are discussed. Numerical simulations on a four-story frame and laboratory vibration experiments on a simply supported beam have demonstrated the accuracy and reliability of the proposed method in eliminating spurious modes under non-white noise ambient excitations.

• Nuclear Engineering and Technology
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• v.53 no.5
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• pp.1602-1611
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• 2021

A varying degree of eccentricity always exists in the initial configuration of a nuclear fuel rod. Its impact on traditional LWR fuel is limited as the radial gap closes relatively early during irradiation. However, the effect of misalignment is expected to be more relevant in rods with highly conductive fuels, large initial gaps and low conductivity filling gases. In this paper, we study similar characteristics in the experimental setup of two fuel disc irradiation campaigns carried out in the OECD Halden Boiling Water Reactor. Using the multi-dimensional fuel performance code OFFBEAT, we combine 2-D axisymmetric and 3-D simulations to investigate the effect of eccentricity on the fuel temperature distribution. At the same time, we illustrate how the advent of modern tools with multi-dimensional capabilities might further improve the design and interpretation of in-pile separate-effect tests and we outline the potential of such an analysis for upcoming experiments.

• Journal of Broadcast Engineering
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• v.26 no.7
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• pp.862-867
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• 2021

Although Deep Neural Networks (DNNs) have shown remarkable performance in various artificial intelligence fields, it is well known that DNNs are vulnerable to adversarial attacks. Since adversarial attacks are implemented by adding perturbations onto benign examples, increasing the sparsity of DNNs minimizes the propagation of errors to high-level layers. In this paper, unlike the traditional pruning scheme removing low magnitude weights, we eliminate high magnitude weights that are usually considered high absolute values, named 'reverse pruning' to ensure robustness. By conducting both theoretical and experimental analyses, we observe that reverse pruning ensures the robustness of DNNs. Experimental results show that our reverse pruning outperforms previous work with 29.01% in Top-1 accuracy on perturbed CIFAR-10. However, reverse pruning does not guarantee benign samples. To relax this problem, we further conducted experiments by adding a regularization term for the high magnitude weights. With adding the regularization term, we also applied conventional pruning to ensure the robustness of DNNs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.2986-2999
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• 2019

The uncertainties of the uncertain graph make the traditional definition and algorithms on mining dense graph for certain graph not applicable. The subgraph obtained by maximizing expected density from an uncertain graph always has many low edge-probability data, which makes it low reliable and low expected edge density. Based on the concept of ${\beta}$-subgraph, to overcome the low reliability of the densest subgraph, the concept of optimal ${\beta}$-subgraph is proposed. An efficient greedy algorithm is also developed to find the optimal ${\beta}$-subgraph. Simulation experiments of multiple sets of datasets show that the average edge-possibility of optimal ${\beta}$-subgraph is improved by nearly 40%, and the expected edge density reaches 0.9 on average. The parameter ${\beta}$ is scalable and applicable to multiple scenarios.

• Journal of IKEEE
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• v.22 no.4
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• pp.948-952
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• 2018

Haze removal is an object of scientific desire due to its various practical applications. Existing algorithms are founded upon histogram equalization, contrast maximization, or the growing trend of applying machine learning in image processing. Since machine learning-based algorithms solve problems based on the data, they usually perform better than those based on traditional image processing/computer vision techniques. However, to achieve such a high performance, one of the requisites is a large and reliable training database, which seems to be unattainable owing to the complexity of real hazy and haze-free images acquisition. As a result, researchers are currently using the synthetic database, obtained by introducing the synthetic haze drawn from the standard uniform distribution into the clear images. In this paper, we propose the enhanced equidistribution, improving upon our previous study on equidistribution, and use it to make a new database for training machine learning-based haze removal algorithms. A large number of experiments verify the effectiveness of our proposed methodology.