Earticle

Ground speed calculation is basic work for trajectory prediction, conflict detection and air traffic flow management. This paper proposes a novel algorithm based on Vincenty’s formulas for ground speed calculation. Our experiments used simulations with wind components and our experimental results show that our ground speed calculation exhibits much better accuracy performance.

This paper proposes a recommender system based on periodicity analysis from purchase history data using mining sequential pattern with time-series and FRAT (Frequency, Regency, Amount and Type of merchandise or service) analysis. In this paper, not used user's profile for rating, it is necessary for us to make the task of mining sequential pattern with time-series and clustering of category with weight using FRAT analysis in order to recommend service by frequently changing trends of purchase pattern. For doing that, it is considered the importance of type of merchandise or service and then, we suggest recommending method based on periodicity analysis to reflect frequently changing trends of seasonable purchase pattern for the four seasons as time goes by and as often as customers need different merchandises on e-commerce being extremely diverse. We evaluated the proposing system on the data set collected in the same dataset collected on a cosmetic internet shop to measure its performance. We reported some of the experimental result, in the same condition, which is better performance than both the previous system and existing system.

The purpose of this study is to identify the optimal threshold ranges for predicting osteoporosis and osteoporotic fractures by analyzing the correlations between the trabecular patterns and fracture risk factors of the proximal femur. We selected 85 post-menopausal women as experimental subjects and classified them into groups of 29 normal and 56 osteoporotic patients according to their T-scores. We proposed a novel thresholding algorithm, which divides the threshold ranges from 0% to 95% on the basis of the trabecular bone area, and assessed the osteoporosis predictability for each range. The evaluation parameters were categorized into four morphological parameters and six fracture risk factors. Consequently, we found the clinical usefulness of the proposed algorithm for discriminating the patients with osteoporosis from those with normal bone. Moreover, the significances between the morphological parameters and the fracture risk factors improved as the bone mineral density (BMD) decreased. Based on these experimental results, we selected the optimal threshold conditions for predicting osteoporosis and osteoporotic fractures at thresholds of 40%-80%.

The fusion algorithm of multiple images of which focal lengths are different is necessary to generate visually enhanced composite image. Also, it can be employed as a pre-processing step for edge detection or feature extraction in order to improve the performances. In this paper, we suggest a pixel- based approach for the fusion of multi-focused image. The focus measure of SML(Sum of Modified- Laplacian) is slightly modified by adopting double thresholds. We firstly calculate 3-level SML comparison map which contains intermediate level for ambiguous pixels. And the final 2-level map is generated after reallocating these pixels to get more reliable result and median filtering to reduce the effect of isolated noises. The performances are evaluated by computer simulation which showed that the visual quality of composite image was successfully improved not only subjectively but objectively by the proposed fusion algorithm.

To restore the ecosystem and monitor the progress, changes of fish fauna were selected for wireless ecosystem monitoring data. The habitat conditions in the test bed were categorized into three types. Two types of dam lakes in which an existing artificial vegetation island is installed in a riverbed made up of clay and sand (St. 1) and dam lakes that apply a hybrid artificial vegetation island and artificial fish reefs that complement the functions of the existing artificial vegetation island (St. 2) were constructed. The third type is an artificial wetland built on the upstream of the dam in which artificial habitats are made using artificial fish reefs, riffle frames, piling rocks, etc in an environment that has repeated multipath puddles made mainly of sand, as well as riffles and pockets of riverbeds composed of gravel and cobble stones (St. 3). When examining the fish fauna that appear per habitat condition, in the case of St. 1, a total of four families and ten species were confirmed, while four families and eleven species and three families and seventeen species were confirmed for St. 2 and St. 3, respectively. In the case of St. 2 that applies a hybrid artificial vegetation island and artificial fish reefs that complement the functions of the existing artificial vegetation island, it could provide relatively more shelter to conceal small fish compared to St. 1. In the case of St. 3 that maintains a constant water level to stabilize vegetation around the waterside and maintains various water flows and multi-porous habitat environments, it is suitable as a habitat for various life forms.

Sharing resources and information on the Internet is becoming a popular and essential activity, requiring the development of methods to achieve collective intelligence. Our repository has been developed for managing and sharing resources that are collected from the Internet and number approximately 300,000 items. The resources are created and reused by users for specific purposes. In this research, the inner attributes of Internet resources and their correlations with others are investigated in a federated repository. An approach integrating the concept of graph model and social network analysis is proposed, in order to achieve efficient representation of resources over the Internet. In addition, two automated mechanisms concerning the resources discovery and reuse are implemented, to facilitate the usage scenario. Experiment results, including several self-evaluations and an empirical study with 40 users, demonstrate the performance and feasibility of this research.

This paper proposes a new weighted mining frequent pattern based on customer’s RFM(Recency, Frequency, Monetary) score for personalized u-commerce recommendation system under ubiquitous computing. An existing recommendation system using traditional mining has the problem, such as delay of processing speed from a cause of frequent scanning a large data, considering equal weight value of every item, and accuracy as well. In this paper, to solve these problems, it is necessary for us to extract the most frequently purchased data from whole data, to consider the weight/importance of attribute of item in order to forecast frequently changing trends by emphasizing the important items with high purchasability and to improve the accuracy of personalized u-commerce recommendation. To verify improved performance, we make experiments with dataset collected in a cosmetic internet shopping mall.

Under indoor environments, a beacon’s received-signal strength is very unstable. Utilizing the signal strength indication for estimating the distance between Access Points(APs) and beacons is very hard as the received signal frequently contains noise signals. Therefore, the noise signals need to be eliminated from the signal. Traditional research methods remove the noise signals by extracting and removing the peak signals. However, these methods consider only the upper side peak signals. This paper proposes a signal processing method that eliminates impulse noises in the beacon-AP signal in real time. By applying a noise filter to the two directional signals of impulse noises, the impulse noises can be eliminated. In the experiments, the proposed method was compared with traditional research methods. Compared to the traditional method, the proposed method reduced the difference between the calculated and estimated signal strength to 64%.

Typically, when a user wishes to minimise the energy consumption for an application running on a handheld device, he/she may choose to set the processor speed to its slowest level. However, our study indicated that due to the processes involved in memory accesses, decreasing the CPU frequency may not always reduce the energy consumption. A critical speed has been defined as the CPU frequency, at which energy consumption can be minimised for a program. It can be used when the user wants to maximise energy saving for the device if performance is a less important issue. In this paper, an energy conservation DVFS algorithm is proposed to achieve this goal. It predicts and applies the critical speed as the target CPU frequency during the program’s execution time. The algorithm relies on a prediction equation that is constructed based on the correlation between the critical speed and the memory access rate. We have implemented the algorithm on the Android operating system. Our results show that both the energy consumption and the performance can be improved than the situation of simply selecting the lowest frequency.