Abstract-Top-k monitoring is important to many wireless sensor applications. This paper exploits the semantics of top-k query and proposes an energy-efficient monitoring approach called FILA. The basic idea is to install a filter at each sensor node to suppress unnecessary sensor updates. Filter setting and query reevaluation upon updates are two fundamental issues to the correctness and efficiency of the FILA approach. We develop a query reevaluation algorithm that is capable of handling concurrent sensor updates. In particular, we present optimization techniques to reduce the probing cost. We design a skewed filter setting scheme, which aims to balance energy consumption and prolong network lifetime. Moreover, two filter update strategies, namely, eager and lazy, are proposed to favor different application scenarios. We also extend the algorithms to several variants of top-k query, that is, orderinsensitive, approximate, and value monitoring. The performance of the proposed FILA approach is extensively evaluated using real data traces. The results show that FILA substantially outperforms the existing TAG-based approach and range caching approach in terms of both network lifetime and energy consumption under various network configurations.
To determine an appropriate trading time for buying or selling stocks is always a difficult task. The common way to deal with it is using trading strategies formed by technical or fundamental indicators. Lots of approaches have been presented on how to form trading strategies and how to set suitable parameters for those strategies. Furthermore, some approaches were also designed to optimize a trading strategy portfolio, which is a set of strategies where the return and risk of the portfolio can be maximized and minimized, respectively. To provide a more useful trading strategy portfolio, we first define a group trading strategy portfolio (GTSP). Then, an algorithm that utilizes the grouping genetic algorithm is designed for solving the GTSP optimization problem. In the chromosome representation, the grouping, strategy, and weight parts are employed to encode a possible GTSP. The fitness value of a chromosome is calculated by the group balance, weight balance, portfolio return, and risk to assess the quality of every possible solution. Genetic operators, including crossover, mutation, and inversion, are applied on the population to form a new offspring. Evolution is continued until the stop conditions are reached. Lastly, experiments were conducted on two real datasets with different trends to show that the advantages and the effectiveness of the presented approach. INDEX TERMS Group trading strategy portfolio, grouping genetic algorithm, portfolio optimization, trading strategy, trading strategy portfolio.
In the production process from green beans to coffee bean packages, the defective bean removal (or in short, defect removal) is one of most labor-consuming stages, and many companies investigate the automation of this stage for minimizing human efforts. In this paper, we propose a deep-learning-based defective bean inspection scheme (DL-DBIS), together with a GAN (generative-adversarial network)-structured automated labeled data augmentation method (GALDAM) for enhancing the proposed scheme, so that the automation degree of bean removal with robotic arms can be further improved for coffee industries. The proposed scheme is aimed at providing an effective model to a deep-learning-based object detection module for accurately identifying defects among dense beans. The proposed GALDAM can be used to greatly reduce labor costs, since the data labeling is the most labor-intensive work in this sort of solutions. Our proposed scheme brings two main impacts to intelligent agriculture. First, our proposed scheme is can be easily adopted by industries as human effort in labeling coffee beans are minimized. The users can easily customize their own defective bean model without spending a great amount of time on labeling small and dense objects. Second, our scheme can inspect all classes of defective beans categorized by the SCAA (Specialty Coffee Association of America) at the same time and can be easily extended if more classes of defective beans are added. These two advantages increase the degree of automation in the coffee industry. The prototype of the proposed scheme was developed for studying integrated tests. Testing results of a case study reveal that the proposed scheme can efficiently and effectively generate models for identifying defective beans with accuracy and precision values up to 80 % .
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