The k-means algorithm of the partitioning clustering method is used to analyze cloud migration strategies in this study. The extent of assistance required to be provided to organizations while working on migration strategies was investigated for each cloud service model and concrete clusters were formed. This investigation is intended to aid cloud consumers in selecting their required cloud migration strategy. It is not easy for businessmen to select the most appropriate cloud migration strategy, and therefore, we proposed a suitable model to solve this problem. This model comprises a web of migration strategies, which provides an unambiguous visualization of the selected migration strategy. The cloud migration strategy targets the technical aspects linked with cloud facilities and measures the critical realization factors for cloud acceptance. Based on similar features, a correlation among the migration strategies is suggested, and three main clusters are formed accordingly. This helps to link the cloud migration strategies across the cloud service models (software as a service, platform as a service, and infrastructure as a service). This correlation was justified using the digital logic approach. This study is useful for the academia and industry as the proposed migration strategy selection process aids cloud consumers in efficiently selecting a cloud migration strategy for their legacy applications.
Functional near-infrared spectroscopy (fNIRS) and brain computer interface (BCI) have become indispensable tools for recording and monitoring brain activity, comprising a non-invasive and safe technique that allows researchers to monitor blood flow in the front part of the brain. Although some medical device manufacturers developed complex fNIRS systems, downsized fNIRS systems are important for other uses, such as in portable (palm-sized) and wearable healthcare devices. This paper proposes a downsized compact fNIRS prototype that detects hemodynamics in the frontal lobe. The aim is to develop a compact fNIRS system, which is reliable and easy to integrate into portable (palm-sized) BCI devices. Through practical experiments with human subjects, our proposed system showed an ability to detect and monitor the start and end time of human brain activities when participants were solving a calculation table.
A novel systematic technique for gain-scheduled control based on fixed-structure synthesis is adopted to design the aerial vehicle autopilot. The gain-scheduled design can be transformed into the multi-model control problem with both controller architecture and gain-scheduled architecture defined a priori. Hidden coupling terms naturally arise in the linearized dynamics of the gain-scheduled controller when some of the state variables are also used as scheduling variables. Unlike traditional approaches that do not consider these terms, the proposed method takes the hidden coupling terms directly into account in the synthesis phase. Finally, numerical simulations are carried out to evaluate the effectiveness of the proposed methods.
An image can be represented in the form of patterns of intensities, with the objects of an image appearing in the form of a pattern on an X-Y plane. The two patterns of intensities of two corresponding facial images are measured by calculating the areas of right triangles formed from patterns in a Cartesian coordinate system. The purpose of representing patterns of intensities in the Cartesian coordinate system is to measure the percentage of similarities that exists between two facial images, similarities inherent in photographs. The percentage is measured by incorporating the proposed technique of areas that are common between two patterns of intensities. The pattern 1 produces areas of right triangles of a parent with respect to areas of right triangles of a child. The strategy of measuring the facial similarities between two patterns of intensities is dependent on the areas of pattern 1 that have commonalities with the areas of pattern 2. This helps in the measuring of the facial similarities between two patterns of intensities. The proposed method has yielded results of 71.3, 77.1, 71.3, and 70.5 percent of similarity on the dataset KinfaceW-I and 80.7, 82.1, 80.6, 81.1 on the dataset KinfaceW-II.
A stem loosening is one of the significant problem in the bipolar hip arthroplasty (BHA), causes a pain and instability of a patient’s hip, and requires a further surgery of BHA. A stem canal fill ratio (SCFR), a general evaluation of the stem loosening, have been clinically studied many; however, a determination of the optimal SCFR evaluation has not been still understood well. A purpose of this study is to propose an automatic and quantitative evaluation of SCFR from BHA hip X-ray image. A proposed method segmented the femoral canal and stem, and evaluated SCFR. In experiments, a proposed method’s accuracy was validated, and six BHA patients’ SCFRs were clinically evaluated (age 88±7 (74–93), one male / five females). In result of the proposed method’s accuracy, the femur canal was 90.60±3.65%, and the stem was 97.83±0.46%; therefore, the proposed method had the accuracy to well evaluate SCFR. Result of six BHA patients’ SCFRs was 63.70±5.62%. In conclusion, the proposed method was able to automatically evaluate SCFR from the hip X-ray image with BHA.
In order to support the tele-operation of drone, a new devise that can be controlled easily and intuitively, is proposed. The proposed devise also can display the feedback information from drone by tactile display, and it employs two visualization modes, first one is to display the difference between the drone’s direction and control direction by using the position of violators. Second one is to display the difference between the drone’s direction and control direction by using various frequencies. Through the experimental comparisons, the effectiveness of the proposed system is confirmed.
One of the main problems for farmers is the protection of their crops, before and after harvesting, from animals and birds. To overcome this problem, this paper proposes a model of safe farming in which the crops will be protected from vertebrates’ attack through a prevention system that is based on Wirelesses Sensors Networks. Different sensor nodes are placed around the field that detect animals or birds’ existence and generate required signals and information. This information is passed to the Repelling and Notifying System (RNS) that is installed at the field through a short range wireless technology, ZigBee. As RNS receives the information, it generates ultrasonic sounds that are unbearable for animals and birds, which causes them to run away from the field. These ultrasonic sounds are generated in a frequency range that only animals and birds can hear, while humans cannot notice the sound. The paper also proposes a notifying system. It will inform the farmer about animals or birds’ intrusion in the field through SMS, but doesn’t need any action from the farmer. The low cost and power efficiency of the proposed system is a key advantage for developing countries where cost and power are major players in any system feasibility.
In this paper, we focus on multiobjective two-level fuzzy random programming problems with simple recourses, in which multiple objective functions are involved in each level, shortages and excesses resulting from the violation of the constraints with fuzzy random variables are penalized, and sums of the objective functions and expectation of the amount of the penalties are minimized. To deal with such problems, the concept of estimated Pareto Stackelberg solutions of the leader is introduced under the assumption that the leader can estimate the preference of the follower as a weighting vector of the weighting problems. Employing the possibility measure for fuzzy numbers, weighting method for multiobjective programming problems, and Kuhn-Tucker approach for two-level programming problems, a nonlinear optimization problem under complementarity conditions is formulated to obtain the estimated Pareto Stackelberg solutions for the leader. A numerical example illustrates the proposed method for a multiobjective two-level fuzzy random programming problem with simple recourses. Several types of estimated Pareto Stackelberg solutions are derived corresponding to the weighting vectors and permissible possibility levels specified by the leader.
2D histogram-based thresholding methods, in which the histogram is computed from local image features, have better performance than 1D histogram-based methods, but they take much more computation time. In this paper, we present a Rényi entropic multilevel thresholding (REMT) method based on a 2D direction histogram constructed from pixel values and local directional features. In addition to presenting a fast recursive method for REMT, we propose the Rényi entropic artificial bee colony multilevel thresholding (REABCMT) method to quickly find the optimal threshold values. In order to demonstrate the efficacy of REABCMT, three versions of this method are compared in terms of computation time and optimal threshold values. In addition, the segmentation performance of REABCMT is also evaluated by comparing it with two other methods to show its effectiveness. Moreover, in order to evaluate the efficiency and stability of using the ABC algorithm in the search for threshold values, genetic algorithm (GA) and particle swarm optimization (PSO), two common optimization algorithms, are also compared with it.
Convolutional Neural Networks (CNNs) effectively extract local features from input data. However, CNN based on word embedding and convolution layers displays poor performance in text classification tasks when compared with traditional baseline methods. We address this problem and propose a model named NNGN that simplifies the convolution layer in the CNN by replacing it with a pooling layer that extracts n-gram embedding in a simpler way and obtains document representations via linear computation. We implement two settings in our model to extract n-gram features. In the first setting, which we refer to as seq-NNGN, we consider word order within each n-gram. In the second setting, BoW-NNGN, we do not consider word order. We compare the performance of these settings in different classification tasks with those of other models. The experimental results show that our proposed model achieves better performance than state-of-the-art models.
Demand forecasting for petrol products in gas stations is crucial to the planning of initiative distribution of petrol products, especially to the stability of product supply in petroleum companies. In this paper, a novel scheme of demand forecasting based on clustering and a decision tree is proposed, which uses a decision tree and integrates the results of clustering validity indices. First, the proposed scheme uses a k-means algorithm to divide the sales data into multiple disjointed clusters, evaluates the clustering result of the daily sales curve of a product according to seven validity indices and determines the optimal number of clustering. Next, the relationship between the sales pattern and the relevant influence factors is described using a decision tree, which can categorize a future day’s sales pattern with these factors into the most suitable cluster to predict the quantity of the demand and the peak demand time windows for each gas station. Finally, three months’ worth of sales data is collected from a gas station in Dalian city, China, to illustrate the proposed forecasting scheme. Experimental results demonstrate that the scheme is an effective alternative for the demand forecasting for petrol products because it outperforms three other selected methods.
This paper focuses on two Apriori-based rule generators. The first is the rule generator in Prolog and C, and the second is the one in SQL. They are named Apriori in Prolog and Apriori in SQL, respectively. Each rule generator is based on the Apriori algorithm. However, each rule generator has its own properties. Apriori in Prolog employs the equivalence classes defined by table data sets and follows the framework of rough sets. On the other hand, Apriori in SQL employs a search for rule generation and does not make use of equivalence classes. This paper clarifies the properties of these two rule generators and considers effective applications of each to existing data sets.
Rough set theory was proposed by Z. Pawlak in 1982. This theory can mine knowledge based on a decision rule from a database, a web base, a set, and so on. The decision rule is used for data analysis as well as calculating an unknown object. We analyzed time-series data using rough sets. Economic time-series data was predicted using decision rules. However, there are cases where an excessive number of decision rules exist, from which, it is difficult to acquire knowledge. In this paper, we propose a method to reduce the number of decision rules by merging them. Similar to how it is difficult to acquire knowledge from multiple rules, it is also difficult to acquire knowledge from rules with a large number of condition attributes. We propose a method to reduce the number of condition attributes and thereby reduce the number of rules. We analyze time-series data using this proposed method and acquire knowledge for prediction using decision rules. We use TOPIX and the yen–dollar exchange rate as knowledge-acquisition data. We propose a method to facilitate knowledge acquisition by merging rules.