• Asia pacific journal of information systems
• /
• v.20 no.1
• /
• pp.81-99
• /
• 2010

Lately, in consumers' markets the number of new items is rapidly increasing at an overwhelming rate while consumers have limited access to information about those new products in making a sensible, well-informed purchase. Therefore, item providers and customers need a system which recommends right items to right customers. Also, whenever new items are released, for instance, the recommender system specializing in new items can help item providers locate and identify potential customers. Currently, new items are being added to an existing system without being specially noted to consumers, making it difficult for consumers to identify and evaluate new products introduced in the markets. Most of previous approaches for recommender systems have to rely on the usage history of customers. For new items, this content-based (CB) approach is simply not available for the system to recommend those new items to potential consumers. Although collaborative filtering (CF) approach is not directly applicable to solve the new item problem, it would be a good idea to use the basic principle of CF which identifies similar customers, i,e. neighbors, and recommend items to those customers who have liked the similar items in the past. This research aims to suggest a hybrid recommendation procedure based on the preference boundary of target customer. We suggest the hybrid recommendation procedure using the preference boundary in the feature space for recommending new items only. The basic principle is that if a new item belongs within the preference boundary of a target customer, then it is evaluated to be preferred by the customer. Customers' preferences and characteristics of items including new items are represented in a feature space, and the scope or boundary of the target customer's preference is extended to those of neighbors'. The new item recommendation procedure consists of three steps. The first step is analyzing the profile of items, which are represented as k-dimensional feature values. The second step is to determine the representative point of the target customer's preference boundary, the centroid, based on a personal information set. To determine the centroid of preference boundary of a target customer, three algorithms are developed in this research: one is using the centroid of a target customer only (TC), the other is using centroid of a (dummy) big target customer that is composed of a target customer and his/her neighbors (BC), and another is using centroids of a target customer and his/her neighbors (NC). The third step is to determine the range of the preference boundary, the radius. The suggested algorithm Is using the average distance (AD) between the centroid and all purchased items. We test whether the CF-based approach to determine the centroid of the preference boundary improves the recommendation quality or not. For this purpose, we develop two hybrid algorithms, BC and NC, which use neighbors when deciding centroid of the preference boundary. To test the validity of hybrid algorithms, BC and NC, we developed CB-algorithm, TC, which uses target customers only. We measured effectiveness scores of suggested algorithms and compared them through a series of experiments with a set of real mobile image transaction data. We spilt the period between 1st June 2004 and 31st July and the period between 1st August and 31st August 2004 as a training set and a test set, respectively. The training set Is used to make the preference boundary, and the test set is used to evaluate the performance of the suggested hybrid recommendation procedure. The main aim of this research Is to compare the hybrid recommendation algorithm with the CB algorithm. To evaluate the performance of each algorithm, we compare the purchased new item list in test period with the recommended item list which is recommended by suggested algorithms. So we employ the evaluation metric to hit the ratio for evaluating our algorithms. The hit ratio is defined as the ratio of the hit set size to the recommended set size. The hit set size means the number of success of recommendations in our experiment, and the test set size means the number of purchased items during the test period. Experimental test result shows the hit ratio of BC and NC is bigger than that of TC. This means using neighbors Is more effective to recommend new items. That is hybrid algorithm using CF is more effective when recommending to consumers new items than the algorithm using only CB. The reason of the smaller hit ratio of BC than that of NC is that BC is defined as a dummy or virtual customer who purchased all items of target customers' and neighbors'. That is centroid of BC often shifts from that of TC, so it tends to reflect skewed characters of target customer. So the recommendation algorithm using NC shows the best hit ratio, because NC has sufficient information about target customers and their neighbors without damaging the information about the target customers.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2008.05a
• /
• pp.857-860
• /
• 2008

In this paper, we proposed cooperative based localization algorithm for wireless sensor networks, which can estimate to unknown node position using received signal strength table set. The unknown nodes are monitor to RSS from neighbor nodes and exclude existence possibility area of sensor node in sensor field. Finally, we can calculate the centroid position for each unknown node with cooperative localization algorithm. Furthermore, these processes are applied iteratively about all nodes which is recorded to RSS table and can estimate for unknown nodes.

• Kyungpook Mathematical Journal
• /
• v.57 no.4
• /
• pp.667-676
• /
• 2017

We study the positive $C^1$ function z = f(x, y) defined on the plane ${\mathbb{R}}^2$. For a rectangular domain $[a,b]{\times}[c,d]{\subset}{\mathbb{R}}^2$, we consider the volume V and the surface area S of the graph of z = f(x, y) over the domain. We also denote by (${\bar{x}}_V,\;{\bar{y}}_V,\;{\bar{z}}_V$) and (${\bar{x}}_S,\;{\bar{y}}_S,\;{\bar{z}}_S$) the geometric centroid of the volume under the graph of z = f(x, y) and the centroid of the graph itself defined on the rectangular domain, respectively. In this paper, first we show that among nonconstant $C^2$ functions with isolated singularities, S = kV, $k{\in}{\mathbb{R}}$ characterizes the family of catenary rotation surfaces f(x, y) = k cosh(r/k), $r={\mid}(x,y){\mid}$. Next, we show that one of $({\bar{x}}_S,\;{\bar{y}}_S)=({\bar{x}}_V,\;{\bar{y}}_V)$, $({\bar{x}}_S,\;{\bar{z}}_S)=({\bar{x}}_V,\;2{\bar{z}}_V)$ and $({\bar{y}}_S,\;{\bar{z}}_S)=({\bar{y}}_V,\;2{\bar{z}}_V)$ characterizes the family of catenary rotation surfaces among nonconstant $C^2$ functions with isolated singularities.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.29 no.9
• /
• pp.14-22
• /
• 2015

TIn this paper, a centering method for an unfocused input character using the spherical domain system and the centering character to use the shift invariant feature for the recognition system is proposed. A system for recognition is implemented using the centroid method with coordinate average values, and the results of an above 78.14% average differential ratio for the character features were obtained. It is possible to extract the shift invariant feature using spherical transformation similar to the human eyeball. The proposed method, which is feature extraction using spherical coordinate transform and transformed extracted data, makes it possible to move the character to the center position of the input plane. Both digital and optical technologies are mixed using a spherical coordinate similar to the 3 dimensional human eyeball for the 2 dimensional plane format. In this paper, a centering character feature using the spherical domain is proposed for character recognition, and possibilities for the recognized possible character shape as well as calculating the differential ratio of the centered character using a centroid method are suggested.

• Journal of Astronomy and Space Sciences
• /
• v.22 no.3
• /
• pp.273-282
• /
• 2005

A star sensor daytime observation model is developed in order to test the performance of the star sensor useful for daylight application. The centroid errors of the star sensor in the day time application are computed by using the model. The standard atmospheric model (LOWTRAN7) is utilized to calculate the physical quantities of the daylight atmospheric environments where the star sensor is immersed. This observation model takes the separation angles between the sun and star, the centroid algorithm and the various system specifications of the star sensor into the account. The developed star sensor model will provide more realistic measurement errors in estimating the performance of the attitude determination from the vector observations.

• Journal of KIISE:Information Networking
• /
• v.32 no.5
• /
• pp.574-582
• /
• 2005

Localization in wireless sensor networks is essential to important network functions such as event detection, geographic routing, and information tracking. Localization is to determine the locations of nodes when node connectivities are given. In this paper, centroid approach known as a distributed algorithm is extended to a centralized algorithm. The centralized algorithm has the advantage of simplicity. but does not have the disadvantage that each unknown node should be in transmission ranges of three fixed nodes at least. The algorithm shows that localization can be formulated to a linear system of equations. We mathematically show that the linear system have a unique solution. The unique solution indicates the locations of unknown nodes are capable of being uniquely determined.

• Journal of Broadcast Engineering
• /
• v.16 no.1
• /
• pp.178-181
• /
• 2011

With the development of mobile devices and intelligent surveillance system loaded with pan/tilt cameras, object tracking with non-stationary cameras has become a topic with increasing importancy. Since it is difficult to model a background image in a non-stationary camera environment, colors and texture are the most important features in the tracking algorithm. However, colors in the background similar to those in the target arise instability in the tracking. Recently, we proposed a robust color based tracking algorithm that uses an area weighted centroid shift. In this letter, we update the model such that it becomes more stable against background colors. The proposed algorithm also incorporates time filtering by adding an additional energy term to the energy functional.

• Journal of Mechanical Science and Technology
• /
• v.19 no.2
• /
• pp.589-604
• /
• 2005

To overcome the drawback of currently available curved beam theories having non-symmetric thin-walled cross sections, a curved beam theory based on centroid-shear center formulation is presented for the spatially coupled free vibration and elastic analysis. For this, the displacement field is expressed by introducing displacement parameters defined at the centroid and shear center axes, respectively. Next the elastic strain and kinetic energies considering the thickness-curvature effect and the rotary inertia of curved beam are rigorously derived by degenerating the energies of the elastic continuum to those of curved beam. And then the equilibrium equations and the boundary conditions are consistently derived for curved beams having non-symmetric thin-walled cross section. It is emphasized that for curved beams with L- or T-shaped sections, this thin-walled curved beam theory can be easily reduced to the solid beam theory by simply putting the sectional properties associated with warping to zero. In order to illustrate the validity and the accuracy of this study, FE solutions using the Hermitian curved beam elements are presented and compared with the results by previous research and ABAQUS's shell elements.

• Journal of IKEEE
• /
• v.17 no.3
• /
• pp.254-259
• /
• 2013

The choice of labeled data in semi-supervised learning algorithm can result in effects on the performance of the resultant classifier. In order to select labeled data required for the training of a semi-supervised learning algorithm, VCNN(Vector Centroid Neural Network) is proposed in this paper. The proposed selection method of label data is evaluated on UCI dataset and caltech dataset. Experiments and results show that the proposed selection method outperforms conventional methods in terms of classification accuracy and minimum error rate.

• Proceedings of the Korean Society of Propulsion Engineers Conference
• /
• 2010.11a
• /
• pp.399-404
• /
• 2010

Test results including the variation of propellant-inlet pressure, pulsed thrust, and environment vacuum with the accompanying thermal responses are presented for the pulse-mode operation of a set of monopropellant hydrazine thrusters producing $0.95lb_f$ of nominal steady-state thrust at an inlet pressure of 350 psia. The test data are reduced into the impulse bit, specific impulse, and force centroid that are the factors typically characterizing pulse-mode performance of small rocket engines. With a scrutiny to the performance parameters, their comparison to the reference criteria of 1 lbf standard monopropellant rocket engine are successfully made.