Chia-Hoang Lee

Handling forecasting problems using fuzzy time series

Abstract In [6–9], Song et al. proposed fuzzy time-series models to deal with forecasting problems. In [10], Sullivan and Woodall reviewed the first-order time-invariant fuzzy time series model and the first-order time-variant model proposed by Song and Chissom [6–8], where the models are compared with each other and with a time-invariant Markov model using linguistic labels with probability distributions. In this paper, we propose a new method to forecast university enrollments, where the historical enrollments of the University of Alabama shown in [7,8] are used to illustrate the forecasting process. The average forecasting errors and the time complexity of these methods are compared. The proposed method is more efficient than the ones presented in [7, 8, 10] due to the fact that the proposed method simplifies the arithmetic operation process. Furthermore, the average forecasting error of the proposed method is smaller than the ones presented in [2, 7, 8].

A new interpolative reasoning method in sparse rule-based systems

Abstract In [7], Yan et al. analyzed Koczy and Hirotas linear interpolative reasoning method presented in [2, 3] and found that the reasoning consequences by their method sometimes become abnormal fuzzy sets. Thus, they pointed out that a new interpolative reasoning method will be needed which can guarantee that the interpolated conclusion will also be triangular-type for a triangular-type observation. In this paper, we extend the works of [2,3,7] to present a new interpolative reasoning method to deal with fuzzy reasoning in sparse rule-based systems. The proposed method can overcome the drawback of Koczy and Hirotas method described in [7]. It can guarantee that the statement “If fuzzy rules A1 ⇒ B1, A2 ⇒ B2 and the observation A ∗ are defined by triangular membership functions, the interpolated conclusion B ∗ wil also be triangular-type” holds.

A fall detection system using k-nearest neighbor classifier

The main purpose of this paper is to use off-the-shelf devices to develop a fall detection system. In human body identification, human body silhouette is adopted to improve privacy protection, and vertical projection histograms of the silhouette image and statistical scheme are used to reduce the effect of human body upper limb activities. The kNN classification algorithm is used to classify the postures using the ratio and difference of human body silhouette bounding box height and width. Meanwhile, since time difference is a vital factor to differentiate fall incident event and lying down event, the critical time difference is obtained from the experiment and verified by statistical hypothesis testing. With the help of the kNN classifier and the critical time difference, a fall incident detection system is developed to detect fall incident events. The experiment shows that it could reduce the effect of upper limb activities and the system has a correct rate of 84.44% on fall detection and lying down event detection.

Comments on Mohr and Henderson's path consistency algorithm

Mohr and Henderson have presented new algorithms for arc and path consistency in [1]. Though the underlying ideas of their algorithms are correct, the path consistency algorithm PC-3 is in error. In this paper we point out the errors in this algorithm and give a correct one. The time complexity and space complexity of the revised algorithm are also analyzed.

Movie Rating and Review Summarization in Mobile Environment

In this paper, we design and develop a movie-rating and review-summarization system in a mobile environment. The movie-rating information is based on the sentiment-classification result. The condensed descriptions of movie reviews are generated from the feature-based summarization. We propose a novel approach based on latent semantic analysis (LSA) to identify product features. Furthermore, we find a way to reduce the size of summary based on the product features obtained from LSA. We consider both sentiment-classification accuracy and system response time to design the system. The rating and review-summarization system can be extended to other product-review domains easily.

Finding point correspondences and determining motion of rigid object from two weak perspective views

Given two images of an n-point configuration which undergoes 3D rotation, translation, and scaling, our problems are (i) How can we match the corresponding points in the two images? Can all the possible mapping be found? (ii) What underlying motions and associated depth components of these points could account for the two images? (iii) Can the object be recovered uniquely? This formulation of the n-point problem is in the most general setting and does not assume attributes or features. A natural question to ask is whether an n-point problem is equivalent to a set of fewer-point problems. This paper presents a method which reduces an n-point problem to a set of 4-point problems. The effort of reduction takes O(n) steps and it also takes O(n) steps to construct all possible mappings of an n-point set from the solution to a 4-point problem. Other results include (1) coplanarity condition of four points in two views, (2) recovering the tilt direction of the rotational axis using four points in two views, (3) recovering the scaling factor.

Structure And Motion From Two Perspective Views Via Planar Patch

We present a method which uses both a planar patch and points for an analysis of time-varying imagery. From our theory, two pieces of information are required for the unique recovery of structure from motion. One is the image motion of a plane and the other one is the image motion of points not on the plane. For the former, one needs four points while for the latter one need two points. Our technique has the following characteristics: (i) it is efficient, (ii) it appears to be very robust over a wide range of simulations, (iii) it sheds some new light on some previous work, and (iv) it has geometric meaning for each step of the derivation of the solution.

Correspondence problem in image sequence analysis

Abstract An algorithm to address the correspondence problem in image sequence analysis is proposed. The underlying philosophy of this algorithm is reductionist, and is based on the assertion that measurements pertaining to motion that are obtained from the whole image sequence must be very similar to the measurements that are obtained from corresponding portions of the image sequence. The algorithm has been extensively tested on synthesized data as well as on real image sequences. It appears that the assumption underlying the proposed algorithm is quite useful and applicable to a large class of images. The proposed algorithm has a high degree of inherent parallelism, and is thus suited for implementation on parallel machines.

Finding point correspondences and determining motion of a rigid object from two weak perspective views

The authors present a method which reduces an n-point problem to a set of four-point problems. The effort of reduction takes O(n) steps and it also takes O(n) steps to construct all possible mappings of an n-point set from the solution to a four-problem. Other results include (1) coplanarity condition of four points in two views, (2) recovering the tilt direction of the rotational axis using four points in two views, and (3) recovering the scaling factor.<<ETX>>

An Unsupervised Automated Essay Scoring System

The proposed automated essay-scoring system uses an unsupervised-learning approach based on a voting algorithm. Experiments show that this approach works well compared to supervised-learning approaches.