This paper proposes a method for estimating the state of mind on observation of the body motion from the front and the side of a subject using 2 cameras. There are many researches that studied about the estimation of a psychological condition using facial features. For example, there are studies about facial expressions, eyes' motion and the facial thermal images using a specific camera. Observation of the movement of a body is easier and takes lower cost than one of the facial images. We especially focused on the situation when people feel bored and pleasant. We used the correlation analysis method to find which body motion correlates to the states of a mind. To estimate the state of a mind the authors have focused on the average amount of body motion, and the power (amplitude) of motion in each frequency range. Our experiment shows that when subject feels bored, stretching body up and down, and movements between left and right tend to increase that can be observed from both cameras. Especially, the result of both cameras has shown that the amplitude of stretching body up and down correlates to the state of mind in frequency range from 2.8Hz to 4.0Hz. The varieties of the subjects show many possible dependencies between body motions and the state of a subject's mind.
This paper presents a method for robust watermarking of medical image using DFT. A feature vector of the medical image is utilized to enhance the robustness against rotation, scaling, and translation. The proposed algorithm utilizes the medical image's feature, Hash function, the third party authentication. We describe how to obtain the feature vector of medical image and embed and extract the watermarking. Simulation results demonstrate the proposed algorithm's robustness against common and geometrical attacks.
Enterprise performance evaluation is an effective method to analyze and estimate companies' running conditions and achievements. Aiming at the features of multi inputs and outputs, DEA model is applied to enterprise performance evaluation technology for decision analysis. First of all, evaluation index system is set up with four aspects: staffs, equipment, workshop and production. After that, CCR model is built for evaluating and validating DEA model. At last, professional software LINGO is used to find solutions in every single decision unit of DEA, which provide a scientific and reasonable evaluation method for enterprise performance evaluation, and offering a new technology for enterprise performance evaluation.
Genetic algorithm (GA) is one of optimization algorithm based on an idea for evolution of life. GA can be applied various combination optimization problem. This paper proposes a parallel processor for distributed genetic algorithm (DGA) with redundant binary number. Since a redundant binary number has redundancy, solution expression becomes variegated. For this reason, it is expected the algorithm easily find the optimized solution, and the error rates decrease. Since DGA is a parallel algorithm, the performance can be improved by using a specified parallel processor. The effectiveness of the proposed processor was confirmed by some simulations and experiments using FPGA circuit board.
Discrete Event Simulation (DES) is a popular technique use in network simulation tools. Some of the popular queuing methods in DES are linked list, heap, splay tree and calendar based queue. Due to the design characteristic of discrete events and single sequential queue in DES, these queuing methods are unable to fully utilize the computing power of multi-core processors. This paper proposes Multi-Dimensional Queue Mechanism (MDQM) to counter the stated problem. The experimental results show improvement of simulation run time by using the proposed mechanism when running DES on multi-core processors environments.
The usefulness of information process and management is universally acknowledged by contemporary scholars, but previously researches of the field does not focus much on applications in handling business issues, such as vaccountability auditing, which strongly resembles information process and management in the aspect of data processing. This paper shows how multi-regression approach of information process and management is well applied in resolving business issues, for example verifying effectiveness of vaccountability audit in the national governance. Hence, we execute a multi-regression research and the multi-regression results eventually prove vaccountability-audit researchers' unverified statement that vaccountability audit is an effective tool to improve the performance of national governance. In this example, we delightedly recognize that the approach of information process and management can be instrumental in efficaciously disposing complex data of the business issue. We hopefully expect that further cooperations between these two disciplines will be explored more profoundly on the basis of this paper.
For any given test, the traditional item relational structure theory can be used for detecting the item relational structure of the students. However, we do not know whether each item of the test is efficient or not, for improving above-mentioned drawback, we can first use the Q-matrix theory to obtain a validate test with all items which are efficient. In this paper, efficient items of the multiplications of fractions were constructed accordingly. Using Liu's before-test item structure theory, we can construct the efficient before-test item structural of the test. After testing the students, we can use the traditional item relational structure theory to construct the efficient after-test item structural of the students, and then, using Liu's criterion related validity index, we can evaluate the item relational structure of the test, and the results could be useful for cognitive diagnosis and remedial instruction.
Time delay estimation is a very general problem with wide range of applications. When noisy repetitive signals are observed, the noise cancellation is achieved by averaging perfectly aligned signals. A time delay estimator is developed for determining time delay between signals received on different trials in the presence of uncorrelated noise. The estimator is based on a probabilistic generative model for delayed signals, and tries to find the delay and the source signal simultaneously so that maximum likelihood is achieved. An iterative method based on the Expectation-Maximization algorithm is used for finding maximum likelihood estimate of parameters. The estimator has been tested on three types of synthetic signals. The result shows that it can tolerate 5 to 10dB more noise while achieving the same performance as cross-correlation estimator.
Since the implementation of the national health insurance (NHI) program, the Taiwanese health care industry has become increasingly competitive. The objective of this paper is to study the flow of endoscope examinations in order to improve service quality. Waiting times could be reduced through a more efficient utilization of available equipment. This study proposes a model to analyze the relationship between the availability of equipment and the waiting time for examinees. The model is implemented using empirical data from a health examination center in Taiwan. The proposed model can also be applied to more generalized cases.
This paper takes the methods of panel cointegration test model and panel ECM to study the relationship between environmental regulation intension and construction industry development. By analyzing the panel data of 31 provinces in China from 2003 to 2010, the results show that (a) in the whole country, the dynamic effect of environmental regulation intension on construction industry development is significant positive in a long run. But it is significant negative in a short term. As for the effect of construction industry development on environmental regulation intension, it is positive in a long term and negative in a short term. (b) in the long run, the dynamic effect of environmental regulation intension on construction industry development is larger in high regulation regions than in low regulation regions. In the short term, the dynamic effect of environmental regulation intension on construction industry development is significant in high regulation regions, but it is not significant in low regulation regions. (c) The dynamic effect of construction industry development on environment regulation is significant from a long run, but it is not significant from a short term both in high regulation regions and in low regulation regions.
We simulated transmission path of an emergency event's (taking an H1N1 epidemic as an example) impact on China's economic system within the framework of a Social Accounting Matrix(SAM), opening `Black Box' of the impact of public health incidents on economic system. Based on SAM, structural path analysis (SPA) is an appropriate method to simulate how demand changes transmit in the economic system. And the starting point of an H1N1 epidemic impacting the economic system just lies on demands of residents (for example, increase of medical expenditure; decrease of inbound, outbound tourism; decrease of leisure activity). We constructed a SAM which includes 49 endogenous accounts, 8 exogenous accounts to reflect Chinese economic characteristics. On the basis of multiplier decomposition and SPA, we analyzed which accounts were most influenced by the rise of demands for medical industry influenced directly by H1N1 and the reduction of demands for transportation, post, commerce, restaurant, and entertainment industry closely related to the `face to face' communication. The results show that the increase of medical expenditure triggered the rise of demands for medical staff and pharmaceutical manufacturing in chemical industry; the reduction of the demands for industries closely related to `face to face' communication mainly had an impact on employment, residents' incomes, profit of enterprise and so on. From the simulation results of the SPA, the transmission path of restaurant industry's demand for agricultural labor, the post's demand for production labor and the entertainment's demand for proficient labor only concentrate on dozens of paths in hundreds of thousands of transmission paths. The simulation methods help mining spatial and temporal character of economic data.
Electronic prescription system brings an information technology progress for the traditional hospitals and this paper aims to solve the following four questions related to electronic prescription (EP): 1) why hospitals choose to adopt EP system? 2) Is electronic prescription necessary for hospitals in this paper? 3) Does the EP system have more advantages than disadvantages? 4) When is the right time that hospitals using the electronic prescription to maximize the revenues? We proposed a game theory model to explore these questions and carried out an equilibrium analysis, which generated the results about the profits gained from electronic prescription in some distinctive certain situations. Our findings provide a reference for hospitals to maximize profits from electronic prescription. The results also indicate the feasible suggestions for few technical issues existed in the EP system.
The proposal of various electronic learning contents, e.g. remote education or virtual classrooms, has given a powerful impetus to the E-Learning techniques. However, there still remain several hard and complicated problems unsolved. Especially when compared with e-commerce and medicine, the problems of the recommenders in E-Learning system have not been fully figured out. In this paper, a novel personalized semantic recommendation system (PSRS) for E-Learning is designed. The proposed PSRS system employs the Video Structurized Description (VSD) technique to extract the initial keywords description of the learning contents, and then adopts the lexical parsing technique to refine the descriptive words with a standard format according to the initial keywords. Subsequently, the PSRS adopts rules auto-updating (RAU) to automatically add sequential items into ontology rules. Depending on the specific ontology knowledge with domain rules, semantic mapping and intelligent reasoning techniques are applied to generate certain semantic related recommending items for the active learners. Experimental results indicate that the proposed PSRS preforms better in accuracy than any other existing algorithms.
Aiming at classifying multisource remote sensing images, we first introduce a Markov Random Field (MRF) to build prior probability models for multiple object classes. The Expectation Maximization-Hierarchical Markov Random Field (EM-HMRF) algorithm is then introduced to take advantage of the equivalence relation between the EM-HMRF and the fuzzy classification method. Second, this paper focused on exploiting self-adaptivity for selecting the prior distribution model parameter β automatically, and then two fusion schemes (centralized-based and distributed-based fusion) are introduced to achieve better classification results. A new algorithm is derived for supporting multisource remote sensing image classification by using image fusion and the EM-HMRF. The experimental results on synthetic images and real remote sensing images indicate that our proposed algorithm with two fusion schemes can not only greatly improve the accuracy of image classification but also strengthen the anti-interference of noise, thereby providing good evidence to support the effectiveness and superiority of our proposed algorithm in solving multisource remote sensing image classification problems. Our proposed algorithm for image classification with a fusion scheme should have great potential value for multisource remote sensing image classification strategies.
This manuscript investigates the abuse of Internet usage by college students in Bangkok and metropolitan areas. The remarkable results show that sensation seeking is the important factor to create the abuse of Internet and abuse behavior in computer. Some suggestions are also provided to reduce the problem.
This paper is mainly about an image reconstruction algorithm, which based on bayer template. First, to estimate other components' gradient information through the information of G, according to each component's gradient correlation, including R, G and B. And then get other component's values. The bayer image, whose resolution ratio is 1280*1024, 25 frames per second, captured by CMOS, is output by DVI interface or stored in memory after being interpolated and raised frame. The algorithm is verified on FPGA platform. The experiment shows that the reconstruction image's quality is very high and the PSNR is high to 37 dB.
In this paper, we propose a new approach to Software Requirements Specifications (SRS) or software requirements quality analysis process. We apply the Software Quality Assurance (SQA) audit technique in determining whether or not the required quality standards within the requirements specifications phase are being followed closely. Quality analysis of the SRS is performed to ensure that the software requirements among others are complete, consistent, correct, modifiable, ranked, traceable, unambiguous, and understandable. Here, a new approach that combines case-based reasoning (CBR) and neural network techniques in analyzing SRS quality is proposed. This approach is used in improving the process of analyzing the quality of a given SRS document for a specific project. The CBR technique is used to evaluate the requirements quality by referring to previously stored software requirements quality analysis cases (past experiences). CBR is an artificial intelligence technique that reasons by remembering previously experienced cases, and this technique will speed up the quality analysis process. Neural Network (Artificial Neural Network or ANN) is the type of information processing paradigm that is inspired by the way biological nervous systems (brain) process information. Neural network technique works well with CBR because it also uses examples to solve problems. The new approach proposed in this research aims at enhancing and improving existing methods in analyzing SRS quality. A framework of the proposed approach is the main outcome of this research study.
To study pattern Auto-generation system based on silk fabric properties, it is proposed a concept about rapid generation of individualize pattern. By carrying out instrumental tests on the 3-silk fabric properties and sample wear trials, the regression mathematical models between fabric properties and parameter pattern (the easing of sleeve cap) were established in this dissertation. Using the mathematical models of the parametric pattern and taking silk clothing as examples, it described the realization process of pattern ato-generation system, which was based on physical properties of the silk fabric.
A bilinear matrix system (BMS) is proposed as a general semi-blind learning framework for modeling matrix-formatted data and for extracting matrix-formatted inner factors. Different special cases of this framework lead to a family of typical learning tasks. The problem of learning such a semi-blind BMS learning is formulated as a problem of learning a particular BYY system for estimating unknown parameters and for making model selection. We develop a BYY harmony learning algorithm for learning matrix normal distribution based BMS, which relates to and also generalizes typical learning methods, such as factor analyses, 2D-PCA, and manifold learning, ..., etc, featured with automatic model selection on the bi-perspective dimensions. Also, we apply this algorithm for estimating the profiles of transcriptional factor activities from gene expression data. Moreover, we briefly outline typical applications of BMS, especially a new perspective of Yang domain based hypothesis test versus Ying domain based test, exampled by schematic algorithms and genetic diagnoses applications.
Diagnostic modeling based on computational anatomy is an important topic. In previous work, discrimination method using support vector machine based on principal component analysis of the hippocampus shapes have been proposed. However, disease-specific component was not considered explicitly. In this paper, we propose a method for constructing the disease subspace using orthogonal complement of the normal subspace. The proposed method was tested using the hepatic cirrhosis and hip osteoarthritis datasets and was compared to a previous method. In our experiments, the proposed method was effective for disease discrimination based on organ shapes.
