Parkinson's disease (PD) is the second most common neurodegenerative disorder (after Alzheimer's disease) and directly affects upto 5 million people worldwide. The stages (Hoehn and Yaar) of disease has been predicted by many methods which will be helpful for the doctors to give the dosage according to it. So these methods were brought up based on the data set which includes about seventy patients at nine clinics in Sweden. The purpose of the work is to analyze unsupervised technique with supervised neural network techniques in order to make sure the collected data sets are reliable to make decisions. The data which is available was preprocessed before calculating the features of it. One of the complex and efficient feature called wavelets has been calculated to present the data set to the network. The dimension of the final feature set has been reduced using principle component analysis. For unsupervised learning k-means gives the closer result around 76% while comparing with supervised techniques. Back propagation and J4 has been used as supervised model to classify the stages of Parkinson's disease where back propagation gives the variance percentage of 76-82%. The results of both these models have been analyzed. This proves that the data which are collected are reliable to predict the disease stages in Parkinson's disease.

In this thesis, a chorus detection and an emotion detection algorithm for popular music are proposed. First, a popular music is decomposed into chorus and verse segments based on its color representation and MFCCs (Mel-frequency cepstral coefficients). Four features including intensity, tempo and rhythm regularity are extracted from these structured segments for emotion detection. The emotion of a song is classified into four classes of emotions: happy, angry, depressed and relaxed via two classification methods. One is back-propagation neural network classifier and the other is Adaboost classifier. A test database consisting of 350 popular music songs is utilized in our experiment. Experimental results show that the average recall and precision of the proposed chorus detection are approximated to 95% and 84%, respectively; the average precision rate of emotion detection is 86% for neural network classifier and 92% for Adaboost classifier. The emotions of a song with different cover versions are also detected in our experiment. The precision rate is 92%.

Taiwan is a small island state surrounded by the oceans but with large population. With limited land space, it would be worthwhile considering how to stabilize the existing coast or to create stable artificial beaches. Under the onslaught of storm surge and large wave from typhoons, beach erosion would occur accompanying by formation of a submerged bar beyond the surf zone with the sand removed from the beach. After the storm, the bar material maybe transport back by the swell and predominant waves which helps recover the original beach, thus producing a beach profile in dynamic equilibrium. The main purpose of this research is to use the back-propagation neural network¡]BPNN¡^, which trains a sample model and creates a system for the estimation, prediction, decision making and verification of an anticipated event. By the BPNN, we can simulate the key characteristic parameters for the storm beach profile resulting from typhoon action. Source data for training and verification are taken from the experimental results of beach profile change observed in large-scale wave tank¡]LWT¡^conducted by Coastal Engineering Research Center¡]CERC¡^in the USA in the 1960s and that from the Central Research Institute of Electric Power Industry in Japan in the 1980s. Some of the data are used as training pairs and others for verification and prediction of the key parameters of berm erosion and bar formation. Through literature review and simulation on the related parameters for storm beach profile, methodology for the prediction of the beach profile and bar/berm characteristics can be established.

This thesis is to study the distribution system loss by applying artificial neural networks(ANN). To enhance the efficiency of loss analysis, the distribution system network has been obtained by retrieving that component information for the automated mapping and facility management system (AM/FM). The topology process and node reduction has also been applied to identify the network configuration and the input data for load flow analysis. The load survey study is used to derive the typical load patterns of various customer losses. The monthly energy consumption of customers by each transformer, which has been retrieved for the customer information system(CIS), is used to derive the hourly loading of each distribution transformer. The three phase load flow analysis has been performed for different types of distribution feeders to solve feeder loss to generate the data set for the training and testing of neural networks. The ANN for distribution loss analysis, which has been obtained after network training, can solve the distribution system loss very efficiently according to the feeder load demand, length, transformer capacity and voltage level. With short feeder length and voluminous customers served by the distribution feeders in urban area, the transformer core loss and secondary line loss contribute most of the distribution feeder loss. On the other hand, the line loss of rural distribution feeder is more significant because of the longer distribution lines to serve more scattering customers. With the neural based distribution system loss modeling, the distribution system loss can be estimated very easily, which can provide Taipower a good reference to enhance the operation efficiency of distribution system.

The thesis illustrates the development of DSP-based systems-¡§DSP Based Face Characteristic Extraction and Identity Recognition System¡¨.The principal system consists of three major subsystems and two kinds of structure of recognition algorithms.Three major subsystems are Image Acquisition System.Image Preprocessing System,and face characteristic extraction individually.Two kinds of structure are Competitive Neural Network and Gaussian mixture model respectively In actual proving,we adopt colored half-length face image alone only face image,and simulate on PC.In order to acquire the characteristic parameter with the different parts to the people faces , and then achieve the purpose that the identity discerns.Finally implant it to DSP .Shown by the experimental result,this system can really reach the anticipative goal,and gain good recognition and efficiency.

An electric power system consists of the generating stations, the transmission lines, and the distribution systems. Transmission lines are the connecting links between the generating stations and the distribution systems. With the rapid growth of economy and technology, the demand for large blocks of power, power quality and increased reliability suggested the interconnection of neighboring systems. Transmission lines are elements of a network which connects the generating plants to the distribution systems, and could extend hundreds of miles . Because of the long distances traversed by transmission lines over open area, they tend to fade by natural and artificial calamity imposed on the power system. It maybe easy to discover the fault with sufficient information in the populous region. When fault occurs in the remote region, it is difficult to identify the outage location. An efficient and reliable technique is thus desirable to resolve the problem. This dissertation presents the fault location for high voltage lines with Artificial Neural Network( ANN ) method. Beside the fault location, this research also improve the problem further by considering the fault resistance. The fault resistance may not remain the same due to the variation of environmental factors. The fault location may involve errors owing to the fault resistance. An algorithms has been developed in this dissertation to calculate fault resistance and revise the ANN training data for three-phase fault, double line-to-ground fault, single line-to-ground fault, and line-to-line fault. To verify the effectiveness of the method, practical transmission lines were used for tests. The results proved that the method could be used to identify the fault location effectively and help dispatchers determine a reference distance.

­^¤å´£­n¡G A utility function model of individual credit card holder based on their spending behavior is constructed in this research. An accumulation of the individual utility of three different risk attitudes of cardholders may be useful for promoting the profits of credit card business for banks. Due to the privacy of cardholders and the lack of real data, a questionnaire sampling is used to collect data for this study. A result of this experimental study indicates that credit card holders with a different sex, age, level of education, asset condition, seniority, and occupation have different risk tendency. Based on 249 effective samples in this research, credit card holders who belong to females, teenagers, relatively low educated, without real estate, middle seniority, and relatively volatile occupation are more risk seeking. Relatively risk seeking credit card holders have the tendency to make use of their revolving credit and to borrow cash or to buy financial products with their credit cards. For those with three different risk attitudes, their default of credit card loans are not significantly different. The finding indicates relatively risk seeking cardholders may contribute more profits to the credit card business for banks. A risk attitude classification model built by artificial neural network has also been developed. The model may assist banks' administrators using their applicants' demographics to distinguish their risk attitude for approving an appropriate credit limit for a cardholder's expenditure to promote the total credit card profit for banks.

The air quality in Kaohsiung and Ping-Dong district is the worst in Taiwan. The air pollution episodes in Kaohsiung are attributed to high concentrations of PM10 and O3. Among them, over half of the episodes result from PM10. In addition to Pollutant Standards Index (PSI), atmospheric visibility is also an indicator of ambient air quality. Citizens always complain about the impairment of visibility because it can be visualized directly. Visibility is closely correlated to both air pollutants and meteorological condition. Extinction of visible light by fine particles is the major reason for visibility impairment. In this study, an artificial neural network was applied to predict the concentration of PM10 and atmospheric visibility. The objectives of this study were to investigate the effects of meteorological factor and air pollutants on visibility and to apply artificial neural network to predict the concentration of PM10 and atmospheric visibility. The measured PM10 data were divided into two parts (i.e. summer and winter, ) to understand whether different season affect the prediction of PM10 concentration. The modeling results showed that the optimum input variables included the PM10 concentration, atmospheric pressure, surface radiation, relative humidity, atmospheric temperature, and cloud condition. The network outputs showed high correlation with measured PM10 concentration (R=0.876) in the whole-year set. Furthermore, the prediction of summer set also showed high correlation with measured PM10 concentration (R=0.753). The winter set demonstrated the worse prediction among three sets, and showed medium correlation with measured PM10 concentration (R=0.553). The visibility network test was conducted by two stages. The first stage (set-1~set-3) showed that relative humidity, atmospheric temperature, and cloud condition were the most important meteorological factors, while PM10, O3, and NO3 were the most important air pollutants on the prediction of atmospheric visibility. The prediction of set-1 considering only meteorological factors was the worst (R=0.586), while set-3 was the best and showed medium correlation with measured atmospheric visibility (R=0.633). The second stage (set-4 and set-5) increased the hidden neuron numbers and input variables, and added atmospheric visibility in the input variables. Although the correlation coefficients between predicted and measured data did not increase, the prediction of atmospheric visibility had significant improvement. Finally, a short-term prediction of PM10 and atmospheric visibility was conducted and validated by the level of PSI values and atmospheric visibility. Prediction results showed that the accuracy of PM10 prediction was 76.9%, while the prediction of atmospheric visibility by set-3 network demonstrated an accuracy of 76.9%. Moreover, no significant difference of prediction was detected by using either three-level or five-level visibility systems.

Abstract The traditional fault inspection of the motorcar engine cannot detect the noise and sound signal resulted from the abnormalities of some mechanical parts. For instance, the cylinder misfires; the looseness of the fan belt is irregular; the valve clearance is out of order¡K. and so on. When the fault message cannot be delivered by the ECU of the computer, the skilled senior engineers are required at this moment to make the experiential judgments. In the present society, due to the development of information, the computer technology makes progress by leaps and bounds. If we can make use of the monitoring method by the Acoustic signal instrument, build up a set of complete and efficient fault diagnosis system through the computer software and apply speedy and accurate way to assist the repairmen in relocating the causes for such faults, the accuracy of inspection can be greatly enhanced with a huge help in the preventive maintenance work. In that case, the fault conditions of the engine can be validated precisely and effectively, so the overhaul efficiency of the engine can be upgraded to a large extent. In this article, the procedures of sound signal recording will be brought forward by linking the digital camera with such a recording equipment as the high-precision microphone to make records of the fault sounds made when the engine runs. It uses the frequency analyzer to conduct the sampling and combine the computer software to further process and analyze the same. Finally the character parameters will be obtained. By applying the mathematical exercise of ¡§Back-Propagation Neural Network¡¨ to undertake the training and detection of the sounds for the purpose of identifying the kinds of the faults. It replaces the errors caused from the experiential judgments made by the expert senior engineers. In terms of the training and maintenance ability of the newly recruited technical repairmen, their capability for exact and reasonable recognition of the fault types is substantially promoted. Keywords¡GAcoustic Signal¡ABack Propagation Neural Network

Abstract On the whole, there are two ways to dispose of multiclass classification problem. One is deal it with directly. And the other is dividing it into several binary-class problems. For this reason, it will be simpler as regards individual binary-class problems. And it can improve the accuracy of the multiclass classification problem by reorganize the effect. So how to decompose several binary-class problems is the most important point. Here, based on our study, we use Dynamic Programming as foundation to get the optimal solution of multiclass¡¦s decomposition. Not only get it simplify but also can achieved the best classified result.