The national government gave up defense of mainland Chinese and a large number of accompanying troops retreated into Taiwan in 1949. After many years, these soldiers continually retired from the military. In order to take care of these groups of retired soldiers who had contributed to the nation, the Taiwanese government set up a veteran status system. These veterans are also Taiwanese citizens, but they hold a double citizenship, and they can benefit from different departments. This study started with veterans¡¦ social capital points of view in order to see the relationships between the soldiers¡¦ knowledge of welfare, level of needs for welfare use, their satisfaction with welfare, and the number of social capital they have received. The research randomly selected samples and used a face-to-face survey with 250 veterans who are Kaohsiung city citizens and aged 65 and over. Effective surveys of 225 were received. The results of the research show that the age factor influences the participants¡¦ satisfaction about the welfare system. The participants¡¦ veteran ranks, religious beliefs, and current living conditions are related to their knowledge and use of the welfare offered by the government. Veterans with middle-low income have less knowledge of the welfare from the Veterans Affairs Commission and the Department of Health, and also know less about the welfare items offered by the Department of Social Welfare. From the social capital point of view, the more abundant social capital the participants have, the higher knowledge of welfare they have. However, different social capital systems will also affect satisfaction with welfare used. Veterans are more satisfied with using welfare if social capital is abundant in the family system, the neighborhood, community system, and the Veterans Affairs Commission. On the other hand, veterans are less satisfied with using welfare if social capital abundance is evident in the Department of Social Welfare and other systems. In addition, the results also show a significant positive relationship between the participants¡¦ knowledge and use of welfare. The more welfare items they know, the more they use. These results offer suggestions to the individual government departments, including the Veterans Affairs Commission, Department of Social Welfare, and Department of Health, as well as interdepartmental collaboration. Each of the related departments should strengthen their welfare campaigns and service mechanisms. Most importantly, they should improve their integration in order to avoid overlapping and waste on welfare services. In addition, they must set up good referral and transition services and carry out the welfare measures to benefit the veterans in need.

This thesis presents a part of the research work at Virginia Tech on developing a prototype automatic luggage scanner for explosive detection, and it deals with the automatic detection of elongated objects (detonators) in x-ray images using matched filtering, the Hough transform, and information fusion techniques. A sophisticated algorithm has been developed for detonator detection in x-ray images, and computer software utilizing this algorithm was programmed to implement the detection on both UNIX and PC platforms. A variety of template matching techniques were evaluated, and the filtering parameters (template size, template model, thresholding value, etc.) were optimized. A variation of matched filtering was found to be reasonably effective, while a Gabor-filtering method was found not to be suitable for this problem. The developed software for both single orientations and multiple orientations was tested on x-ray images generated on AS&E and Fiscan inspection systems, and was found to work well for a variety of images. The effects of object overlapping, luggage position on the conveyor, and detonator orientation variation were also investigated using the single-orientation algorithm. It was found that the effectiveness of the software depended on the extent of overlapping as well as on the objects the detonator overlapped. The software was found to work well regardless of the position of the luggage bag on the conveyor, and it was able to tolerate a moderate amount of orientation change. / Master of Science

This project focuses on semi-supervised human activity recognition (HAR) tasks, in which the inputs are partly labeled time series data acquired from sensors such as accelerometer data, and the outputs are predefined human activities. Most state-of-the-art existing work in HAR area is supervised now, which relies on fully labeled datasets. Since the cost to label the collective instances increases fast with the increasing scale of data, semi-supervised methods are now widely required. This report proposed two semi-supervised methods and then investigated how well they perform on a partly labeled dataset, comparing to the state-of-the-art supervised method. One of these methods is designed based on the state-of-the-art supervised method, Deep-ConvLSTM, together with the semi-supervised learning concepts, self-training. Another one is modified based on a semi-supervised deep learning method, LSTM initialized by seq2seq autoencoder, which is firstly introduced for natural language processing. According to the experiments on a published dataset (Opportunity Activity Recognition dataset), both of these semi-supervised methods have better performance than the state-of-the-art supervised methods. / Detta projekt fokuserar på halvövervakad Human Activity Recognition (HAR), där indata delvis är märkta tidsseriedata från sensorer som t.ex. accelerometrar, och utdata är fördefinierade mänskliga aktiviteter. De främsta arbetena inom HAR-området använder numera övervakade metoder, vilka bygger på fullt märkta dataset. Eftersom kostnaden för att märka de samlade instanserna ökar snabbt med den ökade omfattningen av data, föredras numera ofta halvövervakade metoder. I denna rapport föreslås två halvövervakade metoder och det undersöks hur bra de presterar på ett delvis märkt dataset jämfört med den moderna övervakade metoden. En av dessa metoder utformas baserat på en högkvalitativ övervakad metod, DeepConvLSTM, kombinerad med självutbildning. En annan metod baseras på en halvövervakad djupinlärningsmetod, LSTM, initierad av seq2seq autoencoder, som först införs för behandling av naturligt språk. Enligt experimenten på ett publicerat dataset (Opportunity Activity Recognition dataset) har båda dessa metoder bättre prestanda än de toppmoderna övervakade metoderna.