Scanning Near-shore Intertidal Terrain Using Ground LiDAR

Intertidal zone refers to the area under and above the water during high and low tides. Traditionally, this zone is not within the scope of land management authorities. Moreover, in accordance with principals set out by existing plans, intertidal zones are excluded from management zones. Boundaries should therefore be set at the land and sea border. Traditionally, methods in determining this have included the traditional theodolite (total station) method, mapping and aerial photography (photogrammetry). However, existing operational restrictions lower efficiency, in addition to increasing time and operational costs. Therefore this paper explores the practicality of a user- friendly, ground-based high resolution laser scanning technology. This method offers easy operation and high-density characteristics with an instrument platform that can be installed on elevated rooftops. High accuracy and resolution is achieved using a stop-and-go method producing Digital Terrain Model (DTM) data. The range of the completed data is 61km in length, 2.5km in width, and -0.5m depth, with a sampling error of approximately ±2cm. Through the implementation discussed in this research, accurate information about the changes of topography in intertidal areas can be obtained

Anticipating Daily Intention using On-Wrist Motion Triggered Sensing

Anticipating human intention by observing one's actions has many applications. For instance, picking up a cellphone, then a charger (actions) implies that one wants to charge the cellphone (intention). By anticipating the intention, an intelligent system can guide the user to the closest power outlet. We propose an on-wrist motion triggered sensing system for anticipating daily intentions, where the on-wrist sensors help us to persistently observe one's actions. The core of the system is a novel Recurrent Neural Network (RNN) and Policy Network (PN), where the RNN encodes visual and motion observation to anticipate intention, and the PN parsimoniously triggers the process of visual observation to reduce computation requirement. We jointly trained the whole network using policy gradient and cross-entropy loss. To evaluate, we collect the first daily "intention" dataset consisting of 2379 videos with 34 intentions and 164 unique action sequences. Our method achieves 92.68%, 90.85%, 97.56% accuracy on three users while processing only 29% of the visual observation on average

Improvement of LiDAR Data Accuracy Using 12 Parameter Affine Transformation

LiDAR data in a local coordinate system may need to be georeferenced and converted into a geographic or projected system. In coordinate transformation, the 7-parameter Helmet transformation method is usually used in measurements to eliminate the systematic errors made by a laser scanner. However, 7-parameter coordinate transformation assumes that there is only one scale error in all of the systematic errors. This study used 12 parameter affine transformation for coordinate transformation of airborne LiDAR data and terrestrial LiDAR data. The LiDAR data accuracy results upon 6-parameter similarity transformation, 7-parameter similarity transformation, and 12-parameter affine transformation were compared. The results showed that using 12-parameter affine transformation the airborne LiDAR and terrestrial LiDAR data have 2-3 times greater accuracy than do 7-parameter or 6-parameter transformations

Wealth Effects of Dividend Announcements on Bondholders: The Case of Taiwan Bond Market

Although bonds play an important role in U.S. capital markets, this financial instrument is less common in the emerging markets. Taiwan is a typical case. In fact, both bond issuances and bond transactions in Taiwan have been declining in the past years. Consistent with the previous studies, this research documents that wealth transfer effects exist between bondholders and stockholders. We hypothesize that this wealth transfer discourages investors from investing in bond markets because companies in Taiwan seem to care less about the interest of bondholders. Using the event study methodology, we examine the price change of bonds and stocks in Taiwan capital market around cash dividend announcements. We find that there are significant abnormal returns before cash dividend announcements from 30 days to 60 days and that there is insignificant price change of bonds during the three-day period around the announcement. Possible explanations of the results include low bond trading volumes, insider trading before announcements, and mixing signaling and wealth transfer effects. Although this study cannot prove that the results are directly related to management holdings, we tend to believe that insider trading somehow matters

Factors affecting airliners\u27 decision of purchasing airplanes

Purchasing aircraft is one of the most critical actions in the functioning of airline companies. The emergence of more interconnected global economies and the expansion of international business operations have led to a steady increase in travel demand after the COVID-19 pandemic. Each aircraft purchase includes a sizable amount of money and consideration. With a clear plan, airline businesses can buy the ideal range of aircrafts for their size and operating requirements. However, if companies buy their fleet without a solid strategy, these expensive aircrafts will become a financial burden to the firms. In this paper, we compare Boeing and Airbus\u27s aircraft capabilities, technical support, and maintenance costs over the aircraft\u27s service life. Subsequently, we discuss the significant elements influencing airline firms\u27 purchasing practices. We conclude by projecting the global fleet market for the next 30 years and the factors that airlines should consider most when buying aircrafts. This paper can assist airlines and aircraft manufacturers in making effective decisions regarding their purchasing policies

A Cascaded Approach for ultraly High Performance Lesion Detection and False Positive Removal in Liver CT Scans

Liver cancer has high morbidity and mortality rates in the world. Multi-phase CT is a main medical imaging modality for detecting/identifying and diagnosing liver tumors. Automatically detecting and classifying liver lesions in CT images have the potential to improve the clinical workflow. This task remains challenging due to liver lesions' large variations in size, appearance, image contrast, and the complexities of tumor types or subtypes. In this work, we customize a multi-object labeling tool for multi-phase CT images, which is used to curate a large-scale dataset containing 1,631 patients with four-phase CT images, multi-organ masks, and multi-lesion (six major types of liver lesions confirmed by pathology) masks. We develop a two-stage liver lesion detection pipeline, where the high-sensitivity detecting algorithms in the first stage discover as many lesion proposals as possible, and the lesion-reclassification algorithms in the second stage remove as many false alarms as possible. The multi-sensitivity lesion detection algorithm maximizes the information utilization of the individual probability maps of segmentation, and the lesion-shuffle augmentation effectively explores the texture contrast between lesions and the liver. Independently tested on 331 patient cases, the proposed model achieves high sensitivity and specificity for malignancy classification in the multi-phase contrast-enhanced CT (99.2%, 97.1%, diagnosis setting) and in the noncontrast CT (97.3%, 95.7%, screening setting)