In the wake of rising energy costs, there is a critical need for sustainable energy management of commercial and residential buildings. Buildings consume approximately 40% of total energy consumed in the US, and current methods to reduce this level of consumption include energy monitoring, smart sensing, and advanced integrated building control. However, the building industry has been slow to replace current PID and rule-based control strategies with more advanced strategies such as model-based building control. This is largely due to the additional cost of accurately modeling the dynamics of the building and the general uncertainty that model-based controllers can be reliably used in real conditions. The first half of this thesis addresses the challenge of constructing accurate grey-box building models for control using model identification. Current identification methods poorly estimate building model parameters because of the complexity of the building model structure, and fail to do so quickly because these methods are not scalable for large buildings. Therefore, we introduce the notion of parameter identifiability to determine those parameters in the building model that may not be accurately estimated and we use this information to strategically improve the identifiability of the building model. Finally, we present a decentralized identification scheme to reduce the computational effort and time needed to identify large buildings. The second half of this thesis discusses the challenge of using uncertain building models to reliably control building temperature. Under real conditions, building models may not match the dynamics of the building, which directly causes increased building energy consumption and poor thermal comfort. To reduce the impact of model uncertainty on building control, we pose the model-based building control problem as a robust control problem using well-known H1 control methods. Furthermore, we introduce a tuning law to reduce the conservativeness of a robust building control strategy in the presence of high model uncertainty, both in a centralized and decentralized building control framework.

This thesis examines the British Joint Intelligence Bureau (JIB), which, between its creation in 1946 and its end in 1964, gathered, collated and processed topographic, economic, scientific, and atomic intelligence. It did so on an inter-service, national level. The thesis examines the creation of the organisation, in the aftermath of the Second World War, exploring what factors and which people supported the creation of the new agency. It then moves on to examine the work of the JIB in several of its key fields of work, namely topography, economics and monitoring the threat from Soviet nuclear forces, before examining some of the JIB’s international connections and how these contributed to its work. It concludes with an examination of how the JIB begat the Defence Intelligence Staff (DIS). It argues that the creation of the JIB was an appropriate response to the need to centralise and retrench in the intelligence machinery after the War, but that the organisation, in essence, represented a compromise between those who wanted to fully centralise military (and military-relevant) intelligence and those who wished to preserve service independence. Over the course of its existence it made important contributions to several key areas of policy – including mapping the Soviet Union for nuclear strike planning, the economic containment of the USSR, as well as China and North Korea during the Korean War, and in monitoring the production of Soviet bombers and missiles – before becoming a central component of the new DIS.

近年來，在公開的大規模人工標注數據集上訓練通用行人檢測器的方法有了顯著的進步。然而，當通用行人檢測器被應用到一個特定的，未公開過的場景中時，它的性能會不如預期。這是由待檢測的數據（源樣本）與訓練數據（目標樣本）的不匹配，以及新場景中視角、光照、分辨率和背景噪音的變化擾動造成的。 / 在本論文中，我們提出一個新的自動將通用行人檢測器適應到特定場景中的框架。這個框架分為兩個階段。在第一階段，我們探索監控錄像場景中提供的特定表征。利用這些表征，從目標場景中選擇正負樣本並重新訓練行人檢測器，該過程不斷迭代直至收斂。在第二階段，我們提出一個新的機器學習框架，該框架綜合每個樣本的標簽和比重。根據這些比重，源樣本和目標樣本被重新權重，以優化最終的分類器。這兩種方法都屬於半監督學習，僅僅需要非常少的人工干預。 / 使用提出的方法可以顯著提高通用行人檢測器的准確性。實驗顯示，由方法訓練出來的檢測器可以和使用大量手工標注的目標場景數據訓練出來的媲美。與其它解決類似問題的方法比較，該方法同樣好於許多已有方法。 / 本論文的工作已經分別於朲朱朱年和朲朱朲年在杉杅杅杅計算機視覺和模式識別會議（权杖材杒）中發表。 / In recent years, significant progress has been made in learning generic pedestrian detectors from publicly available manually labeled large scale training datasets. However, when a generic pedestrian detector is applied to a specific, previously undisclosed scene where the testing data (target examples) does not match with the training data (source examples) because of variations of viewpoints, resolutions, illuminations and backgrounds, its accuracy may decrease greatly. / In this thesis, a new framework is proposed automatically adapting a pre-trained generic pedestrian detector to a specific traffic scene. The framework is two-phased. In the first phase, scene-specific cues in the video surveillance sequence are explored. Utilizing the multi-cue information, both condent positive and negative examples from the target scene are selected to re-train the detector iteratively. In the second phase, a new machine learning framework is proposed, incorporating not only example labels but also example confidences. Source and target examples are re-weighted according to their confidence, optimizing the performance of the final classifier. Both methods belong to semi-supervised learning and require very little human intervention. / The proposed approaches significantly improve the accuracy of the generic pedestrian detector. Their results are comparable with the detector trained using a large number of manually labeled frames from the target scene. Comparison with other existing approaches tackling similar problems shows that the proposed approaches outperform many contemporary methods. / The works have been published on the IEEE Conference on Computer Vision and Pattern Recognition in 2011 and 2012, respectively. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Wang, Meng. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 42-45). / Abstracts also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- PedestrianDetection --- p.1 / Chapter 1.1.1 --- Overview --- p.1 / Chapter 1.1.2 --- StatisticalLearning --- p.1 / Chapter 1.1.3 --- ObjectRepresentation --- p.2 / Chapter 1.1.4 --- SupervisedStatisticalLearninginObjectDetection --- p.3 / Chapter 1.2 --- PedestrianDetectioninVideoSurveillance --- p.4 / Chapter 1.2.1 --- ProblemSetting --- p.4 / Chapter 1.2.2 --- Challenges --- p.4 / Chapter 1.2.3 --- MotivationsandContributions --- p.5 / Chapter 1.3 --- RelatedWork --- p.6 / Chapter 1.4 --- OrganizationsofChapters --- p.9 / Chapter 2 --- Label Inferring by Multi-Cues --- p.10 / Chapter 2.1 --- DataSet --- p.10 / Chapter 2.2 --- Method --- p.12 / Chapter 2.2.1 --- CondentPositiveExamplesofPedestrians --- p.13 / Chapter 2.2.2 --- CondentNegativeExamplesfromtheBackground --- p.17 / Chapter 2.2.3 --- CondentNegativeExamplesfromVehicles --- p.17 / Chapter 2.2.4 --- FinalSceneSpecicPedestrianDetector --- p.19 / Chapter 2.3 --- ExperimentResults --- p.20 / Chapter 3 --- Transferring a Detector by Condence Propagation --- p.24 / Chapter 3.1 --- Method --- p.25 / Chapter 3.1.1 --- Overview --- p.25 / Chapter 3.1.2 --- InitialEstimationofCondenceScores --- p.27 / Chapter 3.1.3 --- Re-weightingSourceSamples --- p.27 / Chapter 3.1.4 --- Condence-EncodedSVM --- p.30 / Chapter 3.2 --- Experiments --- p.33 / Chapter 3.2.1 --- Datasets --- p.33 / Chapter 3.2.2 --- ParameterSetting --- p.35 / Chapter 3.2.3 --- Results --- p.36 / Chapter 4 --- Conclusions and Future Work --- p.40

We evaluate the external effects of 183 large mergers at the market level by assessing the impact on the main competitors of the merging firms. Using synthetic control groups and difference in difference estimation, we find that the return on assets of rival firms increases significantly after a merger. The size of the effect varies strongly with market characteristics and the intensity of competition.

Unmanned aerial vehicle technology is rapidly maturing. In recent years, the sight of hobbyist aircraft has become more common. Corporations and governments are also interested in using drone aircraft for applications such as package delivery, surveillance and communications. These autonomous UAV technologies demand robust systems that perform under any circumstances. Many UAV applications rely on GPS to obtain information about their location and velocity. However, the GPS system has known vulnerabilities, including environmental signal degradation, terrestrial or solar weather, or malicious attacks such as GPS spoofing. These conditions occur with enough frequency to cause concern. Without a GPS signal, the state estimation in many autopilots quickly degrades. In the absence of a reliable backup navigation scheme, this loss of state will cause the aircraft to drift off course, and in many cases the aircraft will lose power or crash. While no single approach can solve all of the issues with GPS signal degradation, individual events can be addressed and solved. In this thesis, we present a system which will return an aircraft to its launch point upon the loss of GPS. This functionality is advantageous because it allows recovery of the UAV in circumstances which the lack of GPS information would make difficult. The system presented in this thesis accomplishes the return of the aircraft by means of onboard visual navigation, which removes the dependence of the aircraft on external sensors and systems. The system presented here uses an downward-facing onboard camera and computer to capture a string of overlapping images (keyframes) of the ground as the aircraft travels on its outbound journey. When a signal is received, the aircraft switches into return-to-home mode. The system uses the homography matrix and other vision processing techniques to produce information about the location of the current keyframe relative to the aircraft. This information is used to navigate the aircraft to the location of each saved keyframe in reverse order. As each keyframe is reached, the system programmatically loads the next target keyframe. By following the chain of keyframes in reverse, the system reaches the launch location. Contributions in this thesis include the return-to-home visual flight system for UAVs, which has been tested in simulation and with flight tests. Features of this system include methods for determining new keyframes and switching keyframes on the inbound flight, extracting data between images, and flight navigation based on this information. This system is a piece of the wider GPS-denied framework under development in the BYU MAGICC lab.

Three types of acoustic nonlinearity parameters for solids are discussed. The results of measurements of these parameters for three polymers--polymethyl methacrylate, Polystyrene, and polysulfone--are presented.;The author has developed a new technique, using piezoelectric transducers directly bonded to the specimens, which allows the measurements of fundamental and second harmonics generated in the solids, and thereby the determination of nonlinearity parameter {dollar}\beta\sb3{dollar}, which is the ratio of a linear combination of second- and third-order elastic coefficients to the second-order elastic coefficient.;The second nonlinearity parameter, B/A can be determined from the temperature and pressure derivatives of the sound velocity. We derive its exact relationship for the case of solids. The results from the two techniques are shown to be consistent.;The pressure derivative of the sound velocity is also related to the Gruneisen parameter, which can be used to describe the anharmonicity of interactions in polymer molecules, especially of interchain vibrations. The interchain specific heat for these polymers is then calculated from the Gruneisen parameters; and the characterization of polymers by using these thermoacoustic parameters is discussed.

We have developed four generations of sonar scanning systems to automatically interpret surrounding environment. The first two are stationary 3D air-coupled ultrasound scanning systems and the last two are packaged as sensor heads for mobile robots. Template matching analysis is applied to distinguish simple indoor objects. It is conducted by comparing the tested echo with the reference echoes. Important features are then extracted and drawn in the phase plane. The computer then analyzes them and gives the best choices of the tested echoes automatically. For cylindrical objects outside, an algorithm has been presented to distinguish trees from smooth circular poles based on analysis of backscattered sonar echoes. The echo data is acquired by a mobile robot which has a 3D air-coupled ultrasound scanning system packaged as the sensor head. Four major steps are conducted. The final Average Asymmetry-Average Squared Euclidean Distance phase plane is segmented to tell a tree from a pole by the location of the data points for the objects interested. For extended objects outside, we successfully distinguished seven objects in the campus by taking a sequence scans along each object, obtaining the corresponding backscatter vs. scan angle plots, forming deformable template matching, extracting interesting feature vectors and then categorizing them in a hyper-plane. We have also successfully taught the robot to distinguish three pairs of objects outside. Multiple scans are conducted at different distances. A two-step feature extraction is conducted based on the amplitude vs. scan angle plots. The final Slope1 vs. Slope2 phase plane not only separates the rectangular objects from the corresponding cylindrical.

The study on animal models to investigate different human diseases has made a remarkable contribution to the field of biomedical science. Many studies demonstrating the behavior activities of Drosophila have been studied with a motion tracking system. Most of these studies have been performed by the conventional paradigm where the Drosophila is held fixed by tethering or within the limited space. In this paper, we demonstrate the Active Omnidirectional Treadmill System (AOT) which enables the physically unrestricted Drosophila to navigate infinitely in two-dimensional space with the privilege of real-time tracking. The system maintains the position of the fly at a specific position by compensating the motion of the fly by counter direction motion of the sphere. The system is capable of maintaining the position error < 1000 µm for 89.7% of the time and the angular error < 5o 80.0% of the time. The study evaluates the performance of the different feedback control system through the experimental and simulation results. The successful phototaxis experiment was conducted to validate the usability of the system. The integration of the treadmill system with other peripherals for olfactory, vision and thermal stimuli can be a powerful tool to study the longitudinal behavior of the Drosophila. Further, the integration of the system with the optical microscopic device can be used to perform the brain imaging of the walking Drosophila.

This thesis describes a digital control system used by the Network Analog Group of the Bonneville Power Administration. This system, the Random Sequence Closing Control System, provides automatic control for a special purpose analog computer used in the study of switching surge over voltages on power transmission lines. This system, which uses pseudorandom data, has made it feasible to analyze switching surge phenomena on a statistical basis.

The imposition of water quality controls may affect the economy chiefly by altering aggregate production and changing the factor payments, These two effects could not only reallocate resources among production possibilities, but also could change the distribution of benefits of production among members of the society. This study attempted to provide a workable theory to establish an empirical test of the impacts of water quality controls on family income distribution. It consists of two separate areas: first, to analyze methodologies of measuring income distribution changes, and , second, to develop a theoretical model that is useful for empirical tests of the impacts of different water quality controls. A number of alternative probability density functions have been proposed as models of personal income distribution. The lognormal, displaced lognormal, gamma, and beta distribution functions were considered as appropriate methodologies, since each allows more productive power for income distribution as suggested in the past literature. Detailed information on income distribution can be extracted from the approximations of the distribution functions. One of the objectives of the research was to evaluate the different methodologies for usefulness. The gastwirth bounds for Gini coefficient were used as the test of goodness to fit; the beta density was clearly superior to the other densities for the SMSA data. Next, a theoretical model was constructed, emphasizing the production sector and the distribution sector. Water quality controls were introduced in the production process as a negative input. Water quality data were collected for all states, and indices of quality were estimated using analysis of variance techniques. The equilibrium conditions in commodity and factor markets generated the first impacts of water quality controls on total output and factor payments in the economy. The specific assumption was made as a theoretical bridge connecting family income distribution and factor payments in the distribution sector. It was assumed that a family's income equals total payments received from owned labor and capital in the production process. Thus, changes in factor payments and total output were included in the distribution equations. Water quality controls would, therefore, effect family income distribution through changes in total output and changes in factor payment. The simultaneous equation regression results for 72 SMSA's were not conclusive. It appeared that water quality parameter may effect the wage rate and total output, if the parameter was not, in fact, a surrogate for other excluded variables in the system. The effect of wage changes on income distribution was not significant, but changes in total output appeared to be the most significant variable in the distribution equations. In an attempt to account for the many variables which might be expected to effect income distribution, factor analysis was performed on the SMSA's. Two groups of SMSA's were identified and regressions were performed for these groups. Results from these regressions were similar in sign to the results from the 172 observations regressions, although many of the coefficients were not significant. Interpreting the results of the research was somewhat difficult, even though some results did appear consistent among all regressions. It does appear that there is some evidence to indicate that water quality controls lead to less equal family income distribution. Better data are required from more complete and accurate analysis. The principle thrust of the study was to develop a model to organize the complexity of economic causality with respect to income distribution change and water quality policy. It appeared that this type of systematic econometric approach can be fruitful in analyzing income distribution change.