Summary of the MIP-based Optimizing Approaches Although they are both NP-hard, the two problems of LoD1 building aggregation and facade structure homogenization that were investigated as[r]

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When focusing on **the** second specific objective (second part **of** **the** thesis), **the** core aspect **of** it was to integrate image data for improving **the** planimetrical and topological accuracies **of** **the** reconstructed **models**. This objective was also achieved, while contributing several innovative **aspects** to **the** scientific community. It is already proved that **the** object space line segments can be derived by **the** matching **of** image-based line segments in projective geometry through **the** intersection **of** viewing ray planes. For this matching process, scene constraints were incorporated for minimizing **the** matching ambiguities within this study. Three well-defined evidences were determined with respect to **the** scene i.e. roof **models** reconstructed from point clouds. **The** gradient **of** a roof outline, distance **of** a point to **the** plane and symmetry between two gutters belonging to two opposite roof pairs were defined in this scenario. Fault correspondences representing edges on **the** footprint, or beneath **the** roof outline, or even somewhere on **the** wall, were avoided using **the** first two constraints. Having identified **the** rough symmetries, ambiguities especially relevant to oblique roofs, were further avoided. Similar to many other researchers, this experiment also had to deal with incompleteness issues such as gaps. In addition, some erroneous derivations, such as deviated edges for eave lines, were also found. **The** effects **of** these defects were avoided by predicting **the** most probable boundary edges that could be used to represent such cases. In this regard, known structural arrangements **of** roof **models** and especially defined convergence priors were applied. Although some false positive and negative narrow regions were given for some roof outlines, **the** process enabled to accomplish complete refined roof boundaries for each roof model. **The** evaluation results showed an increased planimetric accuracy (0.55m) for **the** refined **building** **models**. It is **the** highest planimetric accuracy when compared with **the** results **of** other methods submitted to **the** ISPRS project. As such, obviously this is a considerable achievement. In addition, correctness **of** per-object level accuracy and topological accuracy **of** refined **models** reached **the** peak i.e. 100%. These statistics prove that refinement strategies increased both **the** planimetric and topological accuracies considerably.

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In this paper, we present a method for regularizing noisy **3D** reconstructions, which is especially well suited for scenes containing planar structures like buildings. At hori- zontal structures, **the** input model is divided into slices and for each slice, an inside/outside labeling is computed. With **the** outlines **of** each slice labeling, we create an irregularly shaped volumetric cell decomposition **of** **the** whole scene. Then, an optimized inside/outside labeling **of** these cells is computed by solving an energy minimization problem. For **the** cell labeling **optimization** we introduce a novel smooth- ness term, where lines in **the** images are used to improve **the** regularization result. We show that our approach can take arbitrary dense meshed point clouds as input and delivers well regularized **building** **models**, which can be textured af- terwards.

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condition **of** road networks structural and architectural **aspects** **of** cultural landmarks and historic buildings (Randall, 2013). All these can serve as physical (digital) records. Scanning buildings has to date mostly used static terrestrial scanners based on a tripod system. One important myth to address is that scanners are optical systems, only what **the** scanner can “see” is captured, thus scanners cannot go through walls or other obstructions (Randall, 2013). Although static scanning technology delivers good results when scanning **the** outside **of** a **building** there are a number **of** limitations when scanning indoor locations due to **the** need to use “tie points” with physical targets to create a reference frame. **The** manual placement **of** **the** laser scanner on multiple stations interrupts **the** scanning and thus reduces **the** scanning rate (points per second). **The** placement **of** tie points requires additional manual effort. New technologies; Indoor Mobile Mapping Systems (IMMS) and Simultaneous Location and Mapping (SLAM) are emerging as **the** most prominent systems for indoor mapping (Thomson, Apostolopoulos, Backes, & Boehm, 2013). **3D** scanning creates a foundation for a BIM approach by capturing existing conditions in a highly accurate, 3-dimensional format that can be used as a basis for developing project designs (Randall, 2013). PAS 1192-2 (BSi, 2013) also notes that a point cloud survey shall be provided to verify **the** completeness **of** **the** as-constructed model. For FM a key application **of** scanning is for as-built recording, or assessment **of** project performance to support project guarantees during **the** “as constructed” phase **of** a project (Randall, 2013).

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where x denotes **the** real vector **of** unknown parameters (**the** point movements) and v denotes **the** real vector **of** unknown residuals, that is, **the** degree **of** constraint satisfaction. Both, A (referred to as **the** design matrix) and l (**the** vector **of** observations) need to be speciﬁed in advance to deﬁne **the** constraints. **The** constraints are perfectly satisﬁed if v = 0. As this is generally not possible for all constraints, **the** function v T ·P ·v is minimized, where P deﬁnes **the** weights between diﬀerent constraints. If there are non-linear constraints, these are usually replaced by their linear approximations. Sarjakoski & Kilpel¨ ainen (1999) and Harrie & Sarjakoski (2002) show how to solve **the** problem for large datasets, also considering other **generalization** operators. Applying **the** same adjustment technique, Koch & Heipke (2005) and Koch (2007) additionally show how to cope with hard inequality constraints that are needed to ensure consistency between DLMs and digital terrain **models**. Related problems are discussed in **the** **generalization** domain, for example, a river must not run uphill (Gaﬀuri, 2007). Least squares adjustment allows diﬀerent **generalization** operators to be handled, yet **the** existing **generalization** methods that are based on this technique do not take **the** discrete nature **of** map **generalization** into account. Usually, continuous variables are used to model a problem. These are not suited, for example, to represent whether a vertex **of** an original line is selected for its simpliﬁcation. In their system, Sarjakoski & Kilpel¨ ainen (1999) deﬁne a constraint that attempts to pull an unwanted vertex onto **the** line connecting its predecessor and successor. This is a smart workaround to also allow for line simpliﬁcation, but **of** course it is not a solution to **the** discrete problem **of** vertex selection, which only allows two stages and none in between. Sester (2005) applies adjustment calculus to satisfy constraints in **building** simpliﬁcation, but also points out that it does not solve **the** whole problem: **the** elimination **of** details is done in a ﬁrst step, which is not based on **optimization**. **The** handling **of** hard constraints in **optimization** approaches is seldom addressed in **the** map **generalization** literature. Often constraints are relaxed, as they are conﬂicting (Harrie & Weibel, 2007). A few exceptions exist in **the** context **of** discrete **optimization**, which is addressed in **the** next section.

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This report presents a practical approach to stacked **generalization** in surrogate model based **optimization**. It exemplifies **the** integration **of** stacking methods into **the** surrogate model **building** process. First, a brief overview **of** **the** current state in surrogate model based opti- mization is presented. Stacked **generalization** is introduced as a promising ensemble surrogate modeling approach. Then two examples (**the** first is based on a real world application and **the** second on a set **of** artificial test functions) are presented. These examples clearly illustrate two properties **of** stacked **generalization**: (i) combining information from two poor performing **models** can result in a good performing model and (ii) even if **the** ensemble contains a good performing model, combining its information with information from poor performing **models** results in a relatively small performance decrease only.

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Explicit expressions for **the** generating functions **of** one-sided, two-sided and three- sided PWs have been found so far. **The** first class consists **of** partially directed walks and has a rational generating function. **The** second class was shown to have an algebraic generating function by Duchi [Duc05] and recently [BM08] **the** third class was solved and **the** generating function was found not to be D-finite. A (possibly multivariate) function f (z) is D-finite, if **the** vector space over C(z) spanned by its derivatives is finite dimensional. In **the** univariate case this means that f is a solution **of** a homogenous linear ordinary differential equation with polynomial coefficients. A univariate D-finite function can at most have finitely many singularities, namely **the** zeroes **of** **the** coefficient **of** **the** highest order derivative. Guttmann [DGJ07, GGJD09, Gut06] proposed to study **the** polygon version **of** **the** problem, meaning walks, whose last vertex is adjacent to **the** starting vertex. As above, **the** property **of** being prudent demands a starting vertex and a terminal vertex. So prudent polygons are rooted polygons with a directed root edge. Note further that a prudent polygon (PP) which ends, say, to **the** right **of** **the** origin (i.e. in **the** vertex (1, 0)) may never step right **of** **the** line x = 1, and furthermore if **the** walk hits that line it has to head directly to **the** vertex (1, 0). So prudent polygons are directed in **the** sense that they contain a corner **of** their box. Moreover, a k-sided PP can be interpreted as a (k − 1)-sided PW confined in a half-plane. In this chapter we deal with **the** polygon versions **of** **the** two-sided and three-sided walks, referred to as two-sided and three-sided PPs. Enumeration **of** one-sided PPs is trivial, since these are simply rows **of** unit cells. We give explicit expressions for **the** half-perimeter generating functions **of** two-sided and three- sided PPs and show that **the** latter is not D-finite, which was also expected on numerical grounds. To our knowledge three-sided PPs are **the** first exactly solved polygon model with a non-D-finite half-perimeter generating function. Concerning **the** enumeration **of** **the** full class **of** PPs, we are able to give a system **of** functional equations satisfied by **the** generating function, however we have not been able to solve it so far. **The** situation is similar for **the** walk case.

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One particular interest in remote sensing is **the** **3D** recon- struction **of** urban areas for diverse applications such as **3D** city modelling, urban and crisis management etc. A typical method for reconstructing urban areas on a large scale is to employ stereo optical imagery provided by high-resolution space-borne sensors in an ideal acquisition situation. However, because **of** limitations in acquiring those ideal images such as cloud effects as well as limited absolute localization accuracy, optical stereo might not always be **the** optimal choice. In contrast, **the** amplitude images provided by SAR sensors do not suffer from **the** aforementioned issues, and can thus pro- vide input to time-critical **3D** reconstruction tasks. Regarding **the** growing archive **of** very high-resolution SAR and optical imagery, developing a framework that takes advantage **of** both SAR and optical imagery can provide a great opportunity to produce **3D** spatial information over urban areas as an application **of** data fusion in remote sensing [1].

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a young dog a mailman bitten has here already often ‘It has happened often here already that a young dog has bitten a mailman’
Wurmbrand (2004) notes that these examples present a potential problem for **the** SSG. We believe, however, that **the** problem is only apparent. There are two potential explanations for why German permits vP internal subjects and objects, both **of** which are compatible with A&A’s (2001) analysis: (a) One possibility is that German permits feature-chains between null clitics and in situ DP arguments qualifying essentially as a clitic doubling language (following Haider 1985; Fanselow 2001). Hence, there is no violation **of** **the** SSG understood as in (1’). (b) Alternatively, German lacks head-movement being a head-final language (Haider 1993, to appear). Hence, German lacks **the** formation **of** complex heads like (15) that would lead to a violation **of** (16).

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In general, a non-equivalent variant **of** POD, known as factor analysis, has been renowned and has been used for various applications [1, 2, 3, 18], etc. Unlike POD, factor analysis assumes that **the** data have a strict factor structure and it looks for **the** factors that amount for common variance in **the** data. On contrary, PCA **the** finite counterpart **of** POD, allows **the** accountability **of** maximal amount **of** variance for observed variables. **The** PCA analysis consists **of** identifying **the** set **of** variables, also known as principle components, from **the** system that retain as much variation from **the** original set **of** variables as possible. Similarly, Principal Expectile Analysis (PEC), which generalizes PCA for expectiles was recently developed as a dimension reduction tool for extreme value theory [24]. These POD equivalent tools have also been adopted in analysis on several instances such as [1, 9, 17, 24]. Yet, most **of** **the** literature exploits only **the** real life data for dimension reduction. Even though some analysis highly relies on real life data, there is an urgent need **of** introduction **of** tools that utilize simulated data generated from **the** non-standard **models** with nonlinear differential equations that are on constant rise and hold potential for enrichment **of** analysis.

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A simplified structure **of** **the** general design **of** **the** digital beam-forming SAR system based on **the** reflector antenna is depicted in Fig. 1. It consists **of** a parabolic dish, an ar- ray **of** primary antennas located in **the** focal plane, a feed system circuitry and a digital control system. Each feed element is connected to a Transmit/Receive (TR) module. **The** receive part is represented by a switch, a low noise amplifier, a band-pass filter, and an analog-to-digital con- verter. In **the** transmit part a conventional analog configu- ration based on phase shifters is used.

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Furthermore, I would like to thank all my colleagues at **the** mathematics department **of** **the** Trier University a pleasant time not only during working hours but also in many free time activities. Furthermore, I want to thank Dr. Christina Schenk, Olli Hauke, Gennadij Heidel and Daniel Hoffmann for proofreading this thesis and many joyful ”Spieleabende”. A special thank goes to Dr. Christina Schenk, who had to endure my presence countless hours in her office. Finally, I would like to express my deepest thank to my family, my parents Nguyen Van Thoai and Nguyen Hong Phan, as well as my sister Hanh Quyen Nguyen-Dudek for supporting me in so many ways and encouraging me again and again in all situations **of** life. Moreover, I thank my brother in law Sebastian Dudek and my two nieces Emma and Alina and my closest friends Bernhard Schmitt and Achim Schmillen for their moral support.

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During **the** last decades, several approaches for **the** reconstruction **of** **3D** **building** **models** have been developed. Starting in **the** 1980s with manual and semi-automatic reconstruction methods **of** **3D** **building** **models** from aerial images, **the** degree **of** automation has increased in recent years so that they became applicable to various areas. Some typical applications and examples are shown in section 1.1. Especially since **the** 1990s, when airborne light detection and ranging (LiDAR) technology became widely available, approaches for (semi-)automatic **building** reconstruction **of** large urban areas turned out to be **of** particular interest. Only in recent years, some large cities have built detailed **3D** city **models**. Although much effort has been put into **the** development **of** a fully automatic reconstruction strategy in order to overcome **the** high costs **of** semi-automatic reconstructions, no solution proposed so far meets all requirements (e.g., in terms **of** completeness, correctness, and accuracy). **The** reasons for this are manifold as discussed in section 1.2. Some **of** them are manageable, for example, either by using modern sensors which provide denser and more accurate point clouds than before or by incorporating additional data sources such as high-resolution images. However, there is quite a big demand for **3D** **building** **models** in areas where such modern sensors or additional data sources are not available. Therefore, in this thesis a new fully automatic reconstruction approach **of** semantic **3D** **building** **models** for low- and high-density airborne laser scanning (ALS) data **of** large urban areas is presented and discussed. Additionally, it is shown how automatically derived **building** knowledge can be used to enhance existing **building** reconstruction approaches. **The** specific research objectives are outlined in section 1.3. It includes an overview **of** **the** proposed reconstruction workflows and **the** contribution **of** this thesis. In order to have lean workflows with good performance, some general assumptions on **the** buildings to be reconstructed are imposed and explained in section 1.4. **The** introduction ends with an outline **of** this thesis in section 1.5.

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Section 7.2 described **the** first demonstration example **of** **the** **optimization** for some lighting quality **aspects** **of** **the** hybrid LED-lamp adapted to **the** color objects. In that, **the** chosen lighting objects were **the** specific part **of** a museum with oil color paintings, **the** adapted color objects were **the** oil color objects, and **the** optimized target was **the** average new specific oil color rendering index **of** seventy nine oil color objects in **the** museum lighting application with cases **of** 3000 K, 4000 K, 5000 K and 6500 K at **the** hot binning (80 °C). In fact, normally only in a museum **the** ambient temperature can be kept constant because **of** **the** severe requirements **of** temperature and humidity for paintings. Unfortunately, **the** operating temperature **of** **the** hybrid LED-lamp in other applications is not always at 80 °C constant, but changes depending on different factors such as **the** weather, seasons, daytime, nighttime or other operating conditions. When **the** operating temperature changes, **the** previous color mixing rate is not correct furthermore causing **the** total change **of** all lighting quality parameters **of** **the** hybrid LED-lamp such as chromaticity, **the** correlated color temperature, whiteness, color rendering indexes, luminous flux and luminous efficacy. Therefore, in this section **the** control system structure, which is similar to that described in Figure 7.7, will be used to optimize **the** new specific color rendering indexes **of** **the** warm white hybrid LED-lamp (3000 K) adapted to all red objects in general shop lighting applications. **The** hybrid LED-lamp will be established from **the** varied semiconductor LEDs and **the** different warm white PC-LEDs **of** three manufacturers (A, B and C) as well as their combinations at **the** hot binning (80 °C). Then, **the** optimized spectra will be investigated at **the** different operating

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So under conditions **of** rapid change **the** model shows that total return is increased by allowing an additional decision point, that is by rapid adaptation **of** strategy.
CONCLUSIONS
**The** introduction **of** formal **models** into management theory has several advantages. First it forces precision in **the** use **of** management concepts. Verbal concepts are often shrouded in ambiguity. In order to be incorporated in a formal model they must be stated in such a way that their relations to other elements **of** **the** model is clear. Second, **the** web **of** interconnected assumptions which typically comprise

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diabetes. In diabetic foot ulcers, increased Langerhans cells ( Stojadinovic et al., 2013; Strom et al., 2014 ), dermal macrophages ( Loots et al., 1998 ), and neutrophils ( Vatankhah et al., 2017 ) positively correlate with disease severity in humans. Moreover, **the** importance for adipose tissue to promote closure **of** non- diabetic skin wounds was recently demonstrated in drosophila ( Franz et al., 2018 ). Yet, currently **the** investigation **of** wound healing in **3D** skin **models** is limited to wound closure by fibroblasts and keratinocytes. Thus, **the** incorporation **of** resident and circulating immune cell types as well as physiologically relevant delivery **of** nutrients are necessary to recapitulate diseases associated with chronic skin wounds. Furthermore, **the** growth **of** **the** resident dermal bacteria, Staphylococcus aureus (S. aureus) ( Popov et al., 2014 ), as well as pathogenic bacteria, Acinetobacter baumannii ( de Breij et al., 2012 ), have been established on uninjured **3D** skin equivalents, and drug resistant strains **of** S. aureus have also been used in a wound infection model for therapeutic development ( Ventress et al., 2016 ). **The** physiological relevance **of** **3D** skin wound **models** (diabetic ulcers or chronic wounds) can progress with **the** addition **of** today’s bioengineering techniques. Although, skin bioprinting is currently used to produce skin equivalents for in vivo wound treatment (summarized in a recent review; He et al., 2018 ), **the** technology has thus far not been applied to generate in vitro skin wound **models**. Still, it is clear that **the** bioprinting technology will have a substantial impact on development **of** in vitro skin wound **models**.

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which is significantly more difficult to estimate than **the** previous **models**. Again, it is as- sumed that **the** errors are temporally independent, and that x i,0 and y i,0 are observable. OLS is
ineffective due to simultaneity bias. Maximum likelihood becomes unattainable due to **the** fact that **the** covariance matrix **of** **the** error term relies on **the** expected values **of** pre-sample observations, **of** which nothing is known. In fact, no satisfactory estimation has been found (Beck et al., 2006). To solve this problem, two approximations, **the** first based on **the** Bhar- gava and Sargan method (BS) and **the** second on **the** Nerlove and Balestra method (NB), have been proposed (Elhorst, 2003). **The** brief derivation that follows [see (Elhorst, 2003) for additional details] is intended to clarify **the** cost functions that are optimized in this paper.

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