Low-level Part Research Articles

Hierarchical attitude stabilization controller design and analysis for underactuated spacecraft on SO(3)

In this paper, the complete attitude stabilization of underactuated spacecraft with two parallel single gimbal control moment gyroscopes (SGCMGs) is explored on 3-dimensional special orthogonal group (SO(3)) and its corresponding Lie algebra (so(3)). Instead of assumption on zero total angular momentum in existing article, a less conservative criterion is adopted to ensure system controllability, which does not simplify system dynamics. On the kinematic level, an ideal controller is proposed to globally stabilize attitude without singular initial condition and stagnation behavior. Besides, the ideal kinematic controller is developed on so(3), which avoids singularity or unwinding phenomenon caused by other attitude parameters. On the dynamic level, a hierarchical controller consisting of two parts is proposed. The high-level sliding mode part enforces finite time stabilization of angular velocity about underactuated axis while the low-level part tracks ideal angular velocity commands about actuated axes. A rigorous proof of the whole dynamic-kinematic system that has not been explored before is given. Analysis of hierarchical control from the perspective of linear algebra provides a novel insight into controller design for underactuated systems. Furthermore, the paradigm of controller design which is applicable to other underactuated systems is derived. Simulation results validate the effectiveness of the proposed control logic.

Capturing Electricity Market Dynamics in Strategic Market Participation Using Neural Network Constrained Optimization

In competitive electricity markets, the optimal bid or offer problem of a strategic agent is commonly formulated as a bi-level program and solved as a mathematical program with equilibrium constraints (MPEC). If the lower-level part of the problem can be well approximated as a convex problem, this approach leads to a global optimum. However, electricity markets are governed by non-convex (partially known) constraints and reward functions of the participating agents. In this paper, an alternative data-driven paradigm, labeled as a mathematical program with neural network constraint (MPNNC), is developed. The method uses a neural network to represent the mapping between the upper-level (agent) decisions and the lower-level (market) outcomes, i.e., it replaces the lower-level problem with a surrogate model. In the presented case studies, the proposed model is used to find the optimal load shedding strategy of a strategic load-serving entity. First, the MPNNC performance is compared to the MPEC approach, both in convex and non-convex environments, showing that the proposed MPNNC achieves similar performance to an ideal MPEC that has perfect knowledge of the simulated market environment. Then, aggregated supply curves from the Belgian spot exchange are used to assess the potential gains of using the developed model in real-life applications.

Analysing inequity in land use and transportation models by genetic algorithm for realistically quantified penetration rate of Advanced Driving System Equipped Vehicles

The continued evolutions in automated driving technologies and their rapid testing on common roads make it necessary to evaluate their impacts on land use and transportation models. It is crucial to quantify the number of advanced driving system-equipped vehicles that are going to be part of transportation networks. On the other hand, the intuitive property of these vehicles to create an induced demand can bring both positive and negative effects on the travel equilibrium costs that create inequity. To cater for the gap of realistic quantification of penetration rate and inequity evaluation on the inclusion of such vehicles; this research crafts a detailed and effective methodology. This research formulates a convex minimization problem as a lower-level part of the bi-level optimization model intending to minimize the travel equilibrium cost for all OD pairs. Also, acts as an assignment of demand to the network following the stochastic user equilibrium approach by using the Frank–Wolfe algorithm. Whereas, the upper level of the model maximizes the production of newly generated demand incorporating inequity constraints. A genetic algorithm is used to solve the multi-objective fitness function yielded from the bi-level optimization model by application of the model on a real transportation network of the city of Genoa, Italy.

Model Following Robust Control of a Wavestar-prototype Wave Energy Converter: Part 1 Control

Focused on the numerical model of a Wavestar-like wave energy converter (WEC) developed in WEC-Sim, this paper describes a model-following concept to design a robust controller based on a hierarchical structure of tracking control. This low-level controller has the purpose of forcing the WEC system to follow the reference position and velocity trajectories for maximum power absorption from irregular waves. The simultaneous tracking of position and velocity can deal with physical limits of the device by setting proper reference signals. The high-level part of the control aims to calculate the optimal reference signals for the controller. It is assumed that the reference information is derived in Part 2. The low-level part of the hierarchical structure is to design a robust controller to enhance the WEC system robustness against system uncertainties and modelling errors. Two robust control approaches are proposed: (i) a mixed robust controller based on a combination of linear quadratic regulator (LQR) and H∞ performance, and (ii) a sliding mode controller which has simple structure and strong robustness.

Mechanistic decomposition and reduction in complex, context-sensitive systems.

Standard arguments in philosophy of science infer from the complexity of biological and neural systems to the presence of emergence and failure of mechanistic/reductionist explanation for those systems. I argue against this kind of argument, specifically focusing on the notion of context-sensitivity. Context-sensitivity is standardly taken to be incompatible with reductionistic explanation, because it shows that larger-scale factors influence the functioning of lower-level parts. I argue that this argument can be overcome if there are mechanisms underlying those context-specific reorganizations. I argue that such mechanisms are frequently discovered in neuroscience.

Risk of urban flash floods in the Guadalajara Metropolitan Area, Mexico

The Guadalajara Metropolitan Area (GMA), in western Mexico, has experienced a rapid demographic and urban growth in recent decades. As a result of land use changes, an Urban Heat Island (UHI) has developed and intensified, which reflects not only in air surface temperature increases, but also in higher atmospheric moisture content and more intense precipitation events. Storms have become more frequent since the beginning of 1980s, which makes them one of the major meteorological hazards for the city. Such an increase in strong storms appears to be related to the urbanization process which in turn, has increased the vulnerability of the GMA to intense rains. The urban expansion towards the higher parts of the region has led to more water runoff and flash flood events in the low-level parts of the metropolitan area. An analysis by means of observed meteorological data and a rainfall – runoff methodology shows that the combined effect of increased precipitation and vulnerability results in a higher risk of urban flash floods. The loss of natural vegetation, the main roads that act as water channels that converge into the central part of the city, an insufficient drainage system and the lack of prevention actions have resulted in more frequent urban flash floods. All of these vulnerability factors are associated with an inadequate urban expansion process. The model of flood risk may be used to define adaptation measures to face a changing climate.

Naturalism, Disease, and Levels of Functional Description.

The paper engages Christopher Boorse's Bio-Statistical Theory (BST). In its current form, BST runs into a significant challenge. For BST to account for its central tenet-that lower-level part-dysfunction is sufficient for higher-level pathology-it must provide criteria for how to decide which lower-level parts are the ones to be analyzed for health or pathology. As BST is a naturalistic theory, such choices must be based solely on naturalistic considerations. An argument is provided to show that, if BST is to be preserved, such parthood choices are based on non-naturalistic considerations. We demonstrate that even when parthood choices are based on the best way to preserve BST, there are counterintuitive results which bring the central tenet of BST into question.

Microleveling aerogeophysical data using deep convolutional network and MoG-RPCA

Residual magnetic error remains after standard levelling process. The weak non-geological effect, manifesting itself as streaky noise along flight lines, creates a challenge for airborne geophysical data processing and interpretation. Microleveling is the process to eliminate this residual noise and is now a standard areogeophysical data processing step. In this paper, we propose a two-step procedure for single aerogeophysical data microleveling: a deep convolutional network is first adopted as approximator to map the original data into a low-level part with nature geological structures and a corrugated residual which still contains high-level detail geological structures; second, the mixture of Gaussian robust principal component analysis (MoG-RPCA) is then used to separate the weak energy fine structures from the residual. The final microleveling result is the addition of low-level structures from deep convolutional network and fine structures from MoG-RPCA. The deep convolutional network does not need dataset for training and the handcrafted network serves as prior (deep image prior) to capture the low-level nature geological structures in the areogeophysical data. Experiments on synthetic data and field data demonstrate that the combination of deep convolutional network and MoG-RPCA is an effective framework for single areogeophysical data microleveling.

Küresel Salgın (COVID-19) Sürecinde Evde Yaşam Tatmini

Bu calisma, kuresel salgin surecinde bireylerin evde gecirdikleri sure zarfinda yasam tatmin duzeylerini tespit etmeyi amaclamaktadir. Arastirmanin evreni 18 yas uzeri, gonullu ve cevap verebilecek akli yetiye sahip kisilerden olusmaktadir. Arastirmanin orneklemini calismaya katilmayi kabul eden 1254 kisi olusturmaktadir. Arastirmanin verileri online formlar araciliǧiyla cesitli sosyal medya araclari (instagram, whatsapp, google formlar vb.) ile elde edilmistir. Elde edilen veriler betimleyici analizlerin yani sira, Mann Whitney U ve Kruskal Wallis testleri yardimiyla analiz edilmistir. Arastirmanin sonuclarina gore, belirtilen onlem unsurlarinin (maske, sosyal mesafe, hijyen vb.) hayata entegre (uyum) edilmesinin yasam tatmin duzeyi uzerinde onem arz ettiǧi tespit edilmistir. Ote yandan kisilerin yasam tatmin duzeyleri vasat ile yuksek duzey arasinda farklilik gostermektedir. Bu baǧlamda yasam tatmininde yuksek duzeyde yer alan unsurlarin maske, sosyal mesafe, hijyen vb. onlemler olduǧu belirlenirken, olcekte yer alan diǧer ifadelerin vasat duzeyde yer aldiǧi gozlemlenmektedir. Ayrica olcekte yer alan ifadeler icerisinde dusuk duzeyde yer alan tek ifadenin gunluk spor yapma olduǧu elde edilen diǧer sonuclar arasindadir.Alternate abstract:This study aimed to determine the life satisfaction levels of individuals during the time they spend at home during the global epidemic process. In the population of the research, every individual over the age of 18 who was voluntary and had the mental ability to respond was included. The data of the research were obtained by various social media tools (Instagram, WhatsApp, Google Forms, etc.) through online forms. In this context, the sample of the study consisted of 1,254 people. The data obtained were analyzed via Mann Whitney U and Kruskal Wallis tests as well as descriptive analysis. According to the results of the study, it was determined that the integration (adaptation) of the specified precautionary elements (mask, social distance, hygiene, etc.) to life was important in terms of the level of satisfaction with life. On the other hand, life satisfaction levels of people differed between average and high levels. In this context, it was observed that the factors that had a high-level part on life satisfaction were mask, social distance, hygiene, etc., while other expressions in the scale had a moderate level part. In addition, it is among the other results that the only expression that had a low-level part in the scale was daily exercise.

Automated Maneuvering in Confined Waters using Parameter Space Model and Model-based Control

The paper discusses methods to increase the level of automation in ship handling towards a possible autonomous operation. The focus is on maneuvering situations in confined waters within the velocity range between dynamic positioning and transiting. While performant automation solutions exist for specialized vessels, standard ships are operated manually in maneuvering situations. In this context, one challenge is to adapt a model for controller design of maneuvering vessels. It is a cumbersome task to parameterize the common hydrodynamical oriented models, especially for maneuvering standard ships. Therefore, a more experimental approach has been chosen to decrease the complexity of the model structure. In that way, the applied motion model is highly abstracted and has a minimal number of parameters which are mapped in parameter spaces. For motion control, a cascaded structure integrating a velocity and a maneuver control system has been designed. The low-level part consists of a model-based feedforward control applying the parameter space model implicitly. Further a simple decentralized multi-variable feedback controller is used. Here, a robust approach has been applied for controller parameterization by assigning a specific parameter space to each defined operation range. The methods are verified and validated with two demonstrators. Firstly a passenger vessel is used in a ship handling simulator and secondly real world experiments are performed applying an unmanned surface vehicle. The objective of these trials is automated maneuvering in the port of Rostock.

MANET 3.0: Hierarchy and modularity in evolving metabolic networks.

Enzyme recruitment is a fundamental evolutionary driver of modern metabolism. We see evidence of recruitment at work in the metabolic Molecular Ancestry Networks (MANET) database, an online resource that integrates data from KEGG, SCOP and structural phylogenomic reconstruction. The database, which was introduced in 2006, traces the deep history of the structural domains of enzymes in metabolic pathways. Here we release version 3.0 of MANET, which updates data from KEGG and SCOP, links enzyme and PDB information with PDBsum, and traces evolutionary information of domains defined at fold family level of SCOP classification in metabolic subnetwork diagrams. Compared to SCOP folds used in the previous versions, fold families are cohesive units of functional similarity that are highly conserved at sequence level and offer a 10-fold increase of data entries. We surveyed enzymatic, functional and catalytic site distributions among superkingdoms showing that ancient enzymatic innovations followed a biphasic temporal pattern of diversification typical of module innovation. We grouped enzymatic activities of MANET into a hierarchical system of subnetworks and mesonetworks matching KEGG classification. The evolutionary growth of these modules of metabolic activity was studied using bipartite networks and their one-mode projections at enzyme, subnetwork and mesonetwork levels of organization. Evolving metabolic networks revealed patterns of enzyme sharing that transcended mesonetwork boundaries and supported the patchwork model of metabolic evolution. We also explored the scale-freeness, randomness and small-world properties of evolving networks as possible organizing principles of network growth and diversification. The network structure shows an increase in hierarchical modularity and scale-free behavior as metabolic networks unfold in evolutionary time. Remarkably, this evolutionary constraint on structure was stronger at lower levels of metabolic organization. Evolving metabolic structure reveals a ‘principle of granularity’, an evolutionary increase of the cohesiveness of lower-level parts of a hierarchical system. MANET is available at http://manet.illinois.edu.

Improving SBR Performance Alongside with Cost Reduction through Optimizing Biological Processes and Dissolved Oxygen Concentration Trajectory

Authors of this paper take under investigation the optimization of biological processes during the wastewater treatment in sequencing batch reactor (SBR) plant. A designed optimizing supervisory controller generates the dissolved oxygen (DO) trajectory for the lower level parts of the hierarchical control system. Proper adjustment of this element has an essential impact on the efficiency of the wastewater treatment process as well as on the costs generated by the plant, especially by the aeration system. The main goal of the presented solution is to reduce the plant energy consumption and to maintain the quality of effluent in compliance with the water-law permit. Since the optimization is nonlinear and includes variations of different types of variables, to solve the given problem the authors performed simulation tests and decided to implement a hybrid of two different optimization algorithms: artificial bee colony (ABC) and direct search algorithm (DSA). Simulation tests for the wastewater treatment plant case study are presented.

Fuzzy control of bipedal running with variable speed and apex height

In this paper we propose a fuzzy-based control scheme to generate stable planar biped running gaits with variable apex height and velocity. The considered biped robot model includes five links with locked torso angles, point feet, and four actuators at the hip and knees. The controller includes two separate levels: upper-level and lower-level. The lower-level part is composed of a state machine, where the trajectory of running sub-phases and their switching time are controlled. The upper-level part includes an event-based fuzzy logic controller that is called at the apex of each flight phase. We use an offline fuzzy training process for designing fuzzy rules before controlling the robot. Fuzzy training is an iterative computational process that is repeated until convergence. Outputs of the fuzzy controller are fed into the state machine to control the running gaits. Simulation results show that the proposed control strategy generates stable gaits with controllable apex height and velocity in each step. Finally, the effects of apex height and velocity in running efficiency are investigated and optimum height is calculated as a function of running velocity.

Fitting Software Execution-Time Exceedance into a Residual Random Fault in ISO-26262

Car manufacturers relentlessly replace or augment the functionality of mechanical subsystems with electronic components. Most such subsystems (e.g., steer-by-wire) are safety related, hence, subject to regulation. ISO-26262, the dominant standard for road vehicles, regards software faults as systematic , while differentiating hardware faults between systematic and random . The analysis of systematic faults entails rigorous processes and qualitative considerations. The increasing complexity of modern on-board computers, however, questions the very notion of treating the violation of execution-time envelopes for software programs as a systematic fault. Modern hardware in fact reduces the user's ability to delve deep enough into the fabric of hardware–software interaction to gage its extent of contribution to the worst-case execution time (WCET). Changing the nature of the WCET-analysis problem may help address that challenge effectively. To this end, we propose a solution that should allow ISO-26262 to quantify the likelihood of execution-time exceedance events, relating it to target failure metrics employed in support of certification arguments, similarly to random faults in hardware. To this end, we inject randomization in the timing behavior of the computer hardware to relieve the user from the need to control hard-to-reach low-level parts, and use measurement-based probabilistic timing analysis to quantify, constructively, the failure rates resulting from the likelihood of execution-time exceedance events.

Thiophene oxidation with H2O2 over defect and perfect titanium silicalite-1: a computational study

The oxidation mechanisms of thiophene (Tp) with H2O2 over defect and perfect titanium silicalite-1 (TS-1) were investigated by means of DFT/ONIOM2 calculations for different models 7T:45T, 9T:45T and 13T:45T. The B3LYP, ωB97X-D, and M06-2X functionals were used in the high-level part of ONIOM2, with PM6 in the lower-level part. The related potential energy surface profiles are constructed based on the ωB97X-D/ONIOM2 method for 13T:45T cluster. Four possible paths are proposed for the oxidation of Tp into sulfoxide and sulfoxide into sulfone using H2O2 as an oxidant over defect and perfect TS-1. The dimerization of sulfoxide and sulfone through Diels–Alder cycloaddition is also studied. It is found that perfect TS-1 is highly unfavorable compared to defect TS-1 (TS-d). The calculated activation energy of Tp oxidation with TS-d is found to be remarkably lower than that without any zeolite catalyst. The calculated results may contribute to the understanding of the reaction mechanism of the catalytic oxidative desulfurization of sulfides in liquid fuels.

Multi-Scale Integrated Analysis of Charcoal Production in Complex Social-Ecological Systems

We propose and illustrate multi-scale integrated analysis of societal and ecosystem metabolism (MuSIASEM) as a tool to bring nexus thinking into practice. MuSIASEM studies the relations over the structural and functional components of social-ecological systems that determine the entanglement of water, energy and food flows in a complex metabolic pattern. MuSIASEM simultaneously considers various dimensions and multiple scales of analysis and therefore avoids the predicament of quantitative analysis based on reductionism (one dimension and one scale at the time). The different functional elements of society (the parts) are characterized using the concept of ‘processor’, that is, a profile of expected inputs and outputs associated with the expression of a specific function. The processors of the functional elements of the social-ecological system can be scaled-up to describe the metabolic pattern of the system as a whole, and scaled-down by considering the characteristics of its lower-level parts – i.e. the different processors associated with the structural elements required to express the specific function. An analysis of functional elements provides insight in the socio-economic factors that pose internal constraints on the development of the system. An analysis of structural elements makes it possible to study the compatibility of the system with external constraints (availability of natural resources and ecological services) in spatial terms. The usefulness of the approach is illustrated in relation to the use of charcoal in a rural village of Laos.

Dynamic flux balance analysis with nonlinear objective function.

Dynamic flux balance analysis (DFBA) extends flux balance analysis and enables the combined simulation of both intracellular and extracellular environments of microbial cultivation processes. A DFBA model contains two coupled parts, a dynamic part at the upper level (extracellular environment) and an optimization part at the lower level (intracellular environment). Both parts are coupled through substrate uptake and product secretion rates. This work proposes a Karush-Kuhn-Tucker condition based solution approach for DFBA models, which have a nonlinear objective function in the lower-level part. To solve this class of DFBA models an extreme-ray-based reformulation is proposed to ensure certain regularity of the lower-level optimization problem. The method is introduced by utilizing two simple example networks and then applied to a realistic model of central carbon metabolism of wild-type Corynebacterium glutamicum.

Representation for Action Recognition Using Trajectory-Based Low-Level Local Feature and Mid-Level Motion Feature

The dense trajectories and low-level local features are widely used in action recognition recently. However, most of these methods ignore the motion part of action which is the key factor to distinguish the different human action. This paper proposes a new two-layer model of representation for action recognition by describing the video with low-level features and mid-level motion part model. Firstly, we encode the compensated flow (w-flow) trajectory-based local features with Fisher Vector (FV) to retain the low-level characteristic of motion. Then, the motion parts are extracted by clustering the similar trajectories with spatiotemporal distance between trajectories. Finally the representation for action video is the concatenation of low-level descriptors encoding vector and motion part encoding vector. It is used as input to the LibSVM for action recognition. The experiment results demonstrate the improvements on J-HMDB and YouTube datasets, which obtain 67.4% and 87.6%, respectively.

Possible causes of the interdecadal transition of the Somali jet around the late 1990s

This observational study demonstrates that the Somali jet (SMJ) experienced a notable interdecadal transition in not only its lower-level parts (< 850 hPa) but also its higher-level parts (850–600 hPa) in the late 1990s. The results also show that the jet at higher level is more significantly related to East Asian monsoon rainfall than that at lower level. Thus, a new whole-layer SMJ (WSMJ) index which includes variations of the higher-level jet is defined based on the average meridional wind speed at five levels (1000–600 hPa). The interdecadal transition of the SMJ can be mainly attributed to the meridional thermal contrast anomalies near the equator which are associated with the three-pole pattern of the southern Indian Ocean.

A DFT study on Cu(I) coordination in Cu‐ZSM‐5: Effects of the functional choice and tuning of the ONIOM approach

The coordination of Cu(+) at the T1 and T7 positions of the M7 ring of Cu-ZSM-5, and the interaction of NO with coordinated Cu(+) were investigated by means of DFT/ONIOM calculations. The B3LYP, BLYP, PBE1PBE, PBE, M06, and M062X functionals with the def2-TZVP (def2-QZVP for Cu) basis set were used in the high-level part of ONIOM calculations, with the HF/3-21G, B3LYP/LANL2DZ, M06/LANL2DZ, and M062X/LANL2DZ methods in the low-level part. The ability of suitable combinations of the above methods to reproduce (i) the crystallographic structure of purely siliceous ZSM-5, (ii) the tendency of Cu(+) to be twofold or fourfold coordinated by framework oxygen atoms of Cu-ZSM-5, and (iii) the interaction energy and the N-O stretching frequency of adsorbed nitrogen oxide are discussed, showing that different results are obtained depending on the adopted computational approach. With reference to above properties, some considerations about the employment of the ONIOM approximations are also included.