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Aiming at the problem that existing water surface garbage detection algorithms cannot meet the tradeoff between real-time and accuracy in natural scenes, we propose a GFL (Generalized Focal Loss) based GFL_ HAM algorithm to improve the accuracy of water surface garbage detection, and better apply to water surface garbage detection application scenarios. We have added the HAM (Hybrid Attention Model) module to the feature extraction module (resnet50) of the GFL network to improve the feature extraction capability of the backbone network and the network representation capability. And use the self-constructed garbage detection datasets under natural scenes for training and testing. After analyzing the training process and test results, our network has higher reliability compared to the current mainstream single stage network. Our network can achieve 60.12% mAP on the water surface garbage detection datasets, which is 1.09%, 1.36%, and 1.61% higher than YOLOv3, SSD, and GFL, respectively.

The missile-target assignment (MTA) is a typical weapon-target assignment problem in Command and Control of modern warfare. Despite the significance of the problem, traditional algorithms still lack efficiency, solution quality, and practicability in the adversarial environment. In this article, we propose a data-driven policy optimization with deep reinforcement learning (PODRL) for the adversarial MTA. We design a comprehensive reward function to motivate the optimization of assignment policy. As such, the learned policy can implicitly model the penetration of missiles under an adversarial environment in a data-driven way. We also present a fair sample strategy to improve the sample efficiency and accelerate the policy optimization. Experimental results show that PODRL can adaptively generate satisfactory solutions in both small-scale and large-scale instances. Furthermore, we evaluate the effectiveness of PODRL in a multiobjective scenario. The result demonstrates that a well-optimized policy can achieve high-quality allocation and demand forecast of the missile resources simultaneously.

Learning linear temporal logic on finite traces (LTLf) formulae aims to learn a target formula that characterizes the high-level behavior of a system from observation traces in planning. Existing approaches to learning LTLf formulae, however, can hardly learn accurate LTLf formulae from noisy data. It is challenging to design an efficient search mechanism in the large search space in form of arbitrary LTLf formulae while alleviating the wrong search bias resulting from noisy data. In this paper, we tackle this problem by bridging LTLf inference to GNN inference. Our key theoretical contribution is showing that GNN inference can simulate LTLf inference to distinguish traces. Based on our theoretical result, we design a GNN-based approach, GLTLf, which combines GNN inference and parameter interpretation to seek the target formula in the large search space. Thanks to the non-deterministic learning process of GNNs, GLTLf is able to cope with noise. We evaluate GLTLf on various datasets with noise. Our experimental results confirm the effectiveness of GNN inference in learning LTLf formulae and show that GLTLf is superior to the state-of-the-art approaches.

Configuration checking (CC) has been confirmed to alleviate the cycling problem in local search for combinatorial optimization problems (COPs). When using CC heuristics in local search for graph problems, a critical concept is the configuration of the vertices. All existing CC variants employ either 1- or 2-level neighborhoods of a vertex as its configuration. Inspired by the idea that neighborhoods with different levels should have different contributions to solving COPs, we propose the probabilistic configuration (PC), which introduces probabilities for neighborhoods at different levels to consider the impact of neighborhoods of different levels on the CC strategy. Based on the concept of PC, we first propose probabilistic configuration checking (PCC), which can be developed in an automated and lightweight favor. We then apply PCC to two classic COPs which have been shown to achieve good results by using CC, and our preliminary results confirm that PCC improves the existing algorithms because PCC alleviates the cycling problem.

Convolutional neural networks (CNNs) are employed to achieve the color balance and dehazing of degraded underwater images. In the module of color balance, an underwater generative adversarial network (UGAN) is constructed. The mapping relationship between underwater images with color deviation and clean underwater images is learned. In the module of clarity improvement, an all-in-one dehazing model is proposed in which a comprehensive index is introduced and estimated by deep CNN. The third module to enhance underwater images adopts an adaptive contrast improvement method by fusing global and local histogram information. Combined with several underwater image datasets, the proposed enhancement method based on the three modules is evaluated, both by subjective visual effects and quantitative evaluation metrics. To demonstrate the advantages of the proposed method, several commonly used underwater image enhancement algorithms are compared. The comparison results indicate that the proposed method gains better enhancement effects for underwater images in different scenes than the other enhancement algorithms, since it can significantly diminish the color deviation, blur, and low contrast in degraded underwater images.

Linear temporal logic over finite traces (LTLf) satisfiability checking is a fundamental and hard (PSPACE-complete) problem in the artificial intelligence community. We explore teaching end-to-end neural networks to check satisfiability in polynomial time. It is a challenge to characterize the syntactic and semantic features of LTLf via neural networks. To tackle this challenge, we propose LTLfNet, a recursive neural network that captures syntactic features of LTLf by recursively combining the embeddings of sub-formulae. LTLfNet models permutation invariance and sequentiality in the semantics of LTLf through different aggregation mechanisms of sub-formulae. Experimental results demonstrate that LTLfNet achieves good performance in synthetic datasets and generalizes across large-scale datasets. They also show that LTLfNet is competitive with state-of-the-art symbolic approaches such as nuXmv and CDLSC.

Fault tree analysis (FTA) is a prominent reliability analysis method, which is widely used in safety-critical industries. Computing the minimal cut sets (MCSs) of a fault tree, i.e., finding all the smallest combinations of the basic events that cause system failures, is a fundamental step in FTA. Since coherent fault trees are the most common in industrial systems in practice, they are the focus of this article. Computing MCSs is a computationally hard problem. Classical methods have been proposed based on manipulation of Boolean expressions and binary decision diagrams. However, given the inherent intractability of computing MCSs in practice, there are still limitations on time and memory in these methods. Therefore, developing new methods over different paradigms remains to be an interesting research direction. In this article, motivated by recent progress on modern Boolean satisfiability problem (SAT) solvers, we present a new method for computing MCSs based on SAT, namely SATMCS . Specifically, given a fault tree, we iteratively search for a cut set based on the conflict-driven clause learning framework. By exploiting local propagation graph, which characterizes the partial failure propagation based on the cut set, we provide efficient algorithms for extracting an MCS. The new MCS is learned as a block clause for SAT solving, and the conflict clauses in iterations are incrementally recorded, which helps to prune search space and ensures completeness of the results. Moreover, we adopt a jump-chronological backtracking strategy to prepare the next iteration, which allows for reusing the same search steps in SAT solving. We compare SATMCS with state-of-the-art commercial tools on practical fault trees. Although SATMCS is only a prototype, it shows comparable performance in time consumption with one tool ( XFTA ), and in various cases, it outperforms the others ( FaultTree + and Commander ). Besides, SATMCS exhibits much better performance on memory usage than these tools. Specifically, SATMCS consumes about one order of magnitude less memory usage in most instances.

A fusion algorithm is proposed for the restoration and enhancement of underwater images. Color balance, contrast optimization and histogram stretching are carried out. To alleviate the effect of color shift in an underwater image, the scalar values of R, G, B channels are renewed so that the distributions of the three channels in histogram are similar. Instead of refining the transmittance in dark channel prior based restoration, an optimized contrast algorithm is employed by which the optimal transmittance is determined. To further improve the brightness and contrast of underwater images, a histogram stretching algorithm based on the red channel is given. To verify the effectiveness of the proposed fusion algorithm, experimental underwater images are treated. Results show that the quality of underwater images is improved significantly, both in term of subjective visual effect and objective evaluation. The proposed underwater image processing strategy is also compared with some popular techniques. Comparison results indicate the advantage of the proposed strategy over others.

In the presence of the main lobe jamming, the performance of the modern radar system would degrade significantly. In this study, a blind source separation (BSS) method based on an Adaptive Opposite Fireworks Algorithm (AOFWA) is proposed to address the problem of ineffective target echo detection when the radar is subjected to main lobe jamming. First, a signal model for BSS applied in the anti-main lobe jamming is provided, and the number of source signals is estimated. Then, an adaptive opposite learning operator is designed to improve the optimization capability and convergence speed of the basic Fireworks Algorithm (FWA). Finally, pulse compression is performed for separated signals to achieve peak detection. The simulation result shows that the proposed algorithm can effectively extract target echo under suppression jamming and deception jamming, and the separation precision is relatively high, and its convergence speed is superior.

Nowadays, deep neural architectures have acquired great achievements in many domains, such as image processing and natural language processing. In this paper, we hope to provide new perspectives for the future exploration of novel artificial neural architectures via reviewing the proposal and development of existing architectures. We first roughly divide the influence domain of intrinsic motivations on some common deep neural architectures into three categories: information processing, information transmission and learning strategy. Furthermore, to illustrate how deep neural architectures are motivated and developed, motivation and architecture details of three deep neural networks, namely convolutional neural network (CNN), recurrent neural network (RNN) and generative adversarial network (GAN), are introduced respectively. Moreover, the evolution of these neural architectures are also elaborated in this paper. At last, this review is concluded and several promising research topics about deep neural architectures in the future are discussed.

Aimed at the bottleneck of Firework Algorithm's performance and the problem that it is easy to fall into the local extremum, this paper introduces a chaotic map approach and analyzes its statistical characteristics. New hybrid chaotic systems and a chaotic perturbation operator are designed. Based on different chaotic map approaches, a new Chaotic Firework Algorithm (CFWA) is proposed and simulated by typical test functions. The results show that performance of the Chaotic Firework Algorithm is generally better than the original algorithms, and the chaotic hybrid approaches are superior to single chaotic approaches. In order to further verify the application of the proposed algorithm in practical engineering problems, CFWA is applied to Blind Source Separation (BSS) of radar signals. As a result, it does well in sorting observed signals, and has faster convergence and better sorting performance than the traditional algorithms. In this way, the CFWA extends its engineering application.

Due to refraction, absorption, and scattering of light by suspended particles in water, underwater images are characterized by low contrast, blurred details, and color distortion. In this paper, a fusion algorithm to restore and enhance underwater images is proposed. It consists of a color restoration module, an end-to-end defogging module and a brightness equalization module. In the color restoration module, a color balance algorithm based on CIE Lab color model is proposed to alleviate the effect of color deviation in underwater images. In the end-to-end defogging module, one end is the input image and the other end is the output image. A CNN network is proposed to connect these two ends and to improve the contrast of the underwater images. In the CNN network, a sub-network is used to reduce the depth of the network that needs to be designed to obtain the same features. Several depth separable convolutions are used to reduce the amount of calculation parameters required during network training. The basic attention module is introduced to highlight some important areas in the image. In order to improve the defogging network’s ability to extract overall information, a cross-layer connection and pooling pyramid module are added. In the brightness equalization module, a contrast limited adaptive histogram equalization method is used to coordinate the overall brightness. The proposed fusion algorithm for underwater image restoration and enhancement is verified by experiments and comparison with previous deep learning models and traditional methods. Comparison results show that the color correction and detail enhancement by the proposed method are superior.

A model of a submerged angular cavitation nozzle is established, which consists of a contraction part, parallel middle part, and expansion part. Based on the CFD technique, a numerical simulation of the flow field of the submerged cavitation nozzle is carried out, in which a multiphase mixture model, cavitation model, and renormalization group (RNG) k-ε turbulence model are applied. Considering the influence of mixture density on cavitation, the effects of the inlet contraction part, parallel middle part, and outlet expansion part on the velocity and vapor volume fraction are studied. The numerical simulation results show that the mixture density is essential in the cavitation jet. When the nozzle diameter d is fixed, the designed angular cavitation nozzle with contraction angle α = 13.5°, parallel middle part length Ld = 3d, expansion part length Le = 4d, and expansion angle β = 60° can effectively bring out cavitation. A cavitation cloud is produced near the rigid wall of the outlet expansion section and diffuses in a vortex ring shape. Optimizing the nozzle structure can improve the cavitation effect of the nozzle. The feasibility of this model is verified by relevant experimental data.

The YOLOv4 neural network is employed for underwater target recognition. To improve the accuracy and speed of recognition, the structure of YOLOv4 is modified by replacing the upsampling module with a deconvolution module and by incorporating depthwise separable convolution into the network. Moreover, the training set used in the YOLO network is preprocessed by using a modified mosaic augmentation, in which the gray world algorithm is used to derive two images when performing mosaic augmentation. The recognition results and the comparison with the other target detectors demonstrate the effectiveness of the proposed YOLOv4 structure and the method of data preprocessing. According to both subjective and objective evaluation, the proposed target recognition strategy can effectively improve the accuracy and speed of underwater target recognition and reduce the requirement of hardware performance as well.

Lithium-ion batteries (LIBs) are increasingly popular for electric vehicle and grid storage applications, but degradation mechanisms remain poorly understood and are difficult to characterize accurately. Here a new method for in situ monitoring of internal degradation in LIBs using an electrochemical model (EM) is introduced. The main contributions of this work can be summarized as follows: (1) An EM is developed based on a single particle model, the parameters of which are reorganized from the original physical property parameters for convenience of identification, with each new parameter assigned a specific physical meaning. (2) Identification methods for all parameters are determined through activation-and-response analysis, and a combined load profile for parameter identification is developed. (3) Cyclic aging experiments are carried out and the aforementioned model and parameter identification methods are applied at appropriate intervals. The predicted trends of 7 (internal resistance included) of the 10 model parameters show strong correlations with the experimentally observed degradation.

The control of the point stabilization of an underactuated surface ship is investigated. Wave forces and moment impacted on the vessel are considered and suppressed by L2-gain design. Combined with Lyapunov function and backstepping method, a time-invariant nonlinear feedback control approach is proposed. Simulation results prove the good performance of the controller proposed for the stabilization of the underactuated system.

The Dynamic Pickup and Delivery Problem (DPDP) is aimed at dynamically scheduling vehicles among multiple sites in order to minimize the cost when delivery orders are not known a priori. Although DPDP plays an important role in modern logistics and supply chain management, state-of-the-art DPDP algorithms are still limited on their solution quality and efficiency. In practice, they fail to provide a scalable solution as the numbers of vehicles and sites become large. In this paper, we propose a data-driven approach, Spatial-Temporal Aided Double Deep Graph Network (ST-DDGN), to solve industry-scale DPDP. In our method, the delivery demands are first forecast using spatial-temporal prediction method, which guides the neural network to perceive spatial-temporal distribution of delivery demand when dispatching vehicles. Besides, the relationships of individuals such as vehicles are modelled by establishing a graph-based value function. ST-DDGN incorporates attention-based graph embedding with Double DQN (DDQN). As such, it can make the inference across vehicles more efficiently compared with traditional methods. Our method is entirely data driven and thus adaptive, i.e., the relational representation of adjacent vehicles can be learned and corrected by ST-DDGN from data periodically. We have conducted extensive experiments over real-world data to evaluate our solution. The results show that ST-DDGN reduces 11.27% number of the used vehicles and decreases 13.12% total transportation cost on average over the strong baselines, including the heuristic algorithm deployed in our UAT (User Acceptance Test) environment and a variety of vanilla DRL methods. We are due to fully deploy our solution into our online logistics system and it is estimated that millions of USD logistics cost can be saved per year.

The boundary conditions (BCs) have shown great potential in requirements engineering because a BC captures the particular combination of circumstances, i.e., divergence, in which the goals of the requirement cannot be satisfied as a whole. Existing researches have attempted to automatically identify lots of BCs. Unfortunately, a large number of identified BCs make assessing and resolving divergences expensive. Existing methods adopt a coarse-grained metric, generality, to filter out less general BCs. However, the results still retain a large number of redundant BCs since a general BC potentially captures redundant circumstances that do not lead to a divergence. Furthermore, the likelihood of BC can be misled by redundant BCs resulting in costly repeatedly assessing and resolving divergences. In this paper, we present a fine-grained metric to filter out the redundant BCs. We first introduce the concept of contrasty of BC. Intuitively, if two BCs are contrastive, they capture different divergences. We argue that a set of contrastive BCs should be recommended to engineers, rather than a set of general BCs that potentially only indicates the same divergence. Then we design a post-processing framework (PPAc) to produce a set of contrastive BCs after identifying BCs. Experimental results show that the contrasty metric dramatically reduces the number of BCs recommended to engineers. Results also demonstrate that lots of BCs identified by the state-of-the-art method are redundant in most cases. Besides, to improve efficiency, we propose a joint framework (JAc) to interleave assessing based on the contrasty metric with identifying BCs. The primary intuition behind JAc is that it considers the search bias toward contrastive BCs during identifying BCs, thereby pruning the BCs capturing the same divergence. Experiments confirm the improvements of JAc in identifying contrastive BCs., to be published in ICSE21

In goal-oriented requirements engineering, goal conflict identification is of fundamental importance for requirements analysis. The task aims to find the feasible situations which make the goals diverge within the domain, called boundary conditions (BCs). However, the existing approaches for goal conflict identification fail to find sufficient BCs and general BCs which cover more combinations of circumstances. From the BCs found by these existing approaches, we have observed an interesting phenomenon that there are some pairs of BCs are similar in formula structure, which occurs frequently in the experimental cases. In other words, once a BC is found, a new BC may be discovered quickly by slightly changing the former. It inspires us to develop a local search algorithm named LOGION to find BCs, in which the structural similarity is captured by the neighborhood relation of formulae. Based on structural similarity, LOGION can find a lot of BCs in a short time. Moreover, due to the large number of BCs identified, it potentially selects more general BCs from them. By taking experiments on a set of cases, we show that LOG I ON effectively exploits the structural similarity of BCs. We also compare our algorithm against the two state-of-the-art approaches. The experimental results show that LOGION produces one order of magnitude more BCs than the state-of-the-art approaches and confirm that LOGION finds out more general BCs thanks to a large number of BCs.

Computing precise evidences, namely minimal sets of sentences that support or refute a given claim, rather than larger evidences is crucial in fact verification (FV), since larger evidences may contain conflicting pieces some of which support the claim while the other refute, thereby misleading FV. Despite being important, precise evidences are rarely studied by existing methods for FV. It is challenging to find precise evidences due to a large search space with lots of local optimums. Inspired by the strong exploration ability of the deep Q-learning network (DQN), we propose a DQN-based approach to retrieval of precise evidences. In addition, to tackle the label bias on Q-values computed by DQN, we design a post-processing strategy which seeks best thresholds for determining the true labels of computed evidences. Experimental results confirm the effectiveness of DQN in computing precise evidences and demonstrate improvements in achieving accurate claim verification.

The hydrodynamic forces of KVLCC2 ship sailing near inclined banks are calculated by using CFD based on RANS equation. Corresponding CFD uncertainty analysis is conducted according to the procedure recommended by ITTC. An unstructured grid, tetrahedral grid, is employed for discretization. To control the number of grids, global element scale factor is selected as the same as refinement ratio. In numerical simulation, straightforward and oblique navigation conditions are investigated. The variation of transverse force and yaw moment with the ship-shore distance, bank angle, water depth, and drift angle are analyzed. Both hull model and hull-propeller-rudder model are considered in numerical simulation. The simulation results show the hydrodynamic characteristics of ship sailing near inclined banks.

Aiming at the problem that the initial value of Taylor algorithm may cause large error in localization result when solving the unmanned aerial vehicle (UAV) swarms time-difference-of-arrival(TDOA) passive localization equation, a hybrid algorithm based on Chan-Taylor is proposed. The hybrid algorithm proposed in this paper is based on Chan algorithm, and then the location result is used as the initial value of Taylor algorithm iteration, and stop iteration until the error is lower than the threshold, and finally output the localization result. Simulation results show that the localization accuracy, convergence speed and adaptability of the Chan-Taylor hybrid algorithm are better than the original Chan algorithm.

System identification provides an effective way to predict the ship manoeuvrability. In this paper several measures are proposed to diminish the parameter drift in the parametric identification of ship manoeuvring models. The drift of linear hydrodynamic coefficients can be accounted for from the point of view of dynamic cancellation, while the drift of nonlinear hydrodynamic coefficients is explained from the point of view of regression analysis. To diminish the parameter drift, reconstruction of the samples and modification of the mathematical model of ship manoeuvring motion are carried out. Difference method and the method of additional excitation are proposed to reconstruct the samples. Using correlation analysis, the structure of a manoeuvring model is simplified. Combined with the measures proposed, support vector machines based identification is employed to determine the hydrodynamic coefficients in a modified Abkowitz model. Experimental data from the free-running model tests of a KVLCC2 ship are analyzed and the hydrodynamic coefficients are identified. Based on the regressive model, simulation of manoeuvres is conducted. Comparison between the simulation results and the experimental results demonstrates the validity of the proposed measures.

To reduce the ship roll motion, an adaptive robust fin controller based on a feedforward neural network is proposed. The dynamics of the fin actuator is considered in the plant of a roll-fin cascaded system with uncertainties which refer to as the modeling errors and the environmental disturbance induced by waves. An on-line feedforward neural network is constructed to account for the uncertainties. Lyapunov design is employed to obtain the fin stabilizer with guaranteed robustness. Simulation results demonstrate the validity of the controller designed and the superior performance over a conventional PD controller.

Unmanned aerial vehicle (UAV) swarms combat is considered to be a new type of combat that will change the rules of war in the future. This study aiming at single unmanned aerial vehicle (UAV) to the problem of weak target detection and positioning speed, multi-machine joint maximum likelihood estimation (MLE) detection algorithm is applied to distributed UAVs swarmsing collaborative detection, established the unmanned aerial vehicle (UAV) cooperative detection target swarms model of the algorithm, and by setting the relevant parameters are simulated, the results prove that by increasing the number and the formation of the dispersion degree can effectively improve the target detection probability and reduce the mean square error of positioning speed.

In order to compare the performance of different state estimation algorithms in electrochamical model-based SOC(state of charge) estimation for lithium-ion battery, this paper proposed a series of SOC estimation approaches which use different algorithms including extended Kalman filter(EKF), adaptive extended Kalman filter(AEKF), particle filter(PF) and dichotomy. Their accuracy, convergence and computation efficiency was examined at the end of the paper.

A heating method for lithium-ion battery is studied based on a simplified first principle electrochemical model. The criterion for avoiding lithium deposition is converted into current constraints under different temperature and state of charge. An experimental platform with closed-loop pulse current control function is built, by using of which, the effectiveness of the heating strategy is verified through heating a 18650 cylindrical LiFePO 4 battery 100 times. The results show that no capacity fading is observed.

In this paper, aging mechanism of lithium ion batteries and its impact on capacity loss is analyst in detail, based on the simplified electrochemical model. The internal aging mechanism of the battery is identified from the open circuit voltage curve. These aging behaviors which result in capacity loss are classified into four parts: capacity loss of positive and negative electrode, loss of lithium ion inventory, and total polarization potential increase. The positive and negative electrode active materials continuously decay during cycling; and the lithium ions circulating between the positive and negative electrodes are also consumed inevitably during the charging and discharging process; and the total polarization voltage of the battery increases significantly which makes the battery reach the charge and discharge cut-off points in advance; these all lead to the battery capacity drop. Find out these factors from identification can help us understand the battery aging state and ensure the safe use of the battery. The experiment shows the identification results can reflect the internal aging state of the battery very well.

Electrochemical impedance spectroscopy (EIS) is an effective method to characterize electrochemical electrodes, and is potentially a solution for estimating the state of health (SOH) of lithium-ion batteries (LIBs). A time domain EIS measurement technique followed by an equivalent circuit model interpretation is proposed in this paper. The accuracy of the measurement and the EIS-based SOH estimation are validated on Sanyo's UR14500P type LiCoO 2 cells, the results of which show that the proposed methods are effective in fast and economical assessment of LIBs.

Lithium-ion battery thermal management system is critical to improving charge and discharge safety and cycle life. At present, the thermal simulation is an important means to study the thermal problem of lithium battery, and the correct identification of the thermal parameters is the key to thermal simulation. This paper is based on COMSOL Multiphysics for the thermal simulation of a 48V/50Ah LiFePO 4 battery pack. The thermal model for the battery simulation is first established. Secondly, for the thermal parameters involved in the heat transfer model, this paper heats the battery based on different heaters in an adiabatic environment, decoupling the acquisition of thermal parameters. Then, based on the above, the temperature field of battery pack at different charge rates are simulated and analyzed. Finally, experiment results validate the accuracy of the model. The results obtained through simulation can provide reference and guidance for the thermal safety problem.

Dissertação de mestrado em Língua, Literatura e Cultura Inglesas, This thesis focuses on traditional fairy tales especially the well-known in Western literature, such as Little Red Riding Hood, Sleeping Beauty, Cinderella, and Snow White to shed light on another side of the popular viewpoints by means of modern retellings such as rewritings, film adaptations to reveal how the development of those tales can go further in contemporary society. In addition, the comparison with fairy tales in the East, mainly in China, through cross-cultural studies, will show us a different understanding of tales from Eastern and Western cultures, which will be helpful to reduce misunderstandings and discriminations. This dissertation starts by referring to the development of fairytales, a concept and a term invented in Western society, and to explore women issues and their desires, cultural and social similarities and differences. Then, we will try to demonstrate how contemporary rewritings show women stepping out of the shadow of men, and how the woman story teller becomes a heroine stepping into readers’ eyesight and fighting for what she desires through reading and analyzing those classical fairytales and their rewritings. In the final part, with the comparison with Chinese versions and Chinese culture and history, we will have discussions about the interpretations of Chinese variants in terms of morality, beliefs, religions to see the diversity and similarity of literature and culture existing in the two civilizations., Esta tese de mestrado foca-se em contos de fadas tradicionais, essencialmente aqueles mais conhecidos da literatura ocidental, como por exemplo, O Capuchinho Vermelho, A Bela Adormecida, Cinderela e a Branca de Neve, com o objetivo de clarificar um outro ponto de vista popular através de modos de propagação modernos como a reescrita e publicação de contos ou adaptações cinematográficas, que revelam como o desenvolvimento destes contos pode continuar na sociedade contemporânea. Para além disso, através da comparação com contos de fadas orientais, maioritariamente provenientes da China, e de estudos interculturais, será possível obter uma perspetiva diferente acerca de contos do Ocidente e do Oriente, de modo a reduzir mal-entendidos e ideias discriminatórias que possam existir. Este documento começa com o desenvolvimento dos contos de fadas, um conceito e termo inventado pela sociedade ocidental, pela descoberta dos problemas e desejos das mulheres, e pelas diferenças e similaridades culturais e sociais. De seguida, será demostrado como a reescrita contemporânea retrata as mulheres a saírem da sombra dos homens, e como a mulher contadora de histórias se torna numa heroína que se atravessa na visão do leitor e luta por aquilo que pretende ao ler e analisar esses contos de fadas clássicos e as suas versões mais recentes. Na parte final, através da comparação entre versões chinesas e a cultura e história da China, serão discutidas as interpretações das variantes chinesas em termos de moralidade, crenças e religião para ver a diversidade e similaridade da literatura e cultura existente nas duas civilizações.

A parallel multidisciplinary optimization design is proposed for the lines design of an underwater vehicle. Resistance and energy consumption are concerned about in obtaining the optimized lines. Collaborative optimization strategy is adopted to construct the optimization framework. The simulated annealing algorithm in combination of the modified method of feasible directions are taken as the optimization algorithms. CFD based simulation is conducted to analyze the resistance of the underwater vehicle. To improve the optimization efficiency, a surrogate model is incorporated into the optimization framework to approximate the CFD calculation. Unlike the conventional static surrogate models used in the design of underwater vehicles, a dynamic surrogate based on trust region and lower confidence bound is proposed. An adaptive balance parameter is taken in the lower confidence bound approach to guarantee the tradeoff between the exploitation and exploration ability of the dynamic surrogate model. Optimal Latin hypercube algorithm is employed as the method of design of experiments. Radial basis function is employed to construct the surrogate model. SUBOFF model is used to demonstrate the proposed optimization scheme. Lines of the underwater vehicle are determined and optimization results of the resistance and energy consumption show the effectiveness of optimization scheme.

To obtain a stabilizer for an underactuated surface vessel with disturbances, an L2-gain design is proposed. Surge, sway, and yaw motions are considered in the dynamics of a surface ship. To ob-tain a robust adaptive controller, a diffeomorphism transformation and the Lyapunov function are employed in controller design. Two auxiliary controllers are introduced for an equivalent sys-tem after the diffeomorphism transformation. Different from the commonly used disturbance ob-server-based approach, the L2-gain design is used to suppress random uncertain disturbances in ship dynamics. To evaluate the controller performance in suppressing disturbances, two error sig-nals are defined in which the variables to be stabilized are incorporated. Both time-invariant dis-continuous and continuous feedback laws are proposed to obtain the control system. Stability analysis and simulation results demonstrate the validity of the controllers proposed. A comparison with a sliding mode controller is performed, and the results prove the advantage of the proposed controller in terms of faster convergence rate and chattering avoidance.

In this paper, Neural Networks (NNs) are used in the modeling of ship maneuvering motion. A nonlinear response model and a linear hydrodynamic model of ship maneuvering motion are also investigated. The maneuverability indices and linear non-dimensional hydrodynamic derivatives in the models are identified by using two-layer feed forward NNs. The stability of parametric estimation is confirmed. Then, the ship maneuvering motion is predicted based on the obtained models. A comparison between the predicted results and the model test results demonstrates the validity of the proposed modeling method.

Lithium-ion batteries have become critical components to industries as automotive, military, space, medical and consumer electronics. Electrochemical impedance spectroscopy (EIS) is a well-known technique employed on various electrochemical cells including lithium-ion cells, which is practical in estimating battery state of health, as ohmic resistance, formation of solid electrolyte interphase (SEI) film, activation on solid and liquid interface and diffusion process in the battery can be reflected in the Nyquist curve of EIS. However, the long acquisition time of standard EIS makes it unsuitable for rapid on-line impedance measurements. This paper presents a time domain measuring technique based on Fast Fourier Transformation (FFT). Time domain excitation, such as a multi-sine excitation, is applied to the measured system. The excitation and response of the system are acquired simultaneously. Through FFT, acquired signals are transformed from time domain to frequency domain, thus the impedance in frequency domain is calculated. The technique is verified on both lithium-ion battery equivalent circuit and real lithium-ion battery. The resulting impedance data show that proposed technique has good concordance with standard EIS and greatly reduceds the acquisition time compared with standard EIS.

Multidisciplinary Design Optimization (MDO) is proposed for the design of the lines of an underwater robot. Hydrodynamic performances of the underwater robot are concerned about in the design, including the resistance and the manoeuvrability. A method of MDO, Collaborative Optimization (CO), is adopted. To improve the efficiency of optimization, approximate models are established in sub-disciplines. Also, an artificial intelligent technique, Particle Swarm Optimization (PSO), is incorporated into the CO framework. The optimization design of the lines of the underwater robot is carried out on an Isight platform, an automatic integration optimization platform. Through the platform, the optimization design of the lines and the analysis of the hydrodynamic performances can be achieved automatically with high efficiency. An autonomous underwater vehicle (AUV), SUBOFF, is taken as a verification model. For different lines, CFD calculation is performed to analyze the resistance and manoeuvrability. By comparison, the optimal lines of the hull and the fairwater are determined.

Software product line (SPL) engineering is an effective method to improve the software development process in terms of development costs and time-to-market by using comprehensive software reuse technology. The feature model is a demand model that describes the common and variability of software product family and the relationship between features in SPL engineering. The difficulty of product configuration based on the feature model is how to choose the optimal combination of features from the complex feature model to satisfy the constraints. In order to achieve the problem of constrained feature selection optimization, we propose a method based on atomic set and a genetic algorithm to optimize feature selection. Firstly, the feature model is optimized by using the atomic set algorithm. Then, the whole constraints of the model are modeled as the evaluation function of the effective and invalid configuration in the genetic algorithm. Finally, by the genetic operations of combining the effective configuration and the invalid configuration, such as crossover, selection and mutation, it selects the best effective configuration for output.

Fault tree analysis (FTA) is a prominent reliability analysis method widely used in safety-critical industries. Computing minimal cut sets (MCSs), i.e., finding all the smallest combination of basic events that result in the top level event, plays a fundamental role in FTA. Classical methods have been proposed based on manipulation of boolean expressions of fault trees and Binary Decision Diagrams. However, given the inherent intractability of computing MCSs, developing new methods over different paradigms remains to be an interesting research direction. In this paper, motivated by recent progress on modern SAT solver, we present a new method for computing MCSs based on SAT solving. Specifically, given a fault tree, we iteratively search for a cut set based on the DPLL framework. By exploiting local failure propagation paths in the fault tree, we provide efficient algorithms for extracting an MCS from the cut set. The information of a new MCS is learned as a blocking clause for SAT solving, which helps to prune search space and ensures completeness of the results. We compare our method with a popular commercial FTA tool on practical fault trees. Preliminary results show that our method exhibits better performance on time and memory usage.