Bi-level Decision-making Modeling for an Autonomous Driver Agent: Application in the Car-following Driving Behavior

ABSTRACT

Road crashes are present as an epidemic in road traffic and continue to grow up, where, according to World Health Organization; they cause more than 1.24 million deaths each year and 20 to 50 million non-fatal injuries, so they should represent by 2020 the third leading global cause of illness and injury. In this context, we are interested in this paper to the car-following driving behavior problem, since it alone accounts for almost 70% of road accidents, which they are caused by the incorrect judgment of the driver to keep a safe distance. Thus, we propose in this paper a decision-making model based on bi-level modeling, whose objective is to ensure the integration between road safety and the reducing travel time. To ensure this objective, we used the fuzzy logic approach to model the anticipation concept in order to extract more unobservable data from the road environment. Furthermore, we used the fuzzy logic approach in order to model the driver behaviors, in particular, the normative behaviors. The experimental results indicate that the decision to increase in velocity based on our model is ensured in the context of respecting the road safety.     

Opinion mining and sentiment analysis for Arabic on-line texts: application on the political domain

Since the continuous proliferation of the journalistic content online and the changing political landscape in many Arabic countries, we started our current research in order to implement a media monitoring system about the opinion mining in political field. This system allows political actors, despite of the large volume of online data, to be constantly informed about opinions expressed on the web in order to properly monitor their actual standing, orient their communication strategy and prepare the election campaigns. The developed system is based on a linguistic approach using NooJ’s linguistic engine to formalize the automatic recognition rules and apply them to a dynamic corpus composed of journalistic articles. The first implemented rules allow identifying and annotating the different political entities (political actors and organizations). Then these annotations are used in our system of media monitoring in order to identify the opinions associated with the extracted named entities. The system is mainly based on a set of local grammars developed for the identification of different structures of the political opinion phrases. These grammars are using the entries of the opinion lexicon that contain the different opinion words (verbs, adjectives, nouns) where each entry is associated with the corresponding semantic marker (polarity and intensity). Our developed system is able to identify and properly annotate the opinion holder, the opinion target and the polarity (positive or negative) of the phraseological expression (nominal or verbal) expressing the opinion. Our experiments showed that the adopted method of extraction is consistent with 0.83 F-measure.     

Simultaneous Actuator and Sensor Faults Reconstruction Based on Robust Sliding Mode Observer for a Class of Nonlinear Systems

This paper proposes a new robust fault reconstruction and estimation design for a class of nonlinear system described by the Takagi‐Sugeno model with unmeasurable premise variables subject to faults affecting actuators, sensor faults, and unknown disturbances. The augmented Takagi‐Sugeno system is introduced with a new fault vector which has two origins: the first one represents actuator faults, the second one denotes faults affecting sensors. The main contribution is focused primarily to conceive a sliding mode observer with two discontinuous terms designed to compensate for fault behavior and disturbance variation from the system states estimation. In the formalism of linear matrix inequalities, we derive sufficient conditions to guarantee the state estimation error stability and to obtain the observer gains. Meanwhile, additional effort is made to achieve simultaneous faults and disturbance reconstruction. Simulation results are given to illustrate the proposed approach performances.     

On the use of many quality attributes for software refactoring: a many-objective search-based software engineering approach

Search-based software engineering (SBSE) solutions are still not scalable enough to handle high-dimensional objectives space. The majority of existing work treats software engineering problems from a single or bi-objective point of view, where the main goal is to maximize or minimize one or two objectives. However, most software engineering problems are naturally complex in which many conflicting objectives need to be optimized. Software refactoring is one of these problems involving finding a compromise between several quality attributes to improve the quality of the system while preserving the behavior. To this end, we propose a novel representation of the refactoring problem as a many-objective one where every quality attribute to improve is considered as an independent objective to be optimized. In our approach based on the recent NSGA-III algorithm, the refactoring solutions are evaluated using a set of 8 distinct objectives. We evaluated this approach on one industrial project and seven open source systems. We compared our findings to: several other many-objective techniques (IBEA, MOEA/D, GrEA, and DBEA-Eps), an existing multi-objective approach a mono-objective technique and an existing refactoring technique not based on heuristic search. Statistical analysis of our experiments over 31 runs shows the efficiency of our approach.     

A robust framework for joint background/foreground segmentation of complex video scenes filmed with freely moving camera

This paper explores a robust region-based general framework for discriminating between background and foreground objects within a complex video sequence. The proposed framework works under difficult conditions such as dynamic background and nominally moving camera. The originality of this work lies essentially in our use of the semantic information provided by the regions while simultaneously identifying novel objects (foreground) and non-novel ones (background). The information of background regions is exploited to make moving objects detection more efficient, and vice-versa. In fact, an initial panoramic background is modeled using region-based mosaicing in order to be sufficiently robust to noise from lighting effects and shadowing by foreground objects. After the elimination of the camera movement using motion compensation, the resulting panoramic image should essentially contain the background and the ghost-like traces of the moving objects. Then, while comparing the panoramic image of the background with the individual frames, a simple median-based background subtraction permits a rough identification of foreground objects. Joint background-foreground validation, based on region segmentation, is then used for a further examination of individual foreground pixels intended to eliminate false positives and to localize shadow effects. Thus, we first obtain a foreground mask from a slow-adapting algorithm, and then validate foreground pixels (moving visual objects + shadows) by a simple moving object model built by using both background and foreground regions. The tests realized on various well-known challenging real videos (across a variety of domains) show clearly the robustness of the suggested solution. This solution, which is relatively computationally inexpensive, can be used under difficult conditions such as dynamic background, nominally moving camera and shadows. In addition to the visual evaluation, spatial-based evaluation statistics, given hand-labeled ground truth, has been used as a performance measure of moving visual objects detection.     

A Hybrid Fuzzy-Based Multi-Objective PSO Algorithm for Conjunctive Water Use and Optimal Multi-Crop Pattern Planning

This paper focuses on extracting an optimal multi-crop pattern plan through multi-objective conjunctive surface-ground water use management. Minimizing shortages in meeting irrigation demands, maximizing groundwater resources sustainability and maximizing agricultural net benefits are the three main goals of the multi-objective optimization problem solved in this paper. A new robust fuzzy-based multi-objective PSO algorithm called f-MOPSO is adopted and modified to solve a three-objective real-world conjunctive use management problem presented in this paper after testing on standard test problems revealed f-MOPSO superiority as compared to the well-known multi-swarm vector evaluated PSO (VEPSO) algorithm. The f-MOPSO benefits from a well-organized Sugeno fuzzy inference system (SFIS) designed for handling multi-objective nature of the optimization problems. The unique performance of f-MOPSO is not only presenting the better final solutions, but also aggregating the capabilities for measurement of dominance and diversity of the solutions in one stage by one index named comprehensive dominance index, in contrast to a wide range of multi-objective algorithms that evaluate dominance and diversity in two separate stages resulting in excessive computational burden. The optimization model is carried out on a 10-year long-term simulation period, resulting in increasing irrigation efficiency i.e. decreasing water losses, decreasing water consumption per unit cultivated area and increasing water productivity compared to those similar criteria observed in actual operation in the study area. The wheat and rice crops were identified as the dominant crops, while the optimization model was the least interested to onion cultivation, assigning the least average cultivation area to this crop over the whole planning period.     

An Artificial Intelligence Approach for the Stochastic Management of Coastal Aquifers

Aquifer recharge rates and patterns are often uncertain, especially in arid areas due to sporadic and erratic rainfall. Therefore, determining the optimal groundwater abstraction using classical approaches such as Monte Carlo Simulation (MCS) requires a large number of groundwater simulations and exorbitant computational efforts. The problem becomes even more complex and time consuming for regional coastal aquifers whose domains must be discretized using high-resolution meshes. In fact, even fast evolutionary multi-objective optimization techniques generally require a large number of simulations to determine the Pareto-front among the objectives. This study explores the performance of a Decision Tree (DT) approach for the generation of the Pareto optimal solutions of groundwater extraction. This paper applies the DTs for the optimal management of the Al-Khoud coastal aquifer in Oman. The learning process of the developed DT-based model uses the output of a numerical simulation model to assess the aquifer response based on different abstraction policies. The trained DT network then utilizes the NSGA-II to determine the Pareto-optimal solutions. The simulation show that the general flux pattern in the study area is toward the sea and the hydraulic head following a similar pattern in both best and worst recharging scenarios downstream of the studied recharging dam. Statistical tests showed a good correlation between the DT-based and simulation-based results and demonstrate the capability of the DT approach to obtain high-quality solutions by incorporating a large number of recharge scenarios. Moreover, the required runtime of the DT-based approach is extremely low (5 min) compared to that of the simulation-based method (several days). This means that including additional Monte-Carlo simulations can be readily done in few minutes using the obtained DTs, instead of the long computational time needed by the simulation-based approach.     

The Computer Integrated Road Construction project

This paper is about the “Computer Integrated Road Construction” (CIRC) project, which is a Brite-EuRam III funded project, lasting 1997–1999, aiming at introducing a new generation of control and monitoring tools for road pavements construction. These new tools are designed to bring on the sites significant improvements by creating a digital link between design office and job site. The first part of the paper describes the background of the project, which gathers seven European partners from five different countries, and gives the objectives of the project, in general and for each of the two targeted products: one for the compactors (CIRCOM) and one for the asphalt pavers (CIRPAV). Then, the two prototypes are described, each of them being broken down into three main sub-systems: the ground sub-system (GSS), the on-board sub-system (OB) and the positioning sub-system (POS). The expected benefits for the different users are also presented and quantified. The central part of the paper is devoted to the main technical innovations that have been developed in the frame of the project: universal vector database for road equipment guidance, multi-machine functionalities of CIRCOM and the two positioning systems which are actually the technological keys of the systems. Finally, the state of progress of the developments of the two CIRC products and the first commercial success achieved in parallel are presented.     

Nutritional constituents,phytochemical profiles, <Emphasis Type="Italic">in vitro</Emphasis> antioxidant and antimicrobial properties,and gas chromatography–mass spectrometry analysis of various solvent extracts from grape seeds (<Emphasis Type="Italic">Vitis vinifera</Emphasis> L.)

The present study revealed that the nutritive value of grape seeds (Vitis vinifera L.) was 383.55±0.13 Kcal/100 g, with magnesium as the most abundant mineral element (70.44±0.88 mg/L). The maximum phenolic (392.58±1.70mg of GAE/g), flavonoid (256.16±1.60 mg of QE/g), and tannin (30.95±0.17mg of CE/g) contents were also found in the ethanol, dichloromethane, and hexane extracts, respectively. The major phytochemical compounds in the ethyl acetate extract were identified via gas chromatography–mass spectrometry (GC–MS) analysis. The ethanol extract has the highest antioxidant activity (IC₅₀=140±1.20 μg/mL for DPPH, 145.28±0.45mg α-tocopherol/g for total antioxidant capacity, and EC₅₀=80±1.41 μg/mL for ferric-reducing power assays). For β-carotene test, the highest antioxidant activity was obtained in the hexane extract. A satisfactory antimicrobial activity was found against a panel of microorganisms with the ethyl acetate extract as the best antimicrobial agent. Additionally, it was found that the bactericidal concentration required for the grape seed extract to kill Listeria monocytogenes should be less than 12.50 mg/mL (minimum inhibitory concentration=4).     

Experimental study of the salt gradient solar pond stability

Many natural systems such as oceans, lakes, etc.…, are influenced by the effect of double-diffusive convection. This phenomenon, which is a combination of heat and mass transfer, can destroy the stability of system-flows.In the case of solar ponds the middle layer, that is linearly stratified, acts as a thermal and mass insulator for the lower layer. This middle layer, called the Non-Convective Zone (NCZ), needs special care to avoid convection and to maintain its stability. In fact, due to an excess of heat stored, a thermal gradient occurs within the NCZ. A convective movement appears at the bottom of the stratified-layers and then grows to a double-diffusive convection movement. This movement transforms the stratified-layers into a well mixed layer, reducing the storage capacity of the pond.Laboratory small-scale pond and middle-scale outdoor solar ponds were designed and built to provide both quantitative data and to study the dynamic processes in solar ponds, including the behavior of the gradient zone.Particle Image Velocimetry (PIV) visualization-experiments carried out in the mechanical and energetic laboratory in the engineering school of Tunisia and experiments in the field showed that the instability of solar ponds could be limited by using porous media placed in the lower layer of the stratification.