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Cellular Automata Model for Mixed Traffic Flow with Lane Changing Behavior
Indian cities are seen with predominantly mixed traffic plying on the streets. Modeling the mixed traffic involving vehicles characterised of different speed, length, and width is a challenging issue. Based on the finer cell system of cellular automata (CA) models, this paper proposes to evaluate the mixed traffic behavior with cars and motorcycles for intermediate lane width, which is more common in Indian cities. The maximum car flow is observed (even with the presence of motorcycles) in the results which is higher than the Na-Sch model for cars. This increase is mainly due to the changing behavior. The car flow decreases as the density of the motorcycle increases. Furthermore, the paper proposes to evaluate the effect of lane change behavior on the speed and flow of the traffic stream using the fundamental diagrams of speed flow density curves. The simulation result suggests that lane change probability has little effect on the speed and flow of the traffic stream.
Model-Based Hardware-Software Codesign of ECT Digital Processing Unit
Image reconstruction algorithm and its controller constitute the main modules of the electrical capacitance tomography (ECT) system; in order to achieve the trade-off between the attainable performance and the flexibility of the image reconstruction and control design of the ECT system, hardware-software codesign of a digital processing unit (DPU) targeting FPGA system-on-chip (SoC) is presented. Design and implementation of software and hardware components of the ECT-DPU and their integration and verification based on the model-based design (MBD) paradigm are proposed. The inner-product of large vectors constitutes the core of the majority of these ECT image reconstruction algorithms. Full parallel implementation of large vector multiplication on FPGA consumes a huge number of resources and incurs long combinational path delay. The proposed MBD of the ECT-DPU tackles this problem by crafting a parametric segmented parallel inner-product architecture so as to work as the shared hardware core unit for the parallel matrix multiplication in the image reconstruction and control of the ECT system. This allowed the parameterized core unit to be configured at system-level to tackle large matrices with the segment length working as a design degree of freedom. It allows the trade-off between performance and resource usage and determines the level of computation parallelism. Using MBD with the proposed segmented architecture, the system design can be flexibly tailored to the designer specifications to fulfill the required performance while meeting the resources constraint. In the linear-back projection image reconstruction algorithm, the segmentation scheme has exhibited high resource saving of 43% and 71% for a small degradation in a frame rate of 3% and 14%, respectively.
Study on the Dynamic Response of Landslide Subjected to Earthquake by the Improved DDA Method
Majiagou landslide, a major ancient landslide in Three Gorges Reservoir region, is located in the high earthquake area of southwest China. The 2013 Badong earthquake caused an obvious deformation of landslide monitored by the sliding inclinometer. A strong earthquake may induce the reactivation of ancient landslide. So, it is necessary to research the seismic dynamic response of Majiagou landslide. For this purpose, discontinuous deformation analysis (DDA), improved by introducing the artificial joint and viscous boundary, is applied in this study. The displacements at monitoring points caused by Badong earthquake are calculated and compared with the field data, verifying the numerical method and model. Further, a strong earthquake with the peak acceleration of 1 g is assumed to act on the landside, the initiation and evolution process of landslide is simulated, and the movement features of landslide are discussed. The dynamic failure of landslide and the local amplification of seismic wave can be embodied, indicating that the improved DDA provides an alternative approach for analyzing the seismic dynamic response of jointed rock.
Modelling Sustainable Development Aspects within Inventory Supply Strategies
Nowadays, inventory management is a tool that must be extended to cover all aspects of the supply chain (SC). One of these aspects is Sustainable Development (SD) which emphasizes the balance between economic well-being, natural resources, and society. As inventory involves the use of natural and economic resources, the integration of SD criteria is important for a more efficient and sustainable SC. In this work, the most important SD variables associated with inventory management were identified. These variables were integrated as cost elements within a nondeterministic inventory control model to include SD criteria within inventory supply strategies. Through the assessment of the proposed integrated model, it was determined that, although SD practices involve additional investments, specific practices such as reuse/recycling and government incentives can increase revenue and profits. This is important for the development of government and business strategies to perform sustainable practices.
Numerical High-Order Model for the Nonlinear Elastic Computation of Helical Structures
In this work, we propose a high-order algorithm based on the asymptotic numerical method (ANM) for the nonlinear elastic computation of helical structures without neglecting any nonlinear term. The nonlinearity considered in the following study will be a geometric type, and the kinematics adopted in this numerical modeling takes into account the hypotheses of Timoshenko and de Saint-Venant. The finite element used in the discretization of the middle line of this structure is curvilinear with twelve degrees of freedom. Using a simple example, we show the efficiency of the algorithm which was carried out in this context and which resides in the reduction of the number of inversions of the tangent matrix compared to the incremental iterative algorithm of Newton-Raphson.
Energy Analysis of Commercial Buildings Using Artificial Neural Network
Energy consumption in buildings especially in offices is alarming and prompts the desire for more energy analysis work to be done in testing models that can estimate the energy situation of commercial buildings, and the key contributing factors are based on human factors, work load, and weather variables like solar radiation and temperature. In the research, the administration block of the University of Energy and Natural Resources, Ghana, was selected and modeled for energy analysis using SketchUp. Daily energy consumption of the building was generated with EnergyPlus indicating the electricity consumption of the block for the year 2018 for which 68.7% was used by equipment in the block, 26.98% on cooling, and the rest on lighting. The Artificial Neural Network model which had weather variable and days as input neurons and cooling, lighting, equipment, and total building electricity consumption as output neurons was modeled in MATLAB. The model after training had values for training, validation, and testing to be 0.999 and validation performance of . It was able to predict the energy consumption for lighting, cooling, and equipment very close to the results with minimal. The results from the ANN model prediction were compared with the EnergyPlus simulations. The maximum deviation profile for the following parameters (lighting, cooling, and equipment) is 13%, 8%, and 4%, respectively. The large difference in the lighting and cooling is the difficulty involved in predicting human behaviour and weather conditions. The least value recorded for the equipment is due to its independence on external factors.