Search results for: battery grading algorithm

Authors: Yulong Jia, Xiang Zhang, Ziyuan Wu, Shiji Hu

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The calculation of bilinear pairing is the core of the SM9 algorithm, which relies on the underlying prime domain algorithm and the quadratic extension domain algorithm. Among the field algorithms, modular multiplication operation is the most time-consuming part. Therefore, the underlying modular multiplication algorithm is optimized to maximize the operation speed of bilinear pairings. This paper uses a modular multiplication method based on non-least positive (NLP) combined with Karatsuba and schoolbook multiplication to improve the Montgomery algorithm. At the same time, according to the characteristics of multiplication operation in the quadratic extension domain, a quadratic extension domain FP2-NLP modular multiplication algorithm for bilinear pairings is proposed, which effectively reduces the operation time of modular multiplication in the quadratic extension domain. The sub-expanded domain Fp₂ -NLP modular multiplication algorithm effectively reduces the operation time of modular multiplication under the second-expanded domain. The multiplication unit in the quadratic extension domain is implemented using SMIC55nm process, and two different implementation architectures are designed to cope with different application scenarios. Compared with the existing related literature, The output latency of this design can reach a minimum of 15 cycles. The shortest time for calculating the (AB+CD)r⁻¹ mod form is 37.5ns, and the comprehensive area-time product (AT) is 11400. The final R-ate pairing algorithm hardware accelerator consumes 2670k equivalent logic gates and 1.8ms computing time in 55nm process.

Keywords: sm9, hardware, NLP, Montgomery

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Authors: Christin Koch, Andreas Winkel, Martin Kahlmeyer, Stefan Böhm

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Due to environmental regulations on greenhouse gas emissions and the depletion of fossil fuels, there is an increasing interest in electric vehicles.To maximize their driving range, batteries with high storage capacities are needed. In most electric cars, rechargeable lithium-ion batteries are used because of their high energy density. However, it has to be taken into account that these batteries generate a large amount of heat during the charge and discharge processes. This leads to a decrease in a lifetime and damage to the battery cells when the temperature exceeds the defined operating range. To ensure an efficient performance of the battery cells, reliable thermal management is required. Currently, the cooling is achieved by heat sinks (e.g., cooling plates) bonded to the battery cells with a thermally conductive adhesive (TCA) that directs the heat away from the components. Especially when large amounts of heat have to be dissipated spontaneously due to peak loads, the principle of heat conduction is not sufficient, so attention must be paid to the mechanism of heat storage. An efficient method to store thermal energy is the use of phase change materials (PCM). Through an isothermal phase change, PCM can briefly absorb or release thermal energy at a constant temperature. If the phase change takes place in the transition from solid to liquid, heat is stored during melting and is released to the ambient during the freezing process upon cooling. The presented work displays the great potential of thermally conductive adhesives filled with microencapsulated PCM to limit peak temperatures in battery systems. The encapsulation of the PCM avoids the effects of aging (e.g., migration) and chemical reactions between the PCM and the adhesive matrix components. In this study, microencapsulation has been carried out by in situ polymerization. The microencapsulated PCM was characterized by FT-IR spectroscopy, and the thermal properties were measured by DSC and laser flash method. The mechanical properties, electrical and thermal conductivity, and adhesive toughness of the TCA/PCM composite were also investigated.

Keywords: phase change material, microencapsulation, adhesive bonding, thermal management

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Authors: Utkan Caliskan

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Two types of numerical codes based on finite volume method are developed in order to solve compressible Euler equations to simulate the flow through forward facing step channel. Both algorithms have AUSM+- up (Advection Upstream Splitting Method) scheme for flux splitting and two-stage Runge-Kutta scheme for time stepping. In this study, the flux calculations differentiate between the algorithm based on OpenFOAM mesh format which is called 'face-based' algorithm and the basic algorithm which is called 'element-based' algorithm. The face-based algorithm avoids redundant flux computations and also is more flexible with hybrid grids. Moreover, some of OpenFOAM’s preprocessing utilities can be used on the mesh. Parallelization of the face based algorithm for which atomic operations are needed due to the shared memory model, is also presented. For several mesh sizes, 2.13x speed up is obtained with face-based approach over the element-based approach.

Keywords: cell centered finite volume method, compressible Euler equations, OpenFOAM mesh format, OpenMP

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Authors: S. J. Ha, S. J. Baik, T. O. Kim, S. W. Han

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For computing the seismic responses of structures, current seismic design provisions permit response history analyses (RHA) that can be used without limitations in height, seismic design category, and building irregularity. In order to obtain accurate seismic responses using RHA, it is important to use adequate input ground motions. Current seismic design provisions provide criteria for selecting ground motions. In this study, the accurate and computationally efficient algorithm is proposed for accurately selecting ground motions that satisfy the requirements specified in current seismic design provisions. The accuracy of the proposed algorithm is verified using single-degree-of-freedom systems with various natural periods and yield strengths. This study shows that the mean seismic responses obtained from RHA with seven and ten ground motions selected using the proposed algorithm produce errors within 20% and 13%, respectively.

Keywords: algorithm, ground motion, response history analysis, selection

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Authors: S. Adhirai, R. P. Mahapatra, Paramjit Singh

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Optimization is an important tool in making decisions and in analysing physical systems. In mathematical terms, an optimization problem is the problem of finding the best solution from among the set of all feasible solutions. The paper discusses the Whale Optimization Algorithm (WOA), and its applications in different fields. The algorithm is tested using MATLAB because of its unique and powerful features. The benchmark functions used in WOA algorithm are grouped as: unimodal (F1-F7), multimodal (F8-F13), and fixed-dimension multimodal (F14-F23). Out of these benchmark functions, we show the experimental results for F7, F11, and F19 for different number of iterations. The search space and objective space for the selected function are drawn, and finally, the best solution as well as the best optimal value of the objective function found by WOA is presented. The algorithmic results demonstrate that the WOA performs better than the state-of-the-art meta-heuristic and conventional algorithms.

Keywords: optimization, optimal value, objective function, optimization problems, meta-heuristic optimization algorithms, Whale Optimization Algorithm, implementation, MATLAB

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Authors: Xiaogang Li, Jieqiong Miao

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As for the problem of the grey forecasting model prediction accuracy is low, an improved grey prediction model is put forward. Firstly, use trigonometric function transform the original data sequence in order to improve the smoothness of data , this model called SGM( smoothness of grey prediction model), then combine the improved grey model with support vector machine , and put forward the grey support vector machine model (SGM - SVM).Before the establishment of the model, we use trigonometric functions and accumulation generation operation preprocessing data in order to enhance the smoothness of the data and weaken the randomness of the data, then use support vector machine (SVM) to establish a prediction model for pre-processed data and select model parameters using genetic algorithms to obtain the optimum value of the global search. Finally, restore data through the "regressive generate" operation to get forecasting data. In order to prove that the SGM-SVM model is superior to other models, we select the battery life data from calce. The presented model is used to predict life of battery and the predicted result was compared with that of grey model and support vector machines．For a more intuitive comparison of the three models, this paper presents root mean square error of this three different models .The results show that the effect of grey support vector machine (SGM-SVM) to predict life is optimal, and the root mean square error is only 3.18%. Keywords: grey forecasting model, trigonometric function, support vector machine, genetic algorithms, root mean square error

Keywords: Grey prediction model, trigonometric functions, support vector machines, genetic algorithms, root mean square error

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Authors: Jon Ajuria, Edurne Redondo, Roman Mysyk, Eider Goikolea

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Hybrid capacitor configurations are now of increasing interest to overcome the current energy limitations of supercapacitors entirely based on non-Faradaic charge storage. Among them, Li-ion capacitors including a negative battery-type lithium intercalation electrode and a positive capacitor-type electrode have achieved tremendous progress and have gone up to commercialization. Inexpensive electrode materials from renewable sources have recently received increased attention since cost is a persistently major criterion to make supercapacitors a more viable energy solution, with electrode materials being a major contributor to supercapacitor cost. Additionally, Na-ion battery chemistries are currently under development as less expensive and accessible alternative to Li-ion based battery electrodes. In this work, we are presenting both lithium and sodium ion capacitor (LIC & NIC) entirely based on electrodes prepared from carbon materials derived from recycled olive pits. Yearly, around 1 million ton of olive pit waste is generated worldwide, of which a third originates in the Spanish olive oil industry. On the one hand, olive pits were pyrolized at different temperatures to obtain a low specific surface area semigraphitic hard carbon to be used as the Li/Na ion intercalation (battery-type) negative electrode. The best hard carbon delivers a total capacity of 270mAh/g vs Na/Na+ in 1M NaPF6 and 350mAh/g vs Li/Li+ in 1M LiPF6. On the other hand, the same hard carbon is chemically activated with KOH to obtain high specific surface area -about 2000 m2g-1- activated carbon that is further used as the ion-adsorption (capacitor-type) positive electrode. In a voltage window of 1.5-4.2V, activated carbon delivers a specific capacity of 80 mAh/g vs. Na/Na+ and 95 mAh/g vs. Li/Li+ at 0.1A /g. Both electrodes were assembled in the same hybrid cell to build a LIC/NIC. For comparison purposes, a symmetric EDLC supercapacitor cell using the same activated carbon in 1.5M Et4NBF4 electrolyte was also built. Both LIC & NIC demonstrates considerable improvements in the energy density over its EDLC counterpart, delivering a maximum energy density of 110Wh/Kg at a power density of 30W/kg AM and a maximum power density of 6200W/Kg at an energy density of 27 Wh/Kg in the case of NIC and a maximum energy density of 110Wh/Kg at a power density of 30W/kg and a maximum power density of 18000W/Kg at an energy density of 22 Wh/Kg in the case of LIC. In conclusion, our work demonstrates that the same biomass waste can be adapted to offer a hybrid capacitor/battery storage device overcoming the limited energy density of corresponding double layer capacitors.

Keywords: hybrid supercapacitor, Na-Ion capacitor, supercapacitor, Li-Ion capacitor, EDLC

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Authors: Ágnes Bogárdi-Mészöly, Takeshi Hashimoto, Shohei Yokoyama, Hiroshi Ishikawa

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The spread of Web 2.0 has caused user-generated content explosion. Users can tag resources to describe and organize them. Tag clouds provide rough impression of relative importance of each tag within overall cloud in order to facilitate browsing among numerous tags and resources. The goal of our paper is to enrich visualization of tag clouds. A font distribution algorithm has been proposed to calculate a novel metric based on frequency and content, and to classify among classes from this metric based on power law distribution and percentages. The suggested algorithm has been validated and verified on the tag cloud of a real-world thesis portal.

Keywords: tag cloud, font distribution algorithm, frequency-based, content-based, power law

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Authors: Muhammad Luqman, Yusuf Kurniawan

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S-Boxes is one of the linear parts of the cryptographic algorithm. The existence of S-Box in the cryptographic algorithm is needed to maintain non-linearity of the algorithm. Nowadays, modern cryptographic algorithms use an S-Box as a part of algorithm process. Despite the fact that several cryptographic algorithms today reuse theoretically secure and carefully constructed S-Boxes, there is an evaluation characteristic that can measure security properties of S-Boxes and hence the corresponding primitives. Analysis of an S-Box usually is done using manual mathematics calculation. Several S-Boxes are presented as a Truth Table without any mathematical background algorithm. Then, it’s rather difficult to determine the strength of Truth Table S-Box without a mathematical algorithm. A comprehensive analysis should be applied to the Truth Table S-Box to determine the characteristic. Several important characteristics should be owned by the S-Boxes, they are Nonlinearity, Balancedness, Algebraic degree, LAT, DAT, differential delta uniformity, correlation immunity and global avalanche criterion. Then, a comprehensive tool will be present to automatically calculate the characteristics of S-Boxes and determine the strength of S-Box. Comprehensive analysis is done on a deterministic process to produce a sequence of S-Boxes characteristic and give advice for a better S-Box construction.

Keywords: cryptographic properties, Truth Table S-Boxes, S-Boxes characteristic, deterministic process

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Authors: Barun Chakrabarti, Evangelos Kalamaras, Vladimir Yufit, Xinhua Liu, Billy Wu, Nigel Brandon, C. T. John Low

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In this work, we perform electrophoretic deposition of activated carbon on a number of substrates to prepare symmetrical coin cells for supercapacitor applications. From several recipes that involve the evaluation of a few solvents such as isopropyl alcohol, N-Methyl-2-pyrrolidone (NMP), or acetone to binders such as polyvinylidene fluoride (PVDF) and charging agents such as magnesium chloride, we display a working means for achieving supercapacitors that can achieve 100 F/g in a consistent manner. We then adapt this EPD method to deposit reduced graphene oxide on SGL 10AA carbon paper to achieve cathodic materials for testing in a hydrogen/vanadium flow battery. In addition, a self-supported hierarchical carbon nano-fibre is prepared by means of electrospray deposition of an iron phthalocyanine solution onto a temporary substrate followed by carbonisation to remove heteroatoms. This process also induces a degree of nitrogen doping on the carbon nano-fibres (CNFs), which allows its catalytic performance to improve significantly as detailed in other publications. The CNFs are then used as catalysts by attaching them to graphite felt electrodes facing the membrane inside an all-vanadium flow battery (Scribner cell using serpentine flow distribution channels) and efficiencies as high as 60% is noted at high current densities of 150 mA/cm². About 20 charge and discharge cycling show that the CNF catalysts consistently perform better than pristine graphite felt electrodes. Following this, we also test the CNF as an electro-catalyst in the hydrogen/vanadium flow battery (cathodic side as mentioned briefly in the first paragraph) facing the membrane, based upon past studies from our group. Once again, we note consistently good efficiencies of 85% and above for CNF modified graphite felt electrodes in comparison to 60% for pristine felts at low current density of 50 mA/cm² (this reports 20 charge and discharge cycles of the battery). From this preliminary investigation, we conclude that the CNFs may be used as catalysts for other systems such as vanadium/manganese, manganese/manganese and manganese/hydrogen flow batteries in the future. We are generating data for such systems at present, and further publications are expected.

Keywords: electrospinning, carbon nano-fibres, all-vanadium redox flow battery, hydrogen-vanadium fuel cell, electrocatalysis

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Authors: Shari Ann S. Botin, Ruziel Larmae T. Gimpaya, Rembrant Rockwell Gamboa, Rinlee Butch M. Cervera

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In recent years, demand for the use of solid electrolytes as alternatives to liquid electrolytes has increased due to recurring battery safety and stability issues, in addition to an increase in energy density requirement which can be made possible by using solid electrolytes. Among the solid electrolyte systems, Li7La3Zr2O12 (LLZ) is one of the most promising as it exhibits good chemical stability against Li metal and has a relatively high ionic conductivity. In this study, pure and doped LLZ were synthesized via conventional solid state reaction. The precursor chemicals (such as LiOH, La2O3, Ga2O3 and ZrO2) were ground and then calcined at 900 °C, pressed into pellets and finally sintered at 1000 °C to 1200 °C. The microstructure and ionic conductivity of the obtained samples have been investigated. Results show that for pure LLZ, sintering at lower temperature (1000 °C) produced tetragonal LLZ while sintering at higher temperatures (≥ 1150 °C) produced cubic LLZ based from the XRD results. However, doping with Ga produces an easier formation of LLZ with cubic structure at lower sintering duration. On the other hand, the lithium conductivity of the samples was investigated using electrochemical impedance spectroscopy at room temperature. Among the obtained samples, Ga-doped LLZ sintered at 1150 °C obtained the highest ionic conductivity reaching to about 1x10⁻⁴ S/cm at room temperature. In addition, fabrication and initial investigation of an all-solid state Lithium Battery using the synthesized LLZ sample with the use of commercial cathode materials have been investigated.

Keywords: doped LLZ, lithium-ion battery, pure LLZ, solid electrolytes

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Authors: Sajjad Baghernezhad

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Meta-heuristic and hybrid algorithms have high adeer in modeling medical problems. In this study, a neural network was used to predict covid-19 among high-risk and low-risk patients. This study was conducted to collect the applied method and its target population consisting of 550 high-risk and low-risk patients from the Kerman University of medical sciences medical center to predict the coronavirus. In this study, the memetic algorithm, which is a combination of a genetic algorithm and a local search algorithm, has been used to update the weights of the neural network and develop the accuracy of the neural network. The initial study showed that the accuracy of the neural network was 88%. After updating the weights, the memetic algorithm increased by 93%. For the proposed model, sensitivity, specificity, positive predictivity value, value/accuracy to 97.4, 92.3, 95.8, 96.2, and 0.918, respectively; for the genetic algorithm model, 87.05, 9.20 7, 89.45, 97.30 and 0.967 and for logistic regression model were 87.40, 95.20, 93.79, 0.87 and 0.916. Based on the findings of this study, neural network models have a lower error rate in the diagnosis of patients based on individual variables and vital signs compared to the regression model. The findings of this study can help planners and health care providers in signing programs and early diagnosis of COVID-19 or Corona.

Keywords: COVID-19, decision support technique, neural network, genetic algorithm, memetic algorithm

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Authors: N. Dahraoui, M. Boulakroune, D. Benatia

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In this paper, the improvement by deconvolution of the depth resolution in Secondary Ion Mass Spectrometry (SIMS) analysis is considered. Indeed, we have developed a new Tikhonov-Miller deconvolution algorithm where a priori model of the solution is included. This is a denoisy and pre-deconvoluted signal obtained from: firstly, by the application of wavelet shrinkage algorithm, secondly by the introduction of the obtained denoisy signal in an iterative deconvolution algorithm. In particular, we have focused the light on the effect of the iterations number on the evolution of the deconvoluted signals. The SIMS profiles are multilayers of Boron in Silicon matrix.

Keywords: DRF, in-depth resolution, multiresolution deconvolution, SIMS, wavelet shrinkage

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Authors: B. S. Akhmetov, S. T. Akhmetova, A. I. Ivanov, T. S. Kartbayev, A. Y. Malygin

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The problem of training of a network of artificial neurons in biometric appendices is that this process has to be completely automatic, i.e. the person operator should not participate in it. Therefore, this article discusses the issues of training the network of artificial neurons and the description of the non-iterative learning algorithm of artificial neuron.

Keywords: artificial neuron, biometrics, biometrical applications, learning of neuron, non-iterative algorithm

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Authors: Dimeji Abe, Nonso Okoye, Gideon Ikpimi, Prince Idemudia

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Multi-reservoir optimization operation has been a critical issue for river basin management. Water, as a scarce resource, is in high demand and the problems associated with the reservoir as its storage facility are enormous. The complexity in balancing the supply and demand of this prime resource has created the need to examine the best way to solve the problem using optimization techniques. The objective of this study is to evaluate the performance of the multi-objective meta-heuristic algorithm for the operation of Gariep Dam for satisfying ecological flow requirements. This study uses an evolutionary algorithm called backtrack search algorithm (BSA) to determine the best way to optimise the dam operations of hydropower production, flood control, and water supply without affecting the environmental flow requirement for the survival of aquatic bodies and sustain life downstream of the dam. To achieve this objective, the operations of the dam that corresponds to different tradeoffs between the objectives are optimized. The results indicate the best model from the algorithm that satisfies all the objectives without any constraint violation. It is expected that hydropower generation will be improved and more water will be available for ecological flow requirements with the use of the algorithm. This algorithm also provides farmers with more irrigation water as well to improve their business.

Keywords: BSA evolutionary algorithm, metaheuristics, optimization, river basin management

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Authors: Fadzil Ahmad, Noor Ashidi Mat Isa

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This study introduces an improved genetic algorithm procedure that focuses search around near optimal solution corresponded to a group of elite chromosome. This is achieved through a novel crossover technique known as Segmented Multi Chromosome Crossover. It preserves the highly important information contained in a gene segment of elite chromosome and allows an offspring to carry information from gene segment of multiple chromosomes. In this way the algorithm has better possibility to effectively explore the solution space. The improved GA is applied for the automatic and simultaneous parameter optimization and feature selection of artificial neural network in pattern recognition of medical problem, the cancer and diabetes disease. The experimental result shows that the average classification accuracy of the cancer and diabetes dataset has improved by 0.1% and 0.3% respectively using the new algorithm.

Keywords: genetic algorithm, artificial neural network, pattern clasification, classification accuracy

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Authors: Elham Jelodari Mamaghani, Christian Prins, Haoxun Chen

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In this paper, there is concentration on collaborative transportation planning (CTP) among multiple carriers with pickup and delivery requests and time windows. This problem is a vehicle routing problem with constraints from standard vehicle routing problems and new constraints from a real-world application. In the problem, each carrier has a finite number of vehicles, and each request is a pickup and delivery request with time window. Moreover, each carrier has reserved requests, which must be served by itself, whereas its exchangeable requests can be outsourced to and served by other carriers. This collaboration among carriers can help them to reduce total transportation costs. A mixed integer programming model is proposed to the problem. To solve the model, a hybrid algorithm that combines Genetic Algorithm and Simulated Annealing (GASA) is proposed. This algorithm takes advantages of GASA at the same time. After tuning the parameters of the algorithm with the Taguchi method, the experiments are conducted and experimental results are provided for the hybrid algorithm. The results are compared with those obtained by a commercial solver. The comparison indicates that the GASA significantly outperforms the commercial solver.

Keywords: centralized collaborative transportation, collaborative transportation with pickup and delivery, collaborative transportation with time windows, hybrid algorithm of GA and SA

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Authors: Achim Kampker, Heiner H. Heimes, Mathias Ordung, Christoph Lienemann, Ansgar Hollah, Nemanja Sarovic

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Electric cars with their fast innovation cycles and their disruptive character offer a high degree of freedom regarding innovative design for remanufacturing. Remanufacturing increases not only the resource but also the economic efficiency by a prolonged product life time. The reduced power train wear of electric cars combined with high manufacturing costs for batteries allow new business models and even second life applications. Modular and intermountable designed battery packs enable the replacement of defective or outdated battery cells, allow additional cost savings and a prolongation of life time. This paper discusses opportunities for future remanufacturing value chains of electric cars and their battery components and how to address their potentials with elaborate designs. Based on a brief overview of implemented remanufacturing structures in different industries, opportunities of transferability are evaluated. In addition to an analysis of current and upcoming challenges, promising perspectives for a sustainable electric car circular economy enabled by design for remanufacturing are deduced. Two mathematical models describe the feasibility of pursuing a circular economy of lithium ion batteries and evaluate remanufacturing in terms of sustainability and economic efficiency. Taking into consideration not only labor and material cost but also capital costs for equipment and factory facilities to support the remanufacturing process, cost benefit analysis prognosticate that a remanufacturing battery can be produced more cost-efficiently. The ecological benefits were calculated on a broad database from different research projects which focus on the recycling, the second use and the assembly of lithium ion batteries. The results of this calculations show a significant improvement by remanufacturing in all relevant factors especially in the consumption of resources and greenhouse warming potential. Exemplarily suitable design guidelines for future remanufacturing lithium ion batteries, which consider modularity, interfaces and disassembly, are used to illustrate the findings. For one guideline, potential cost improvements were calculated and upcoming challenges are pointed out.

Keywords: circular economy, electric mobility, lithium ion batteries, remanufacturing

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Authors: Satyendra Pratap Singh, S. P. Singh

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This paper presents a methodology using Gravitational Search Algorithm for optimal placement of Phasor Measurement Units (PMUs) in order to achieve complete observability of the power system. The objective of proposed algorithm is to minimize the total number of PMUs at the power system buses, which in turn minimize installation cost of the PMUs. In this algorithm, the searcher agents are collection of masses which interact with each other using Newton’s laws of gravity and motion. This new Gravitational Search Algorithm based method has been applied to the IEEE 14-bus, IEEE 30-bus and IEEE 118-bus test systems. Case studies reveal optimal number of PMUs with better observability by proposed method.

Keywords: gravitational search algorithm (GSA), law of motion, law of gravity, observability, phasor measurement unit

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Authors: Hee-Choul Kwon, Hyungjin Cho, Heeyong Kwon

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Image rotation is one of main pre-processing step in image processing or image pattern recognition. It is implemented with rotation matrix multiplication. However it requires lots of floating point arithmetic operations and trigonometric function calculations, so it takes long execution time. We propose a new high speed image rotation algorithm without two major time-consuming operations. We compare the proposed algorithm with the conventional rotation one with various size images. Experimental results show that the proposed algorithm is superior to the conventional rotation ones.

Keywords: high speed rotation operation, image processing, image rotation, pattern recognition, transformation matrix

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Authors: Ali Moradiamani, Samaneh Sadat Sajjadi, Mahdi Jalili

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Power system analysis has been a major research topic in the generation and distribution sections, in both industry and academia, for a long time. Several load flow and fault analysis scenarios have been normally performed to study the performance of different parts of the grid in the context of, for example, voltage and frequency control. Software tools, such as PSCAD, PSSE, and PowerFactory DIgSILENT, have been developed to perform these analyses accurately. Distribution grid had been the passive part of the grid and had been known as the grid of consumers. However, a significant paradigm shift has happened with the emergence of Distributed Energy Resources (DERs) in the distribution level. It means that the concept of power system analysis needs to be extended to the distribution grid, especially considering self sufficient technologies such as microgrids. Compared to the generation and transmission levels, the distribution level includes significantly more generation/consumption nodes thanks to PV rooftop solar generation and battery energy storage systems. In addition, different consumption profile is expected from household residents resulting in a diverse set of scenarios. Emergence of electric vehicles will absolutely make the environment more complicated considering their charging (and possibly discharging) requirements. These complexities, as well as the large size of distribution grids, create challenges for the available power system analysis software. In this paper, we study the requirements of simulation tools in the distribution grid and how data-driven algorithms are required to increase the accuracy of the simulation results.

Keywords: smart grids, distributed energy resources, electric vehicles, battery storage systsms, simulation tools

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Authors: Alejandro Ruiz-Olivares, M. del Carmen González-Chávez, Rogelio Carrillo-González, Martha Reyes-Ramos, Javier Suárez Espinosa

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Use of automobiles has increased and proportionally, the demand for batteries to impulse them. When the device is aged, all the battery materials are reused through lead acid battery recycling (LABR). Importation of used lead acid batteries in Mexico has increased in the last years since many recycling factories have been settled in the country. Inadequate disposal of lead-acid battery recycling (LABR) wastes left soil severely polluted with Pb, Cu, and salts (Na+, SO2− 4, PO3− 4). Soil organic amendments may contribute with essential nutrients and sequester (scavenger compounds) metals to allow plant establishment. The objective of this research was to revegetate a former lead-acid battery recycling site aided with organic amendments. Seven tree species (Acacia farnesiana, Casuarina equisetifolia, Cupressus lusitanica, Eucalyptus obliqua, Fraxinus excelsior, Prosopis laevigata and Pinus greggii) and two organic amendments (vermicompost and vermicompost + sawdust mixture) were tested for phytoremediation of a defunct LABR site. Plants were irrigated during the dry season. Monitoring of the soils was carried out during the experiment: Available metals, salts concentrations and their spatial pattern in soil were analyzed. Plant species and amendments were compared through analysis of covariance and longitudinal analysis. High concentrations of extractable (DTPA-TEA-CaCl₂) metals (up to 15,685 mg kg⁻¹ and 478 mg kg⁻¹ for Pb and Cu) and soluble salts (292 mg kg-1 and 23,578 mg kg-1 for PO3− 4and SO2− 4) were found in the soil after three and six months of setting up the experiment. Lead and Cu concentrations were depleted in the rhizosphere after amendments addition. Spatial pattern of PO3− 4, SO2− 4 and DTPA-extractable Pb and Cu changed slightly through time. In spite of extreme soil conditions the plant species planted: A. farnesiana, E. obliqua, C. equisetifolia and F. excelsior had 100% of survival. Available metals and salts differently affected each species. In addition, negative effect on growth due to Pb accumulated in shoots was observed only in C. lusitanica. Many specimens accumulated high concentrations of Pb ( > 1000 mg kg-1) in shoots. C. equisetifolia and C. lusitanica had the best rate of growth. Based on the results, all the evaluated species may be useful for revegetation of Pb-polluted soils. Besides their use in phytoremediation, some ecosystem services can be obtained from the woodland such as encourage wildlife, wood production, and carbon sequestration. Further research should be conducted to analyze these services.

Keywords: heavy metals, inadequate disposal, organic amendments, phytoremediation with trees

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Authors: Jian Shi, Baoguo Yu, Haonan Jia, Meng Liu, Ping Huang

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Adapting to the flexibility of war, a large number of guided weapons launch from aircraft. Therefore, the inertial navigation system loaded in the weapon needs to undergo an alignment process in the air. This article proposes the following methods to the problem of inaccurate modeling of the system under large misalignment angles, the accuracy reduction of filtering caused by outliers, and the noise changes in GPS signals: first, considering the large misalignment errors of Strapdown Inertial Navigation System (SINS)/GPS, a more accurate model is made rather than to make a small-angle approximation, and the Unscented Kalman Filter (UKF) algorithms are used to estimate the state; then, taking into account the impact of GPS noise changes on the fine alignment algorithm, the innovation adaptive filtering algorithm is introduced to estimate the GPS’s noise in real-time; at the same time, in order to improve the anti-interference ability of the air fine alignment algorithm, a robust filtering algorithm based on outlier detection is combined with the air fine alignment algorithm to improve the robustness of the algorithm. The algorithm can improve the alignment accuracy and robustness under interference conditions, which is verified by simulation.

Keywords: air alignment, fine alignment, inertial navigation system, integrated navigation system, UKF

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Authors: Fuzhang Zhao

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Particle swarm optimization (PSO) is becoming one of the most important swarm intelligent paradigms for solving global optimization problems. Although some progress has been made to improve PSO algorithms over the last two decades, additional work is still needed to balance parameters to achieve better numerical properties of accuracy, efficiency, and stability. In the optimal PSO algorithm, the optimal weightings of (√ 5 − 1)/2 and (3 − √5)/2 are used for the cognitive factor and the social factor, respectively. By the same token, the same optimal weightings have been applied for intensification searches and diversification searches, respectively. Perturbation and constriction effects are optimally balanced. Simulations of the de Jong, the Rosenbrock, and the Griewank functions show that the optimal PSO algorithm indeed achieves better numerical properties and outperforms the canonical PSO algorithm.

Keywords: diversification search, intensification search, optimal weighting, particle swarm optimization

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Authors: Ghada Badr, Arwa Alturki

Abstract:

The biological function of an RNA molecule depends on its structure. The objective of the alignment is finding the homology between two or more RNA secondary structures. Knowing the common functionalities between two RNA structures allows a better understanding and a discovery of other relationships between them. Besides, identifying non-coding RNAs -that is not translated into a protein- is a popular application in which RNA structural alignment is the first step A few methods for RNA structure-to-structure alignment have been developed. Most of these methods are partial structure-to-structure, sequence-to-structure, or structure-to-sequence alignment. Less attention is given in the literature to the use of efficient RNA structure representation and the structure-to-structure alignment methods are lacking. In this paper, we introduce an O(N2) Component-based Pairwise RNA Structure Alignment (CompPSA) algorithm, where structures are given as a component-based representation and where N is the maximum number of components in the two structures. The proposed algorithm compares the two RNA secondary structures based on their weighted component features rather than on their base-pair details. Extensive experiments are conducted illustrating the efficiency of the CompPSA algorithm when compared to other approaches and on different real and simulated datasets. The CompPSA algorithm shows an accurate similarity measure between components. The algorithm gives the flexibility for the user to align the two RNA structures based on their weighted features (position, full length, and/or stem length). Moreover, the algorithm proves scalability and efficiency in time and memory performance.

Keywords: alignment, RNA secondary structure, pairwise, component-based, data mining

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Authors: Chunye Gong, Ming Tie, Jie Liu, Weimin Bao, Xinbiao Gan, Shengguo Li, Bo Yang, Xuguang Chen, Tiaojie Xiao, Yang Sun

Abstract:

Large-scale CFD simulation relies on high-performance parallel computing, and the load balance is the key role which affects the parallel efficiency. This paper focuses on the load-balancing problem of parallel CFD simulation with structured mesh. A mathematical model for this load-balancing problem is presented. The genetic algorithm, fitness computing, two-level code are designed. Optimal selector, robust operator, and local optimization operator are designed. The properties of the presented genetic algorithm are discussed in-depth. The effects of optimal selector, robust operator, and local optimization operator are proved by experiments. The experimental results of different test sets, DLR-F4, and aircraft design applications show the presented load-balancing algorithm is robust, quickly converged, and is useful in real engineering problems.

Keywords: genetic algorithm, load-balancing algorithm, optimal variation, local optimization

Procedia PDF Downloads 183

Authors: Guilherme Baldo Carlos

Abstract:

The flexible job-shop scheduling problem (FJSP) is an NP-hard combinatorial optimization problem, which can be applied to model several applications in a wide array of industries. This problem will have its importance increase due to the shift in the production mode that modern society is going through. The demands are increasing and for products personalized and customized. This work aims to apply a meta-heuristic called a genetic algorithm (GA) to solve this problem. A GA is a meta-heuristic inspired by the natural selection of Charles Darwin; it produces a population of individuals (solutions) and selects, mutates, and mates the individuals through generations in order to find a good solution for the problem. The results found indicate that the GA is suitable for FJSP solving.

Keywords: genetic algorithm, evolutionary algorithm, scheduling, flexible job-shop scheduling

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Authors: Yeongyu Choi, Ju H. Park, Ho-Youl Jung

Abstract:

In this paper, we propose labeling based RANSAC algorithm for lane detection. Advanced driver assistance systems (ADAS) have been widely researched to avoid unexpected accidents. Lane detection is a necessary system to assist keeping lane and lane departure prevention. The proposed vision based lane detection method applies Canny edge detection, inverse perspective mapping (IPM), K-means algorithm, mathematical morphology operations and 8 connected-component labeling. Next, random samples are selected from each labeling region for RANSAC. The sampling method selects the points of lane with a high probability. Finally, lane parameters of straight line or curve equations are estimated. Through the simulations tested on video recorded at daytime and nighttime, we show that the proposed method has better performance than the existing RANSAC algorithm in various environments.

Keywords: Canny edge detection, k-means algorithm, RANSAC, inverse perspective mapping

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Authors: Felix Damilola Ajibade, Opeyemi O. Enoch, Taiwo Paul Fajusigbe

Abstract:

This paper is centered on conducting an inquiry into the stability of the F* iterative algorithm to the fixed point of a strongly pseudo-contractive mapping in the framework of uniformly convex Banach spaces. To achieve the desired result, certain existing inequalities in convex Banach spaces were utilized, as well as the stability criteria of Harder and Hicks. Other necessary conditions for the stability of the F* algorithm on strong pseudo-contractive mapping were also obtained. Through a numerical approach, we prove that the F* iterative algorithm is H-stable for strongly pseudo-contractive mapping. Finally, the solution of the mixed-type Volterra-Fredholm functional non-linear integral equation is estimated using our results.

Keywords: stability, F* -iterative algorithm, pseudo-contractive mappings, uniformly convex Banach space, mixed-type Volterra-Fredholm integral equation

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Authors: Mallikarjuna Rao Yamarthy, Subramanyam Makam Venkata, Satya Prasad Kodati

Abstract:

The main objective of the proposed work is to reduce the overall network traffic incurred by mobility management, packet delivery cost and to increase the resource utilization. The proposed algorithm, An Efficient Resource Management Algorithm (ERMA) for mobility management in wireless mesh networks, relies on pointer based mobility management scheme. Whenever a mesh client moves from one mesh router to another, the pointer is set up dynamically between the previous mesh router and current mesh router based on the distance constraints. The algorithm evaluated for signaling cost, data delivery cost and total communication cost performance metrics. The proposed algorithm is demonstrated for both internet sessions and intranet sessions. The proposed algorithm yields significantly better performance in terms of signaling cost, data delivery cost, and total communication cost.

Keywords: data delivery cost, mobility management, pointer forwarding, resource management, wireless mesh networks

Procedia PDF Downloads 367