José Antonio Vilán

Support Vector Machines Used to Estimate the Battery State of Charge

The aim of this study is to estimate the state of charge (SOC) of a high-capacity lithium iron manganese phosphate (LiFeMnPO4) battery cell from an experimental dataset using a support vector machine (SVM) approach. SVM is a type of learning machine based on statistical learning theory. Many applications require accurate measurement of battery SOC in order to give users an indication of available runtime. It is particularly important for electric vehicles or portable devices. In this paper, the proposed SOC estimator extracts model parameters from battery charging/discharging testing cycles, using cell current, cell voltage, and cell temperature as independent variables. Tests are carried out on a 60 Ah lithium-ion cell with the dynamic stress test cycle to set up the SVM model. The SVM SOC estimator maintains a high level of accuracy, better than 6% over all ranges of operation, whether the battery is charged/discharged at constant current or it is operating in a variable current profile.

Functional classification of ornamental stone using machine learning techniques

Automated classification of granite slabs is a key aspect of the automation of processes in the granite transformation sector. This classification task is currently performed manually on the basis of the subjective opinions of an expert in regard to texture and colour. We describe a classification method based on machine learning techniques fed with spectral information for the rock, supplied in the form of discrete values captured by a suitably parameterized spectrophotometer. The machine learning techniques applied in our research take a functional perspective, with the spectral function smoothed in accordance with the data supplied by the spectrophotometer. On the basis of the results obtained, it can be concluded that the proposed method is suitable for automatically classifying ornamental rock.

Functional Pattern Recognition of 3D Laser Scanned Images of Wood-Pulp Chips

We evaluate the appropriateness of applying a functional rather than the typical vectorial approach to a pattern recognition problem. The problem to be resolved was to construct an online system for controlling wood-pulp chip granulometry quality for implementation in a wood-pulp factory. A functional linear model and a functional logistic model were used to classify the hourly empirical distributions of wood-chip thicknesses estimated on the basis of images produced by a 3D laser scanner. The results obtained using these functional techniques were compared to the results of their vectorial counterparts and support vector machines, whose input consisted of several statistics of the hourly empirical distribution. We conclude that the empirical distributions have sufficiently rich functional traits so as to permit the pattern recognition process to benefit from the functional representation.

Identification of Granite Varieties from Colour Spectrum Data

The granite processing sector of the northwest of Spain handles many varieties of granite with specific technical and aesthetic properties that command different prices in the natural stone market. Hence, correct granite identification and classification from the outset of processing to the end-product stage optimizes the management and control of stocks of granite slabs and tiles and facilitates the operation of traceability systems. We describe a methodology for automatically identifying granite varieties by processing spectral information captured by a spectrophotometer at various stages of processing using functional machine learning techniques.

Shape functional optimization with restrictions boosted with machine learning techniques

Shape optimization is a widely used technique in the design phase of a product. Current ongoing improvement policies require a product to fulfill a series of conditions from the perspective of mechanical resistance, fatigue, natural frequency, impact resistance, etc. All these conditions are translated into equality or inequality restrictions which must be satisfied during the optimization process that is necessary in order to determine the optimal shape. This article describes a new method for shape optimization that considers any regular shape as a possible shape, thereby improving on traditional methods limited to straight profiles or profiles established a priori. Our focus is based on using functional techniques and this approach is, based on representing the shape of the object by means of functions belonging to a finite-dimension functional space. In order to resolve this problem, the article proposes an optimization method that uses machine learning techniques for functional data in order to represent the perimeter of the set of feasible functions and to speed up the process of evaluating the restrictions in each iteration of the algorithm. The results demonstrate that the functional approach produces better results in the shape optimization process and that speeding up the algorithm using machine learning techniques ensures that this approach does not negatively affect design process response times.

Application of a Hybrid 3D-2D Laser Scanning System to the Characterization of Slate Slabs

Dimensional control based on 3D laser scanning techniques is widely used in practice. We describe the application of a hybrid 3D-2D laser scanning system to the characterization of slate slabs with structural defects that are difficult for the human eye to characterize objectively. Our study is based on automating the process using a 3D laser scanner and a 2D camera. Our results demonstrate that the application of this hybrid system optimally characterizes slate slabs in terms of the defects described by the Spanish UNE-EN 12326-1 standard.

Quality Control of Wood-Pulp Chips Using A 3D Laser Scanner and Functional Pattern Recognition

We describe a real-time quality control system for wood chips using a 3D laser scanner. The work evaluates the appropriateness of applying a functional rather than the typical vectorial approach to a pattern recognition problem. The problem to be resolved was to construct an online system for controlling wood-pulp chip granulometry quality for implementation in a wood-pulp factory. A functional linear model and a functional logistic model were used to classify the hourly empirical distributions of wood-chip thicknesses estimated on the basis of images produced by a 3D laser scanner. The results obtained using these functional techniques were compared to the results of their vectorial counterparts and support vector machines, whose input consisted of several statistics of the hourly empirical distribution. We conclude that the empirical distributions have sufficiently rich functional traits so as to permit the pattern recognition process to benefit from the functional representation.

Study of a Steel’s Energy Absorption System for Heavy Quadricycles and Nonlinear Explicit Dynamic Analysis of its Behavior under Impact by FEM

Current knowledge of the behavior of heavy quadricycles under impact is still very poor. One of the most significant causes is the lack of energy absorption in the vehicle frame or its steel chassis structure. For this reason, special steels (with yield stresses equal to or greater than 350 MPa) are commonly used in the automotive industry due to their great strain hardening properties along the plastic zone, which allows good energy absorption under impact. This paper presents a proposal for a steel quadricycle energy absorption system which meets the percentages of energy absorption for conventional vehicles systems. This proposal is validated by explicit dynamics simulation, which will define the whole problem mathematically and verify behavior under impact at speeds of 40 km/h and 56 km/h using the finite element method (FEM). One of the main consequences of this study is that this FEM–based methodology can tackle high nonlinear problems like this one with success, avoiding the need to carry out experimental tests, with consequent economical savings since experimental tests are very expensive. Finally, the conclusions from this innovative research work are given.

Structural design of an RPC-based time-of-flight wall for ions (iTOF) for the R3B-FAIR experiment

In this work we describe the mechanical design of a time-of-flight detector based on strip RPCs dedicated to measure relativistic heavy ions. The proposed design includes innovative solutions which meet the specific requirements to work with ions. The proposal is based on the results of the previous R&D program to build prototypes to test designs, materials and construction solutions, complemented by tests with relativistic ion beams. The first module of the detector has been built to be studied and characterized under beam conditions.

Slate Characterization Using 3D Laser Scanning

Quality control is a necessary component of the slate slab manufacturing process so as to evaluate defects as defined by the current standard for slate. Quality control has traditionally been performed manually by an expert in the field, with the consequent human subjectivity. We studied the feasibility of using a 3D laser scanner to measure slate slabs and analyze possible defects that would lead to the rejection of slabs for particular industrial processes. The application requires slate characterization to be performed in real time and thereby requires a short computation time. We describe an optimized calibration method based on Tsais approach that reduces calculation complexity and cost in this key 3D laser scanning stage. Configured and implemented for slate slab characterization, the system produces the required information in real time during the production process.