In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

System Identification I

Preface. Automatic Train Operation System by Predictive Fuzzy Control (S. Yasunobu, S. Miyamoto). Application of Fuzzy Reasoning to the Water Purification Process (O. Yagashita, O. Itoh, M. Sugeno). The Application of a Fuzzy Controller to the Control of a Multi-Degree-of-Freedom Robot Arm (E.M. Scharf, N.J. Mandic). Optimizing Control of a Diesel Engine (Y. Murayama et al.). Development of Performance Adaptive Fuzzy Controllers with Application to Continuous Casting Plants (G. Bartolini et al.). A Fuzzy Logic Controller for Aircraft Flight Control (L.I. Larkin). Automobile Speed Control System Using a Fuzzy Logic Controller (S. Murakami, M. Maeda). An Experimental Study on Fuzzy Parking Control Using a Model Car (M. Sugeno, K. Murakami). A Fuzzy Controller in Turning Process Automation (Y. Sakai, K. Ohkusa). Design of Fuzzy Control Algorithms with the Aid of Fuzzy Models (W. Pedrycz). Human Operator's Fuzzy Model in Man-Machine System with a Nonlinear Controlled Object (K. Matsushima, H. Sugiyama). The Influence of Some Parameters on the Accuracy of a Fuzzy Model (J.B. Kiszka, M.E. Kochanska, D.S. Sliwinska). A Microprocessor Based Fuzzy Controller for Industrial Purposes (T. Yamazaki, M. Sugeno). The Application of Fuzzy and Artificial Intelligence Methods in the Building of a Blast Furnace Smelting Process Model (H. Zhao, M. Ma). An Annotated Bibliography of Fuzzy Control (R.M. Tong).

Industrial applications of fuzzy control

Shape is one of the most important features in Content Based Image Retrieval (CBIR). Many shape representations and retrieval methods exists. However, most of those methods either do not well represent shape or are difficult to do normalization (making matching hard). Among them, methods based Fourier descriptors (FD) achieve both well representation and well normalization. Different shape signatures have been exploited to derive FDs, however, FDs derived from different signatures can have significant different effect on the result of retrieval. In this paper, we build a Java retrieval framework to compare shape retrieval using FDs derived from different signatures. Common issues and techniques for shape representation and normalization are also analyzed in the paper. Data is given to show the retrieval result.

A comparative study on shape retrieval using Fourier descriptiors with different shape signatures

One of the major problems in Big Data is a large number of features or dimensions, which causes the issue of “the curse of dimensionality” when applying machine learning, especially classification algorithms. Feature selection is an important technique which selects small and informative feature subsets to improve the learning performance. Feature selection is not an easy task due to its large and complex search space. Recently, swarm intelligence techniques have gained much attention from the feature selection community because of their simplicity and potential global search ability. However, there has been no comprehensive surveys on swarm intelligence for feature selection in classification which is the most widely investigated area in feature selection. Only a few short surveys is this area are still lack of in-depth discussions on the state-of-the-art methods, and the strengths and limitations of existing methods, particularly in terms of the representation and search mechanisms, which are two key components in adapting swarm intelligence to address feature selection problems. This paper presents a comprehensive survey on the state-of-the-art works applying swarm intelligence to achieve feature selection in classification, with a focus on the representation and search mechanisms. The expectation is to present an overview of different kinds of state-of-the-art approaches together with their advantages and disadvantages, encourage researchers to investigate more advanced methods, provide practitioners guidances for choosing the appropriate methods to be used in real-world scenarios, and discuss potential limitations and issues for future research.

A survey on swarm intelligence approaches to feature selection in data mining

Shape is one of the most important discriminative elements for the content based image retrieval and the most challenging for quantification and description. Fourier descriptors are a very efficient shape description method used in shape-based image retrieval tasks. In order to achieve invariance under rotation and starting point change, most Fourier descriptor implementations disregard the phase of Fourier coefficients, consequently losing valuable information about the shape. This paper proposes a novel method of extracting Fourier descriptors that preserve the phase of Fourier coefficients. We introduce specific points, called pseudomirror points, and use them as a shape orientation reference. They facilitate the extraction of phase-preserving Fourier descriptors which are invariant under translation, scaling, rotation and starting point change. The proposed descriptor was tested on four popular benchmarking datasets: MPEG7 CE-1 Set B, Swedish leaf, ETH-80 and Kimia99 datasets. Performance and computational complexity measures indicate that the proposed method outperforms other state-of-the-art phase-based Fourier descriptors. In addition, it outperforms other state-of-the-art magnitude-based Fourier descriptors, and many non-Fourier based shape description methods in terms of performance - complexity ratio. HighlightsMagnitude-based Fourier descriptors discard information contained in phase.Invariant phase-preserving Fourier descriptor is proposed.Pseudomirror points are introduced, and used as shape orientation references.The proposed descriptor is compact, effective and simple to extract and compare.Retrieval performance is improved without increasing computational complexity.

Phase preserving Fourier descriptor for shape-based image retrieval

The paper deals with one of frequently encountered tasks in process industry - water level control Proportional Integral Derivative (PID) control is often used for this purpose Since control parameters of PID controller are fixed and tank system is inherently nonlinear, PID controller should not be used on wider level range Therefore, this paper analyzes the effectiveness of water level control using fuzzy controller The fuzzy controller is implemented based on mathematical model of tank and using MATLAB The controller is implemented on Friendly ARM - embedded computer Arduino board is used as an acquisition board for collecting sensor data from tank system Festo Didactic DD 3100 and as a PWM signal generator for water pump control Experimental results confirm that the fuzzy control system has good adaptability in comparison with PID and provided satisfying results

Design and implementation of fuzzy controller on embedded computer for water level control

This chapter aims at presenting the general problem of decision making in unknown, complex or changing environment by an extension of static multiobjective optimization problem. General optimization problem is defined, which encompasses not just dynamics, but also change in the optimization problem itself, with focus on changing number of objectives used to evaluate potential solutions.

Evolutionary Approach to Solving Non-stationary Dynamic Multi-Objective Problems

In this paper, we propose one model for high school timetable generation, which uses two-phase linear integer programming to solve the problem. This reduces the required computation time, by decomposing the problem to determine the day and then, in the second phase, to generate a daily schedule. The approach was demonstrated on a test problem, and the results for various settings of the model parameters are presented.

A two phase integer linear programming approach to solving the school timetable problem

Two methods of the multi-objective design optimization of switched reluctance motor with 8 stator and 14 rotor poles (SRM 8/14) are compared in this paper. The first optimization method is based on using the results of Two-Dimensional Finite Element Method (2D FEM) parametric analysis of SRM 8/14. Augmented Lagrangian Genetic Algorithm (ALGA) and 2D FEM are coupled in the second optimization process. The optimization objectives are to minimize torque ripple and maximize average steady-state torque, torque factor, torque quality factor, torque density and losses factor. The optimization variables are the stator and rotor poles arc angles, the taper angles of the stator and rotor poles, stator yoke radius, rotor yoke radius, and stator coil height.

Samim Konjicija

Papers

Phase preserving Fourier descriptor for shape-based image retrieval

Design and implementation of fuzzy controller on embedded computer for water level control

Evolutionary Approach to Solving Non-stationary Dynamic Multi-Objective Problems

A two phase integer linear programming approach to solving the school timetable problem

Multi-objective design optimization of 8/14 switched reluctance motor