Hamit Erdem

A multi-channel remote controller for home and office appliances

This paper presents the design and implementation of a multi-channel remote controller (MCRC) for home and office appliances. The aim of this work is to integrate several existing remote controller channels in a common platform. The proposed controller supports access to controlled environment via Web Page, Smart-Phone (SP), PDA, GSM network and telephone lines. The suggested controller is more reliable than conventional ones especially during emergency condition such as main server failure and interruption in GSM or Internet network. The suggested system enables easy and flexible access to controlled devices. The hardware part of the controller contains a home server which is built upon PC and auxiliary microcontrollers. The feasibility of this architecture has been demonstrated with a prototype implementation and presented in details.

Comparison of fuzzy, PI and fixed frequency sliding mode controller for DC-DC converters

This paper presents a comprehensive study about the application of three control methods in DC/DC converters. Fuzzy, proportional-integral and fixed frequency sliding mode controllers are applied to DC/DC buck converter. Fixed frequency sliding mode controller is studied with two different design approaches. Advantages, disadvantages, similarities and design procedures of controllers are studied. The dynamic performance of these controllers under input voltage change and load current variations are presented.

A Review of KDD99 Dataset Usage in Intrusion Detection and Machine Learning between 2010 and 2015

Although KDD99 dataset is more than 15 years old, it is still widely used in academic research. To investigate wide usage of this dataset in Machine Learning Research (MLR) and Intrusion Detection Systems (IDS); this study reviews 149 research articles from 65 journals indexed in Science Citation In- dex Expanded and Emerging Sources Citation Index during the last six years (2010–2015). If we include papers presented in other indexes and conferences, number of studies would be tripled. The number of published studies shows that KDD99 is the most used dataset in IDS and machine learning areas, and it is the de facto dataset for these research areas. To show recent usage of KDD99 and the related sub-dataset (NSL-KDD) in IDS and MLR, the following de- scriptive statistics about the reviewed studies are given: main contribution of articles, the applied algorithms,

Optimization of waiting and journey time in group elevator system using genetic algorithm

Efficient elevator group control is an important issue for vertical transportation in high-rise buildings. From the engineering design perspective, regulation of average waiting time and journey time while considering energy consumption is an optimization problem. Alternatively to the conventional algorithms for scheduling and dispatching cars to hall calls, intelligent systems based methods have drawn much attention in the last years. This study aims to improve the elevator group control systems performance by applying genetic algorithm based optimization algorithms considering two systems. Firstly, average passenger waiting time is optimized in the conventional elevator systems in which a hall call is submitted by indicating the travel direction. Secondly, a recent development in elevator industry is considered and it is assumed that instead of direction indicators there are destination button panels at floors that allow passengers to specify their destinations. In this case optimization of average waiting time, journey time and car trip time is investigated. Two proposed algorithms have been applied considering preload conditions in a building with 20 floors and 4 cars. The simulation results have been compared with a previous study and conventional duplex algorithm.

A practical fuzzy logic controller for sumo robot competition

This paper describes the design of fuzzy logic based sumo wrestling robot. The designed robot has a simple control algorithm and single fuzzy microcontroller is used in hardware implementation. The designed robot meets the specifications needed to compete in a sumo robot competition. The main difference of the designed system with earlier sumo robots is in control algorithms. A simple fuzzy logic controller is developed for detection and tracking of opponent in competition ring. Three infrared (IR) sharp sensors are used for target detection. Fuzzy microcontroller fuses the sensor datas and provides the necessary control signal to motors for heading robot toward the opponent. The fuzzy rules were optimized for the best results possible in software which are loaded in fuzzy controller. The implemented control algorithm shows better performance and executes the opponent detection algorithm in less time in comparison with conventional sumo robot algorithm. Design procedure and experimental results are presented to show the performance of the intelligent controller in designed system.

Implementation of PSO-based fixed frequency sliding mode controller for buck converter

This study presents the application of the optimized Fixed Frequency Sliding Mode (FFSM) Controller for a Synchronous Buck Converter. The sliding coefficient of the controller, which affects the controller performance, is optimized by using Particle Swarm Optimization (PSO). In order to analysis the performance of the optimized controller, a DC-DC Buck converter which has a variable structure and nonlinear model is used as a test-bed. The controller design procedure and optimization approach are presented in details. Simulation and hardware implementation results are provided to demonstrate the performance of the optimized controller under load and line variations.

A Random Forest Method to Detect Parkinson’s Disease via Gait Analysis

Remote care and telemonitoring have become essential component of current geriatric medicine. Intelligent use of wireless sensors is a major issue in relevant computational studies to realize these concepts in practice. While there has been a growing interest in recognizing daily activities of patients through wearable sensors, the efforts towards utilizing the streaming data from these sensors for clinical practices are limited. Here, we present a practical application of clinical data mining from wearable sensors with a particular objective of diagnosing Parkinson’s Disease from gait analysis through a sets of ground reaction force (GRF) sensors worn under the foots. We introduce a supervised learning method based on Random Forests that analyze the multi-sensor data to classify the person wearing these sensors. We offer to extract a set of time-domain and frequency-domain features that would be effective in distinguishing normal and diseased people from their gait signals. The experimental results on a benchmark dataset have shown that proposed method can significantly outperform the previous methods reported in the literature.

Tuning of output scaling factor in PI-like fuzzy controllers for power converters using PSO

ProportionalIntegral (PI) like Fuzzy Logic Controllers (FLC) has been widely used for control of static power converters (SPC). The performance of these controllers is sensitive to controller rules, parameters of membership functions and input-output scaling factors. Among these parameters, scaling factor (SF) directly affects the controller performance in terms of transient response, steady state error and stability. Therefore, using an optimum SF value increases the performance of the FLC against using a constant value. Hence, in this paper optimizing the output scaling factor (OSF) of the PI-like fuzzy logic controller (PIFLC) by using Particle Swarm Optimization (PSO) algorithm is proposed. In order to optimize and analyze the effect of this parameter on the controller performance, first the output scaling factor of FLC is optimized with various PSO algorithms and one of these algorithms is selected for experimental test. Then optimized FLC is applied to a DC-DC Buck converter, and the performance of the controller is evaluated under nominal load and load disturbance. The controller design, the OSF optimization, and the controller performance analysis approaches are presented in detail.

Sparsity-driven weighted ensemble classifier

In this study, a novel sparsity-driven weighted ensemble classifier (SDWEC) that improves classification accuracy and minimizes the number of classifiers is proposed. Using pre-trained classifiers, an ensemble in which base classifiers votes according to assigned weights is formed. These assigned weights directly affect classifier accuracy. In the proposed method, ensemble weights finding problem is modeled as a cost function with the following terms: (a) a data fidelity term aiming to decrease misclassification rate, (b) a sparsity term aiming to decrease the number of classifiers, and (c) a non-negativity constraint on the weights of the classifiers. As the proposed cost function is non-convex thus hard to solve, convex relaxation techniques and novel approximations are employed to obtain a numerically efficient solution. Sparsity term of cost function allows trade-off between accuracy and testing time when needed. The efficiency of SDWEC was tested on 11 datasets and compared with the state-of-the art classifier ensemble methods. The results show that SDWEC provides better or similar accuracy levels using fewer classifiers and reduces testing time for ensemble.

A SVM based speech to text converter for Turkish language

In proposed speech to text conversion, a Support Vector Machines (SVM) based Turkish speech to text converter system has been developed. In the recognition system, Mel Frequency Cepstral Coefficients (MFCC) has been applied to extract features of Turkish speech and SVM based classifier has been used to classify the phonemes. The morphological structure of Turkish, a language based on phonemes, has been taken into consideration in the devoloped person-dependent voice recognition system. Unlike the multiclass classifiers which are used in the SVM-MFCC based voice recognition system, a new SVM classifier system has been developed that uses fewer classes in layers, increasing the number of multiclass layers. A new Text Comparison Algorithm is proposed, which also uses phoneme sequence to measure similarity in word similarity measurement. Along with these enhancements, as the training period becomes higher, performance of voice recognition is improved and word recognition performance is increased. The performance of the proposed structure is compared with similar systems.