Adaptive Path Planning for Mobile Robot Obstacle Avoidance

Generally speaking, the mobile robot is capable of sensing its surrounding environment, interpreting the sensed information to obtain the knowledge of its location and the environment, planning a real-time trajectory to reach the object. In this process, the issue of obstacle avoidance is a fundamental topic to be challenged. Thus, an adaptive path-planning control scheme is designed without detailed environmental information, large memory size and heavy computation burden in this study for the obstacle avoidance of a mobile robot. In this scheme, the robot can gradually approach its object according to the motion tracking mode, obstacle avoidance mode, self-rotation mode, and robot state selection. The effectiveness of the proposed adaptive path-planning control scheme is verified by numerical simulations of a differential-driving mobile robot under the possible occurrence of obstacle shapes.

Transformation of Vocal Characteristics: A Review of Literature

The transformation of vocal characteristics aims at modifying voice such that the intelligibility of aphonic voice is increased or the voice characteristics of a speaker (source speaker) to be perceived as if another speaker (target speaker) had uttered it. In this paper, the current state-of-the-art voice characteristics transformation methodology is reviewed. Special emphasis is placed on voice transformation methodology and issues for improving the transformed speech quality in intelligibility and naturalness are discussed. In particular, it is suggested to use the modulation theory of speech as a base for research on high quality voice transformation. This approach allows one to separate linguistic, expressive, organic and perspective information of speech, based on an analysis of how they are fused when speech is produced. Therefore, this theory provides the fundamentals not only for manipulating non-linguistic, extra-/paralinguistic and intra-linguistic variables for voice transformation, but also for paving the way for easily transposing the existing voice transformation methods to emotion-related voice quality transformation and speaking style transformation. From the perspectives of human speech production and perception, the popular voice transformation techniques are described and classified them based on the underlying principles either from the speech production or perception mechanisms or from both. In addition, the advantages and limitations of voice transformation techniques and the experimental manipulation of vocal cues are discussed through examples from past and present research. Finally, a conclusion and road map are pointed out for more natural voice transformation algorithms in the future.

Aerodynamics and Optimization of Airfoil Under Ground Effect

The Prediction of aerodynamic characteristics and shape optimization of airfoil under the ground effect have been carried out by integration of computational fluid dynamics and the multiobjective Pareto-based genetic algorithm. The main flow characteristics around an airfoil of WIG craft are lift force, lift-to-drag ratio and static height stability (H.S). However, they show a strong trade-off phenomenon so that it is not easy to satisfy the design requirements simultaneously. This difficulty can be resolved by the optimal design. The above mentioned three characteristics are chosen as the objective functions and NACA0015 airfoil is considered as a baseline model in the present study. The profile of airfoil is constructed by Bezier curves with fourteen control points and these control points are adopted as the design variables. For multi-objective optimization problems, the optimal solutions are not unique but a set of non-dominated optima and they are called Pareto frontiers or Pareto sets. As the results of optimization, forty numbers of non- dominated Pareto optima can be obtained at thirty evolutions.

A “Greedy“ Czech Manufacturing Case

The article describes a case study on one of Czech Republic-s manufacturing middle size enterprises (ME), where due to the European financial crisis, production lines had to be redesigned and optimized in order to minimize the total costs of the production of goods. It is considered an optimization problem of minimizing the total cost of the work load, according to the costs of the possible locations of the workplaces, with an application of the Greedy algorithm and a partial analogy to a Set Packing Problem. The displacement of working tables in a company should be as a one-toone monotone increasing function in order for the total costs of production of the goods to be at minimum. We use a heuristic approach with greedy algorithm for solving this linear optimization problem, regardless the possible greediness which may appear and we apply it in a Czech ME.

Creating Maintenance Cost Model for University Buildings

Maintenance costs incurred on building differs. The difference can be as results of the types, functions, age, building health index, size, form height, location and complexity of the building. These are contributing to the difficulty in maintenance development of deterministic maintenance cost model. This paper is concerns with reporting the preliminary findings on the creation of building maintenance cost distributions for universities in Malaysia. This study is triggered by the need to provide guides on maintenance costs distributions for decision making. For this purpose, a survey questionnaire was conducted to investigate the distribution of maintenance costs in the universities. Altogether, responses were received from twenty universities comprising both private and publicly owned. The research found that engineering services, roofing and finishes were the elements contributing the larger segment of the maintenance costs. Furthermore, the study indicates the significance of maintenance cost distribution as decision making tool towards maintenance management.

Value of Sharing: Viral Advertisement

Sharing motivations of viral advertisements by consumers and the impacts of these advertisements on the perceptions for brand will be questioned in this study. Three fundamental questions are answered in the study. These are advertisement watching and sharing motivations of individuals, criteria of liking viral advertisement and the impact of individual attitudes for viral advertisement on brand perception respectively. This study will be carried out via a viral advertisement which was practiced in Turkey. The data will be collected by survey method and the sample of the study consists of individuals who experienced the practice of sample advertisement. Data will be collected by online survey method and will be analyzed by using SPSS statistical package program. Recently traditional advertisement mind have been changing. New advertising approaches which have significant impacts on consumers have been argued. Viral advertising is a modernist advertisement mind which offers significant advantages to brands apart from traditional advertising channels such as television, radio and magazines. Viral advertising also known as Electronic Word-of- Mouth (eWOM) consists of free spread of convincing messages sent by brands among interpersonal communication. When compared to the traditional advertising, a more provocative thematic approach is argued. The foundation of this approach is to create advertisements that are worth sharing with others by consumers. When that fact is taken into consideration, in a manner of speaking it can also be stated that viral advertising is media engineering. The content worth sharing makes people being a volunteer spokesman of a brand and strengthens the emotional bonds among brand and consumer. Especially for some sectors in countries which are having traditional advertising channel limitations, viral advertising creates vital advantages.

Fuzzy Rules Generation and Extraction from Support Vector Machine Based on Kernel Function Firing Signals

Our study proposes an alternative method in building Fuzzy Rule-Based System (FRB) from Support Vector Machine (SVM). The first set of fuzzy IF-THEN rules is obtained through an equivalence of the SVM decision network and the zero-ordered Sugeno FRB type of the Adaptive Network Fuzzy Inference System (ANFIS). The second set of rules is generated by combining the first set based on strength of firing signals of support vectors using Gaussian kernel. The final set of rules is then obtained from the second set through input scatter partitioning. A distinctive advantage of our method is the guarantee that the number of final fuzzy IFTHEN rules is not more than the number of support vectors in the trained SVM. The final FRB system obtained is capable of performing classification with results comparable to its SVM counterpart, but it has an advantage over the black-boxed SVM in that it may reveal human comprehensible patterns.

A Flexible Flowshop Scheduling Problem with Machine Eligibility Constraint and Two Criteria Objective Function

This research deals with a flexible flowshop scheduling problem with arrival and delivery of jobs in groups and processing them individually. Due to the special characteristics of each job, only a subset of machines in each stage is eligible to process that job. The objective function deals with minimization of sum of the completion time of groups on one hand and minimization of sum of the differences between completion time of jobs and delivery time of the group containing that job (waiting period) on the other hand. The problem can be stated as FFc / rj , Mj / irreg which has many applications in production and service industries. A mathematical model is proposed, the problem is proved to be NPcomplete, and an effective heuristic method is presented to schedule the jobs efficiently. This algorithm can then be used within the body of any metaheuristic algorithm for solving the problem.

Idiopathic Constipation can be Subdivided in Clinical Subtypes: Data Mining by Cluster Analysis on a Population based Study

The prevalence of non organic constipation differs from country to country and the reliability of the estimate rates is uncertain. Moreover, the clinical relevance of subdividing the heterogeneous functional constipation disorders into pre-defined subgroups is largely unknown.. Aim: to estimate the prevalence of constipation in a population-based sample and determine whether clinical subgroups can be identified. An age and gender stratified sample population from 5 Italian cities was evaluated using a previously validated questionnaire. Data mining by cluster analysis was used to determine constipation subgroups. Results: 1,500 complete interviews were obtained from 2,083 contacted households (72%). Self-reported constipation correlated poorly with symptombased constipation found in 496 subjects (33.1%). Cluster analysis identified four constipation subgroups which correlated to subgroups identified according to pre-defined symptom criteria. Significant differences in socio-demographics and lifestyle were observed among subgroups.

A Critical Study of Media Profiling on Society-s Social Problems from a British Perspective

This article explores the sociological perspectives on social problems and the role of the media which has a delicate role to tread in balancing its duty to the public and the victim Whilst social problems have objective conditions, it is the subjective definition of such problems that ensure which social problem comes to the fore and which doesn-t. Further it explores the roles and functions of policymakers when addressing social problems and the impact of the inception of media profiling as well as the advantages and disadvantages of media profiling towards social problems. It focuses on the inception of media profiling due to its length and a follow up article will explore how current media profiling towards social problems have evolved since its inception.

Design of Thermal Control Subsystem for TUSAT Telecommunication Satellite

TUSAT is a prospective Turkish Communication Satellite designed for providing mainly data communication and broadcasting services through Ku-Band and C-Band channels. Thermal control is a vital issue in satellite design process. Therefore, all satellite subsystems and equipments should be maintained in the desired temperature range from launch to end of maneuvering life. The main function of the thermal control is to keep the equipments and the satellite structures in a given temperature range for various phases and operating modes of spacecraft during its lifetime. This paper describes the thermal control design which uses passive and active thermal control concepts. The active thermal control is based on heaters regulated by software via thermistors. Alternatively passive thermal control composes of heat pipes, multilayer insulation (MLI) blankets, radiators, paints and surface finishes maintaining temperature level of the overall carrier components within an acceptable value. Thermal control design is supported by thermal analysis using thermal mathematical models (TMM).

Heuristic Set-Covering-Based Postprocessing for Improving the Quine-McCluskey Method

Finding the minimal logical functions has important applications in the design of logical circuits. This task is solved by many different methods but, frequently, they are not suitable for a computer implementation. We briefly summarise the well-known Quine-McCluskey method, which gives a unique procedure of computing and thus can be simply implemented, but, even for simple examples, does not guarantee an optimal solution. Since the Petrick extension of the Quine-McCluskey method does not give a generally usable method for finding an optimum for logical functions with a high number of values, we focus on interpretation of the result of the Quine-McCluskey method and show that it represents a set covering problem that, unfortunately, is an NP-hard combinatorial problem. Therefore it must be solved by heuristic or approximation methods. We propose an approach based on genetic algorithms and show suitable parameter settings.

Stability Analysis of Impulsive Stochastic Fuzzy Cellular Neural Networks with Time-varying Delays and Reaction-diffusion Terms

In this paper, the problem of stability analysis for a class of impulsive stochastic fuzzy neural networks with timevarying delays and reaction-diffusion is considered. By utilizing suitable Lyapunov-Krasovskii funcational, the inequality technique and stochastic analysis technique, some sufficient conditions ensuring global exponential stability of equilibrium point for impulsive stochastic fuzzy cellular neural networks with time-varying delays and diffusion are obtained. In particular, the estimate of the exponential convergence rate is also provided, which depends on system parameters, diffusion effect and impulsive disturbed intention. It is believed that these results are significant and useful for the design and applications of fuzzy neural networks. An example is given to show the effectiveness of the obtained results.

Salicylhydroxamic Acid Inhibits the Growth of Candida albicans

Candida spp. are common and aggressive pathogens. Because of the growing resistance of Candida spp. to current antifungals, novel targets, found in Candida spp. but not in humans or other flora, have to be identified. The alternative oxidase (AOX) is one such possibility. This enzyme is insensitive to cyanide, but is sensitive to compounds such as salicylhydroxamic acid (SHAM), disulfiram and n-alkyl gallates. The growth Candida albicans was inhibited by SHAM (Ki = 9-15 mM) and cyanide (Ki = 2-4 mM), albeit to differing extents. The rate of O2 uptake was inhibited by less than 10% by 25 mM SHAM and by about 90% by 250 μM KCN. Although SHAM substantially inhibited the growth of C. albicans, it is unlikely that the inhibition of AOX was the cause. Salicylhydroxamic acid is used therapeutically in the treatment of urinary tract infections and urolithiasis, but it also has some potential in the treatment of C. albicans infection.

Sustainable Design of Impinging Premixed Slot Jets

Cooktop burners are widely used nowadays. In cooktop burner design, nozzle efficiency and greenhouse gas(GHG) emissions mainly depend on heat transfer from the premixed flame to the impinging surface. This is a complicated issue depending on the individual and combined effects of various input combustion variables. Optimal operating conditions for sustainable burner design were rarely addressed, especially in the case of multiple slot-jet burners. Through evaluating the optimal combination of combustion conditions for a premixed slot-jet array, this paper develops a practical approach for the sustainable design of gas cooktop burners. Efficiency, CO and NOx emissions in respect of an array of slot jets using premixed flames were analysed. Response surface experimental design were applied to three controllable factors of the combustion process, viz. Reynolds number, equivalence ratio and jet-to-vessel distance. Desirability Function Approach(DFA) is the analytic technique used for the simultaneous optimization of the efficiency and emission responses.

A Practical Method for Load Balancing in the LV Distribution Networks Case Study: Tabriz Electrical Network

In this paper, a new efficient method for load balancing in low voltage distribution systems is presented. The proposed method introduces an improved Leap-frog method for optimization. The proposed objective function includes the difference between three phase currents, as well as two other terms to provide the integer property of the variables; where the latter are the status of the connection of loads to different phases. Afterwards, a new algorithm is supplemented to undertake the integer values for the load connection status. Finally, the method is applied to different parts of Tabriz low voltage network, where the results have shown the good performance of the proposed method.

A Comparison between Heuristic and Meta-Heuristic Methods for Solving the Multiple Traveling Salesman Problem

The multiple traveling salesman problem (mTSP) can be used to model many practical problems. The mTSP is more complicated than the traveling salesman problem (TSP) because it requires determining which cities to assign to each salesman, as well as the optimal ordering of the cities within each salesman's tour. Previous studies proposed that Genetic Algorithm (GA), Integer Programming (IP) and several neural network (NN) approaches could be used to solve mTSP. This paper compared the results for mTSP, solved with Genetic Algorithm (GA) and Nearest Neighbor Algorithm (NNA). The number of cities is clustered into a few groups using k-means clustering technique. The number of groups depends on the number of salesman. Then, each group is solved with NNA and GA as an independent TSP. It is found that k-means clustering and NNA are superior to GA in terms of performance (evaluated by fitness function) and computing time.

Polyethylenimine Coated Carbon Nanotube for Detecting Rancidity in Frying Oil

Chemical detection is still a continuous challenge when it comes to designing single-walled carbon nanotube (SWCNT) sensors with high selectivity, especially in complex chemical environments. A perfect example of such an environment would be in thermally oxidized soybean oil. At elevated temperatures, oil oxidizes through a series of chemical reactions which results in the formation of monoacylglycerols, diacylglycerols, oxidized triacylglycerols, dimers, trimers, polymers, free fatty acids, ketones, aldehydes, alcohols, esters, and other minor products. In order to detect the rancidity of oxidized soybean oil, carbon nanotube chemiresistor sensors have been coated with polyethylenimine (PEI) to enhance the sensitivity and selectivity. PEI functionalized SWCNTs are known to have a high selectivity towards strong electron withdrawing molecules. The sensors were very responsive to different oil oxidation levels and furthermore, displayed a rapid recovery in ambient air without the need of heating or UV exposure.

Economic Load Dispatch with Daily Load Patterns and Generator Constraints by Particle Swarm Optimization

This paper presents an optimization technique to economic load dispatch (ELD) problems with considering the daily load patterns and generator constraints using a particle swarm optimization (PSO). The objective is to minimize the fuel cost. The optimization problem is subject to system constraints consisting of power balance and generation output of each units. The application of a constriction factor into PSO is a useful strategy to ensure convergence of the particle swarm algorithm. The proposed method is able to determine, the output power generation for all of the power generation units, so that the total constraint cost function is minimized. The performance of the developed methodology is demonstrated by case studies in test system of fifteen-generation units. The results show that the proposed algorithm scan give the minimum total cost of generation while satisfying all the constraints and benefiting greatly from saving in power loss reduction

Model Predictive Control of Gantry Crane with Input Nonlinearity Compensation

This paper proposed a nonlinear model predictive control (MPC) method for the control of gantry crane. One of the main motivations to apply MPC to control gantry crane is based on its ability to handle control constraints for multivariable systems. A pre-compensator is constructed to compensate the input nonlinearity (nonsymmetric dead zone with saturation) by using its inverse function. By well tuning the weighting function matrices, the control system can properly compromise the control between crane position and swing angle. The proposed control algorithm was implemented for the control of gantry crane system in System Control Lab of University of Technology, Sydney (UTS), and achieved desired experimental results.