Soft Computation Research Articles

A Novel Algorithmic Cost Estimation Model Based on Soft Computing Technique

Problem statement: Software development effort estimation is the process of predicting the most realistic use of effort required for developing software based on some parameters. It has always characterized one of the biggest challenges in Computer Science for the last decades. Because time and cost estimate at the early stages of the software development are the most difficult to obtain and they are often the least accurate. Traditional algorithmic techniques such as regression models, Software Life Cycle Management (SLIM), COCOMO II model and function points, require an estimation process in a long term. But, nowadays that is not acceptable for software developers and companies. Newer soft computing techniques to effort estimation based on non-algorithmic techniques such as Fuzzy Logic (FL) may offer an alternative for solving the problem. This work aims to propose a new fuzzy logic realistic model to achieve more accuracy in software effort estimation. The main objective of this research was to investigate the role of fuzzy logic technique in improving the effort estimation accuracy by characterizing inputs parameters using two-side Gaussian function which gave superior transition from one interval to another. Approach: The methodology adopted in this study was use of fuzzy logic approach rather than classical intervals in the COCOMO II. Using advantages of fuzzy logic such as fuzzy sets, inputs parameters can be specified by distribution of its possible values and these fuzzy sets were represented by membership functions. In this study to get a smoother transition in the membership function for input parameters, its associated linguistic values were represented by two-side Gaussian Membership Functions (2-D GMF) and rules. Results: After analyzing the results attained by means of applying COCOMO II and proposed model based on fuzzy logic to the NASA dataset and created an artificial dataset, it had been found that proposed model was performing better than ordinal COCOMO II and the achieved results were closer to the actual effort. The relative error for proposed model using two-side Gaussian membership functions is lower than that of the error obtained using ordinal COCOMO II. Conclusion: Based on the achieved results, it was concluded that, using soft computation approaches such as fuzzy logic and their advantages, good predication; adaption; understandability and the accuracy of software effort estimation can be improved and the estimation can be very close to the actual effort. This novelty model will lead researchers to focus on benefits of non-algorithmic models to overcome the estimation problems.

Supersizing the mind: embodiment, action, and cognitive extension

Forward: By David Chalmers / Acknowledgements / Introduction: BRAINBOUND versus EXTENDED / I: From Embodiment to Cognitive Extension - 1. The Active Body: 1.1 A Walk on the Wild Side 1.2 Inhabited Interaction 1.3 Active Sensing 1.4 Distributed Functional Decomposition 1.5 Sensing for Coupling 1.6 Information Self-Structuring 1.7 Perception, Qualia, and Sensorimotor Expectations 1.8 Time and Mind 1.9 Dynamics and (Soft) Computation. 1.10 Out from the Bedrock 2. The Negotiable Body: 2.1 Where the Rubber Meets the Road 2.2 What's in an Interface? 2.3 New Systemic Wholes 2.4 Substitutes 2.5 Incorporation Vs Use 2.6 Towards Cognitive Extension 2.7 Three Grades of Embodiment 3. Material Symbols: 3.1 Language as Scaffolding 3.2 Augmenting Reality 3.3 Sculpting Attention 3.4 Hybrid Thoughts? 3.5 From Translation to Coordination 3.6 Second-order Cognitive Dynamics 3.7 Self-made Minds. 4. World, Incorporated: 4.1 Cognitive Niche Construction: A Primer 4.2 Cognition in the Globe: A Cameo 4.3 Thinking Space 4.4 Epistemic Engineers 4.5 Exploitative Representation and Wide Computation 4.6 Tetris: The Update 4.7 The Swirl of Organization 4.8 Extending the Mind 4.9 BRAINBOUND versus EXTENDED: The Case So Far. II. Boundary Disputes - 5. Mind Re-bound?: 5.1 EXTENDED Anxiety 5.2 Pencil Me In 5.3 The Odd Coupling 5.4 Cognitive Candidacy 5.5 The Mark of the Cognitive? 5.6 Kinds and Minds 5.7 Perception and Development 5.8 Deception and Contested Space 5.9 Folk Intuition and Cognitive Extension 5.10 Asymmetry and Lopsideness 5.11 Similarity vs Complementarity 5.12 Hippo-World 6. The Cure for Cognitive Hiccups (HEMC, HEC, HEMC): 6.1 Rupert's Challenge 6.2 HEC versus HEMC 6.3 Parity and Cognitive Kinds (Again) 6.4 The Persisting Core 6.5 Cognitive Impartiality 6.6 A Brain Teaser 6.7 Thoughtful Gestures 6.8 Material Carriers 6.9 Loops as Mechanisms 6.10 Anarchic Self-Stimulation 6.11Autonomous Coupling 6.12 Why the HEC? 6.13 The Cure 7. Rediscovering the Brain: 7.1 Matter into Mind 7.2.Honey, I Shrunk the Representations 7.3 Change Spotting: The Sequel 7.4 Thinking about Thinking: The Brain's Eye View.: 7.5 Born-Again Cartesians? 7.6 Surrogate Situations 7.7 Plug Points 7.8 Brain Control 7.9 Asymmetry Arguments 7.10 Extended in a Vat 7.11 The (Situated) Cognizer's Innards III: The Limits of Embodiment - 8. Painting, Planning, and Perceiving: 8.1 Enacting Perceptual Experience 8.2 The Painter and the Perceiver 8.3 Three Virtues of the Strong Sensorimotor Model 8.4 A Vice: Sensorimotor (Hyper) Sensitivity 8.5 What Reaching Teaches 8.6 (Tweaked)Tele-Assistance 8.7 Sensorimotor Summarizing 8.8 Virtual Content, Again 8.9 Beyond the Sensorimotor Frontier 9. Disentangling Embodiment: 9.1 Three Threads 9.2 The Separability Thesis 9.3 Beyond Flesh-eating Functionalism. 9.4 Ada, Adder, and Odder 9.5 A Tension Revealed 9.6 What Bodies Are 9.7 Participant Machinery and Morphological Computation 9.8 Quantifying Embodiment 9.9 The Heideggerian Theatre / 10. Conclusions: Mindsized Bites / Appendix: The Extended Mind (Andy Clark and David Chalmers)

Research on Tasks Scheduling Algorithms for Dynamic and Uncertain Computing Grid Based on a+bi Connection Number of SPA

Task scheduling algorithms are key techniques in task management system of computing grid. Because of the uncertainty nature of a grid, traditional task scheduling algorithms do not work well in an open, heterogeneous and dynamic grid environment of real world. In this paper, Set Pair Analysis (SPA), a new soft computation method is used to process the synthetic uncertainty in the task scheduling of a computing grid. After introducing SPA and its application, the paper goes on to introduce the definition of connection number to express the uncertain Expected Time to Compute of tasks, analysis operation properties and linear order relation suitable for computing grid scheduling. Three online uncertain dynamic scheduling algorithms, OUD_OLB, OUD_MET, OUD_MCT, and three batch uncertain dynamic scheduling algorithms BUD_Min-min, BUD_Min-max, BUD_Surferage, are presented for the uncertain dynamic computing grid. Theoretical analysis and experimental results illustrate that these algorithms are capable of representing the dynamics and uncertainty in a computing grid environment. These algorithms are the generalization of traditional grid scheduling algorithms, and they possess high value in theory and application in a grid environment. Certainly it will be a new method to design tasks scheduling algorithm in uncertain computing grid environment.

Application of artificial neural networks, fuzzy logic and wavelet transform in fault diagnosis via vibration signal analysis: A review

Fault diagnosis through vibration analysis is a method widely used by maintenance engineers for the condition monitoring of rotating machinery. Conventional vibration signature analysis techniques have been used, and after the invention of soft computation, techniques such as artificial neural network (ANN), fuzzy logic, wavelet transform, genetic algorithms, are broadly used by several researchers. These techniques show tremendous scope in the field of fault diagnosis through vibration analysis. The objective of the present paper is to provide a brief review of recent developments in the area of applications of ANN, fuzzy logic and wavelet transform in fault diagnosis. Special attention is given to the rolling element bearings fault diagnosis through vibration analysis, due to the fact that bearings are among the most important and frequently encountered components in rotating machinery.

Estimating the depth of anesthesia using fuzzy soft computation applied to EEG features

Estimating the depth of anesthesia (DOA) is still a challenging area in anesthesia research. The objective of this study was to design a fuzzy rule based system which integrates electroencephalogram (EEG) features to quantitatively estimate the DOA. The proposed method is based on the analysis of single-channel EEG using frequency and time domain methods. A clinical study was conducted on 22 patients to construct subsets of reference data corresponding to four well-defined anesthetic states: awake, moderate anesthesia, surgical anesthesia and isoelectric. Statistical analysis of features was used to design input membership functions (MFs). The input space was partitioned with respect to the derived MFs and the training data was used to label the partitions and extract efficient fuzzy if-then rules. Consequently, the fuzzy rule-base index (FRI) is derived between 0 (isoelectric) to 100 (fully awake) using fuzzy inference engine and designed output MFs. We also applied the same features to an adaptive network-based fuzzy inference system (ANFIS) derived without any prior knowledge. The results show that FRI correlates more with the clinically accepted DOA index, CSI™ (CSM, Danmeter, Denmark). In addition to this achievement the main idea behind this study is to simplify the mutual knowledge exchange between the human expert and the machine, leading to enhance both interpretability of the results and performance of the system.

Using Fuzzy Controller in Induction Motor Speed Control with Constant Flux

Abstract — Variable speed drives are growing and varying. Drives expanse depend on progress in different part of science like power system, microelectronic, control methods, and so on. Artificial intelligent contains hard computation and soft computation. Artificial intelligent has found high application in most nonlinear systems same as motors drive. Because it has intelligence like human but there are no sentimental against human like angriness and.... Artificial intelligent is used for various points like approximation, control, and monitoring. Because artificial intelligent techniques can use as controller for any system without requirement to system mathematical model, it has been used in electrical drive control. With this manner, efficiency and reliability of drives increase and volume, weight and cost of them decrease. Keywords — Artificial intelligent, electrical motor, intelligent drive and control, I. I NTRODUCTION ECAUSE induction motors require low maintenance and are robust [8], have many applications in industry [9]. Along with industry progress, it indicates requirement to progresses drive with high performance [8]. DC motors are control abler than AC motors but they require much cost. In addition, in equal power, DC motors have higher volume and weight. Main variations in semiconductors, converters topology, analyze technique and simulation of electrical machines drive and newer control technique have had role in this progress [1]. Usually classical control is used in motors drive [8,10,11]. Design and implementation of Conventional controls have difficulties that nameable: a) It is basis on mathematical accurate model of system that usual it is not known [12,13]. b) Drives are nonlinear systems and Classical control performance with this system decrease [9,13]. c) Variation of machine parameters (especially in vector control [12]) by load disturbance [9], motor saturation [9,13] or thermal variations [13] do not cause expectation performance [9,13]. d) Classical linear control shows high performance only for one unique act point [13]. e) With choose improperly coefficient, classical control cannot receive acceptable result and suitable choose for constant coefficient in especial application condition with set point varying, necessarily is not optimum [14].

A Systems‐Based Approach for Supporting Discourse in Decision Making

Abstract: A systemic methodology has been applied to improve decision making with uncertainty. Process modeling has been used to provide the means for gaining a holistic view of the system, and to allow for the complexity to be managed through a hierarchically structured set of subsystems. The success of each process depends significantly on its ability to address issues that arise, which could be the subject of discourse and debate among participants. This debate and discussion has been modeled using the Issue‐Based Information System framework for argumentation. The uncertain propositions are combined using an extended aggregation rule in evidence theory. The resulting soft computation framework allows the evidence and arguments to be assembled and balanced. The overall framework is implemented in a software tool and applied to a case of climate change impacts on infrastructures to demonstrate how the favorability of each potential adaptation solution can be assessed with respect to system performance requirements.

Personal technologies: memory and intimacy through physical computing

In this paper, I present an overview of personal and intimate technologies within a pedagogical context. I describe two courses that I have developed for Computation Arts at Concordia University: “Tangible Media and Physical Computing” and “Second Skin and Soft Wear.” Each course deals with different aspects of physical computing and tangible media in a Fine Arts context. In both courses, I introduce concepts of soft computation and intimate reactive artifacts as artworks. I emphasize the concept of memory (contrasting computer memory and personal, interpretive memory), and explore how responsive or interactive objects can create a new medium for annotating architectural space and objects, for leaving traces of presence, and for recording personal histories. At the core of this pedagogical practice is a strong emphasis on engaging a vulnerable, personal approach to working with electronics and physical computation. To contextualize the teaching practice, I begin by presenting some of my own research projects developed at Extra Soft Labs (also known as XS Labs), then segue into a detailed discussion of these two classes, and conclude with a discussion of some student work.

Electronic Textiles: Wearable Computers, Reactive Fashion, and Soft Computation

Electronic textiles, also referred to as smart fabrics, are quite fashionable right now. Their close relationship with the field of computer wearables gives us many diverging research directions and possible definitions. On one end of the spectrum, there are pragmatic applications such as military research into interactive camouflage or textiles that can heal wounded soldiers. On the other end of the spectrum, work is being done by artists and designers in the area of reactive clothes: “second skins” that can adapt to the environment and to the individual. Fashion, health, and telecommunication industries are also pursuing the vision of clothing that can express aspects of people's personalities, needs, and desires or augment social dynamics through the use and display of aggregate social information.In my current production-based research, I develop enabling technology for electronic textiles based upon my theoretical evaluation of the historical and cultural modalities of textiles as they relate to future computational forms. My work involves the use of conductive yarns and fibers for power delivery, communication, and networking, as well as new materials for display that use electronic ink, nitinol, and thermochromic pigments. The textiles are created using traditional textile manufacturing techniques: spinning conductive yarns, weaving, knitting, embroidering, sewing, and printing with inks.

Distributed agent paradigm for soft and hard computation

This paper describes a multiagent-based simulation paradigm, for hybrid (soft and hard) simulation of complex dynamical systems. The computations are interpreted as the outcome arising out of deterministic, nondeterministic or stochastic interaction among the agents, which includes the environment. These interactions are like chemical reactions and the evolution of the multiset of agents can mimic the evolution of the complex system, e.g. genetic, nature inspired self-organized criticality and active walker (swarm and ant intelligence) models. Since the reaction rules are inherently parallel, any number of actions can be performed cooperatively or competitively among the subsets of the agents, so that the system evolve reaches an equilibrium (or a chaotic or an emergent) state. Practical realisation of this paradigm is achieved through a coordination programming language using Multiagent and transactions.

On Optimal Forecasting with Soft Computation for Nonlinear Time Series

In this paper we present a new approach on optimal forecasting by using the fuzzy set theory and soft computing methods for the dynamic data analysis. This research is based on the concepts of fuzzy membership function as well as the natural selection of evolution theory. Some discussions in the sensitivity of the design of fuzzy processing will be provided. Through the design of genetic evolution, the AIC criteria is used as the adjust function, and the fuzzy memberships function of each gene model are calculated. Simulation and empirical examples show that our proposed forecasting technique can give an optimal forecasting in time series analysis.

Ordinal Optimization with Subset Selection Rule

Ordinal optimization (OO) has enjoyed a great degree of success in addressing stochastic optimization problems characterized by an independent and identically distributed (i.i.d.) noise. The methodology offers a statistically quantifiable avenue to find good enough solutions by means of soft computation. In this paper, we extend the OO methodology to a more general class of stochastic problems by relaxing the i.i.d, assumption on the underlying noise. Theoretical results and their applications to simple examples are presented.

Tool Condition Monitoring in Machining by Fuzzy Neural Networks

The Multiple Principal Component (MPC) Fuzzy Neural Network for tool condition monitoring in machining under varying cutting conditions is proposed. This approach is based on three major components of “soft computation,” namely fuzzy logic, neural network, and probability reasoning. The MPC classification fuzzy neural networks were built through training with learning data obtained from cutting tests performed in a reasonable range of cutting conditions. Several sensors were used for monitoring feature selection. Force, vibration, and spindle motor power signals were fused in multiple principal component directions to give a highly sensitive feature space. The tool conditions considered in the monitoring tests included sharp tool, tool breakage, slight wear, medium wear, and severe wear. The results showed success rates of approximate 94 percent in self-classification tests (i.e., the same data samples were used for both learning and classification), 84 percent in tests performed using different records for classification than those used for learning under the same cutting conditions, and about 80 percent in tests performed using samples obtained at different cutting conditions for classification than those used for learning within the same range of cutting conditions. The MPC fuzzy neural network classification strategy performed better than back-propagation trained feed-forward neural networks in these tests.

FUZZY CLUSTERING FOR AUTOMATED TOOL CONDITION MONITORING IN MACHINING

A strategy for unsupervised classification for automated tool condition monitoring by using fuzzy neural networks is proposed. This approach is based on a newly developed classification algorithm by the authors, namely the multiple principal component fuzzy neural network for tool condition monitoring. This algorithm uses three major components of soft computation; fuzzy logic, neural network, and probability reasoning. The proposed network functioned successfully in clustering the data obtained from cutting tests performed within a reasonable range of cutting conditions. Experiments in turning were implemented to test the performance of the proposed algorithm. Several sensors were used for monitoring feature selection. The results showed approximately 80 to 94% success rates in these tests.

Effect of Soft Forces and Computation of Thermal Properties of Liquid Metals

Abstract Effect of the soft force component to the entropy of the system is discussed in the light of thermodynamic perturbation theory and an explicit expression is obtained as a function of the ingredients of the hardcore reference system. Same basic ingredients can thus produce the structural and thermal properties reasonably not only near the melting point but also at thermodynamic states considerably away from the melting point.