Analysis and modeling of ants’ behavior from single to multi-body

In order to shed light on the collective behavior of social insects, we analyzed the behavior of ants from single to multi-body. In an experimental set-up, ants are placed in hemisphere without a nest and food. Trajectory of ants is recorded. From this bottom-up approach, we found that collective behavior of ants as follows: 1. Activity of single ant increases and decreases periodically. 2. Spontaneous meeting process is observed between two ants and meeting spot of two ants is localized in hemisphere. 3. Result on division of labor is obtained between two ants.

Analysis and modeling of Diacamma workers’ behavior

It is well-known that social insects such as ants show interesting collective behaviors. How do they organize such behaviors? To expand understanding of collective behaviors of social insects, we focused on ants, Diacamma, and analyzed the behavior of a few individuals. In an experimental set-up, ants are placed in hemisphere without a nest and food and the trajectory of ants is recorded. From this bottom-up approach, we found following characteristics: 1. Activity of individuals increases and decreases periodically. 2. Spontaneous meeting process is observed between two ants and meeting spot of two ants is localized in the experimental field.

How could construction supply chain benefit from RFID/GPS integration: a knowledge management perspective

Construction materials and equipment are essential building blocks of every construction project and may account for 50-60 per cent of the total cost of construction. The rate of their utilization, on the other hand, is the element that most directly relates to a project progress. A growing concern in the industry that inadequate efficiency hinders its success could thus be accommodated by turning construction into a logistic process. Although mostly limited, recent attempts and studies show that Radio Frequency IDentification (RFID) applications have significant potentials in construction. However, the aim of this research is to show that the technology itself should not only be used for automation and tracking to overcome the supply chain complexity but also as a tool to generate, record and exchange process-related knowledge among the supply chain stakeholders. This would enable all involved parties to identify and understand consequences of any forthcoming difficulties and react accordingly before they cause major disruptions in the construction process. In order to achieve this aim the study focuses on a number of methods. First of all it develops a generic understanding of how RFID technology has been used in logistic processes in industrial supply chain management. Secondly, it investigates recent applications of RFID as an information and communication technology support facility in construction logistics for the management of construction supply chain. Based on these the study develops an improved concept of a construction logistics architecture that explicitly relies on integrating RFID with the Global Positioning System (GPS). The developed conceptual model architecture shows that categorisation provided through RFID and traceability as a result of RFID/GPS integration could be used as a tool to identify, record and share potential problems and thus vastly improve knowledge management processes within the entire supply chain. The findings thus clearly show a need for future research in this area.

A minimum approximate-BER beamforming approach for PSK modulated wireless systems

A beamforming algorithm is introduced based on the general objective function that approximates the bit error rate for the wireless systems with binary phase shift keying and quadrature phase shift keying modulation schemes. The proposed minimum approximate bit error rate (ABER) beamforming approach does not rely on the Gaussian assumption of the channel noise. Therefore, this approach is also applicable when the channel noise is non-Gaussian. The simulation results show that the proposed minimum ABER solution improves the standard minimum mean squares error beamforming solution, in terms of a smaller achievable system's bit error rate.

Engaging robots: innovative outreach for attracting cybernetics students

Cybernetics is a broad subject, encompassing many aspects of electrical, electronic, and computer engineering, which suffers from a lack of understanding on the part of potential applicants and teachers when recruiting students. However, once the engineering values, fascinating science, and pathways to rewarding, diverse careers are communicated, appropriate students can be very interested in enrolling. At the University of Reading, Reading, U.K., a key route for outreach to prospective students has been achieved through the use of robots in interactive talks at schools, competitions (often funded by Public Understanding of Science projects), a collectable fortnightly magazine, exhibitions in museums, open days at the University, and appearances in the media. This paper identifies the interactive engagement, anthropomorphic acceptability, and inspirational nature of robots as being key to their successful use in outreach activities. The statistical results presented show that the continued popularity of degrees at Reading in cybernetics, electronic engineering, and robotics over the last 20 years is in part due to the outreach activities to schools and the general public.

Cultured neural networks

This article looks at the use of cultured neural networks as the decision-making mechanism of a control system. In this case biological neurons are grown and trained to act as an artificial intelligence engine. Such research has immediate medical implications as well as enormous potential in computing and robotics. An experimental system involving closed-loop control of a mobile robot by a culture of neurons has been successfully created and is described here. This article gives a brief overview of the problem area and ongoing research. Questions are asked as to where this will lead in the future.

Self-organising neural networks for adaptive control

Self-organizing neural networks have been implemented in a wide range of application areas such as speech processing, image processing, optimization and robotics. Recent variations to the basic model proposed by the authors enable it to order state space using a subset of the input vector and to apply a local adaptation procedure that does not rely on a predefined test duration limit. Both these variations have been incorporated into a new feature map architecture that forms an integral part of an Hybrid Learning System (HLS) based on a genetic-based classifier system. Problems are represented within HLS as objects characterized by environmental features. Objects controlled by the system have preset targets set against a subset of their features. The system's objective is to achieve these targets by evolving a behavioural repertoire that efficiently explores and exploits the problem environment. Feature maps encode two types of knowledge within HLS — long-term memory traces of useful regularities within the environment and the classifier performance data calibrated against an object's feature states and targets. Self-organization of these networks constitutes non-genetic-based (experience-driven) learning within HLS. This paper presents a description of the HLS architecture and an analysis of the modified feature map implementing associative memory. Initial results are presented that demonstrate the behaviour of the system on a simple control task.

The control of dynamical systems by neural networks

In this paper the use of neural networks for the control of dynamical systems is considered. Both identification and feedback control aspects are discussed as well as the types of system for which neural networks can provide a useful technique. Multi-layer Perceptron and Radial Basis function neural network types are looked at, with an emphasis on the latter. It is shown how basis function centre selection is a critical part of the implementation process and that multivariate clustering algorithms can be an extremely useful tool for finding centres.

Neural network based control in food processing

A number of commonly encountered simple neural network types are discussed, with particular attention being paid to their applicability in automation and control when applied to food processing. In the first instance n-tuple networks are considered, these being particularly useful for high speed production checking operations. Subsequently backpropagation networks are discussed, these being useful both in a more familiar feedback control arrangement and also for such things as recipe prediction.

Roll of the rhythmic component in the proactive control of a human hand

In terms of evolution, the strategy of catching prey would have been an important part of survival in a constantly changing environment. A prediction mechanism would have developed to compensate for any delay in the sensory-motor system. In a previous study, “proactive control” was found, in which the motion of the hands preceded the virtual moving target. These results implied that the positive phase shift of the hand motion represents the proactive nature of the visual-motor control system, which attempts to minimize the brief error in the hand motion when the target changes position unexpectedly. In our study, a visual target moves in circle (13 cm diameter) on a computer screen, and each subject is asked to keep track of the target’s motion by the motion of a cursor. As the frequency of the target increases, a rhythmic component was found in the velocity of the cursor in spite of the fact that the velocity of the target was constant. The generation of a rhythmic component cannot be explained simply as a feedback mechanism for the phase shifts of the target and cursor in a sensory-motor system. Therefore, it implies that the rhythmic component was generated to predict the velocity of the target, which is a feed-forward mechanism in the sensory-motor system. Here, we discuss the generation of the rhythmic component and its roll in the feed-forward mechanism.

Modeling the patrol behavior of the Diacamma's gamergate

The gamergate (generally called the “queen”) of the Diacamma sp. walks around in the nest and comes into contact with the workers. The gamergate informs the workers of its presence by physical contact. This behavior is called a “patrol.” In previous work, it was reported that the gamergate controls its patrolling time depending on the colony size. How does the gamergate know the colony size, and how does it control the patrolling time? In this article, we propose a simple dynamics to explain this behavior. We assume that the gamergate and the workers have internal states which interact by physical contacts. By numerical simulations, we confirm that the patrol time of the proposed model depends on the size of the colony.