Gordon K. Lee

An Intelligent Home Energy Management System to Improve Demand Response

Demand Response (DR) and Time-of-Use (TOU) pricing refer to programs which offer incentives to customers who curtail their energy use during times of peak demand. In this paper, we propose an integrated solution to predict and re-engineer the electricity demand (e.g., peak load reduction and shift) in a locality at a given day/time. The system presented in this paper expands DR to residential loads by dynamically scheduling and controlling appliances in each dwelling unit. A decision-support system is developed to forecast electricity demand in the home and enable the user to save energy by recommending optimal run time schedules for appliances, given user constraints and TOU pricing from the utility company. The schedule is communicated to the smart appliances over a self-organizing home energy network and executed by the appliance control interfaces developed in this study. A predictor is developed to predict, based on the users life style and other social/environmental factors, the potential schedules for appliance run times. An aggregator is used to accumulate predicted demand from residential customers.

Optimal time-of-use pricing for residential load control

Demand response (DR) can be defined as change in electric usage by end-use customers from their normal consumption patterns in response to change in the price of electricity over time. Demand Response also refers to incentive payments designed to induce lower electricity use at times of high wholesale market prices. Time-of-use (TOU) power pricing has been shown to have a significant influence on ensuring a stable and optimal operation of a power system. This paper presents a novel algorithm for finding an optimum time-of-use electricity pricing in monopoly utility markets; definitions and the relations between supply and demand as well as different cost components are also presented. Further, the optimal pricing strategy is developed to maximize the benefit of society while implementing a demand response strategy. Finally, the effect of demand response in electricity prices is demonstrated using a simulated case study.

Distributed localization of wireless sensor networks using self-organizing maps

As larger sets of wireless sensor networks are being deployed, an important characteristic of the network which could enhance its capabilities is position awareness. While several approaches have been proposed for localization, that is, position awareness without using GPS, most techniques are either centralized or rely on anchor nodes. In this paper, a decentralized localization method is developed, based upon self-organizing maps. The algorithm is implemented for different size networks and the simulation results show the algorithm is efficient when compared to single processor or centralized localization methods; further the approach does not require anchor nodes. An error analysis shows that the proposed approach is a feasible method for computing the localization of sensor networks using a distributed architecture.

Modeling human cognition using a transformational knowledge architecture

While much research has been devoted to learning and machine intelligence, the field is still in its infancy. In particular, a technology that will allow for heuristic exploitation of information domain regularities to reduce the time required for knowledge acquisition while concomitantly resulting in an increase in the reliability of the acquired knowledge is still lacking. Unfortunately, contemporary learning mechanisms such as neural network architectures are inherently incapable of such performance. The objective of this paper is to present a new way of looking at learning and machine intelligence which has applicability in many fields such as in robotics, intelligent agents, data fusion, and cooperative sensing. In particular, we propose to construct a new architecture, that is, a transformational architecture for learning, intelligent fusion and transference of knowledge. A System of Systems (SoS) approach is used to realize machine intelligence. Random differences are learned by the system, generalized, and made available for subsequent replay in design transformations. Cross-domain symmetries can play a major role in design generation in particular and in the design of SoSs in general. The fundamental theory of randomization is the science, which underpins the practice. This strategy is employed in the design of the Knowledge Amplification by Structural Expert Randomization or KASER system.

On the inherent necessity of heuristic proofs

It follows from the nonreducibility of the theorization problem that an arbitrary proof cannot be valid on an absolute scale. Thus, in order for an arbitrary proof to be generative, it must be self-referential; but then, it must also be heuristic if not incomplete as a consequence. By relaxing the validity requirement, heuristic (i.e., relative) proof techniques are enabled. We show that heuristics are search randomizations in space-time. It is shown how one can develop heuristics, which are randomizations of knowledge. Even more intriguing, it is shown that heuristic proof is to formal proof what fuzzy logic is to formal logic. Simply put, the paper argues for the need to relax the notion of formal proof if AI is to advance.

Forming Shaped/Molded Structures by Integrating Meltblowing and Robotic Technologies

A novel system is described that forms three-dimensional (3D) molded nonwoven structures through proper integration of a laboratory scale meltblown unit with a small die and a six-axis robot. The 3D fiberweb structures can be formed by deposition of fibers from the die of the meltblown unit, which is manipulated by the robot, on any desired 3D mold. The mold rotational and surface speeds can be controlled by an additional external axis. The die is connected by two flexible hoses to the melt extruder of the meltblown unit and a hot air supply system. This system directly sprays fibers onto a 3D mannequin mold to produce structures from polypropylene polymers. With varying degrees of success. several robot manipulation algorithms of fiber deposition on the mold are developed to accurately control the basis weight uniformity the fiberwebs. A rule-based control algorithm using a linear variable differential transducer to map the mold contour results in the greatest fiberweb basis weight uniformity.

A smart transmission scheme for emergency data from a network of bio-sensors on the human body

As a nation of an estimated 45 million uninsured and underinsured Americans (almost 15% of the population), out of which over 11 million suffer from chronic diseases who require constant medical supervision, America today is plagued by the national crisis of inadequate and expensive healthcare. This paper introduces an architecture of a multi-tier telemedicine system comprised of strategically placed bio-sensors on a human body capable of collecting vital medical statistics (such as heart rate and blood pressure) and transmitting them (wired or wirelessly)over multiple hops to a remote medical server at a caregivers location thereby taking telemedicine from the desktop to roaming. However, fundamental wireless networking issues must be addressed and resolved before this dream can be realized. In this regards, this paper proposes a Medium Access Control (MAC) protocol specifically designed for a Wireless Body Area Network (WBAN). Our protocol is designed to cater to the Quality of Service (QoS) requirements that would be essential for an application like WBAN. It fuses data from several biosensors and based on the time criticality of the data, schedules them intelligently such that the data reaches its destination in a timely and energy efficient manner. Simulation results show that the traffic prioritization and scheduling scheme proposed in our MAC architecture surpasses the standard IEEE 802.15.4 MAC protocol in performance.

Risk evaluation through decision-support architectures in threat assessment and countering terrorism

Owing to their inherent nature, terrorist activities could be highly diversified. The risk assessment becomes a crucial component as it helps us weigh pros and cons versus possible actions or some planning pursuits. The recognition of threats and their relevance/seriousness is an integral part of the overall process of classification, recognition, and assessing eventual actions undertaken in presence of acts of chem.-bio terrorism. In this study, we introduce an overall scheme of risk assessment realized on a basis of classification results produced for some experimental data capturing the history of previous threat cases. The structural relationships in these experimental data are first revealed with the help of information granulation - fuzzy clustering. We introduce two criteria using which information granules are evaluated, that is (a) representation capabilities which are concerned with the quality of representation of numeric data by abstract constructs such as information granules, and (b) interpretation aspects which are essential in the process of risk evaluation. In case of representation facet of information granules, we demonstrate how a reconstruction criterion quantifies their quality. Three ways in which interpretability is enhanced are studied. First, we show how to construct the information granules with extended cores (where the uncertainty associated with risk evaluation could be reduced) and shadowed sets, which provide a three-valued logic perspective of information granules given in the form of fuzzy sets. Subsequently, we show a way of interpreting fuzzy sets via an optimized set of its @a-cuts.

On the Use of Randomization for System of Systems (SoS) Design of Intelligent Machines

This paper takes a system of systems (SoS) approach to the realization of machine intelligence. Random differences are learned by the system, generalized, and made available for subsequent replay in design transformations. It is empirically demonstrated that cross-domain symmetries can play a major role in design generation in particular and in the design of SoSs in general. The fundamental theory of randomization is the science, which underpins the practice. The approach is illustrated by an example of the design of a refrigeration system.

An Adaptive Fitness Function for Evolutionary Algorithms Using Heuristics and Prediction

A genetic algorithm usually performs a search over a complex and multimodal space and is an important component in several applications such as evolutionary learning and optimization. The search is dependent on several parameters including the fitness function, parent selection process, mutation rate and crossover rate. The fitness function is an important component in the evolutionary process since this performance metric is used to select the best individuals in a population that will then evolve through the mutation, crossover and reproduction process in successive generations. In this paper, a fitness function is developed that employs heuristic information based upon past history, current information and future knowledge; in particular, prediction and expectation are integrated into the fitness function. Simulation results show an improvement over classical fitness techniques.