A new and efficient intelligent collaboration scheme for fashion design

Technology-mediated collaboration process has been extensively studied for over a decade. Most applications with collaboration concepts reported in the literature focus on enhancing efficiency and effectiveness of the decision-making processes in objective and well-structured workflows. However, relatively few previous studies have investigated the applications of collaboration schemes to problems with subjective and unstructured nature. In this paper, we explore a new intelligent collaboration scheme for fashion design which, by nature, relies heavily on human judgment and creativity. Techniques such as multicriteria decision making, fuzzy logic, and artificial neural network (ANN) models are employed. Industrial data sets are used for the analysis. Our experimental results suggest that the proposed scheme exhibits significant improvement over the traditional method in terms of the time–cost effectiveness, and a company interview with design professionals has confirmed its effectiveness and significance.

Robust suspicious behaviour detection for smart surveillance systems

Video surveillance technology, based on Closed Circuit Television (CCTV) cameras, is one of the fastest growing markets in the field of security technologies. However, the existing video surveillance systems are still not at a stage where they can be used for crime prevention. The systems rely heavily on human observers and are therefore limited by factors such as fatigue and monitoring capabilities over long periods of time. To overcome this limitation, it is necessary to have “intelligent” processes which are able to highlight the salient data and filter out normal conditions that do not pose a threat to security. In order to create such intelligent systems, an understanding of human behaviour, specifically, suspicious behaviour is required. One of the challenges in achieving this is that human behaviour can only be understood correctly in the context in which it appears. Although context has been exploited in the general computer vision domain, it has not been widely used in the automatic suspicious behaviour detection domain. So, it is essential that context has to be formulated, stored and used by the system in order to understand human behaviour. Finally, since surveillance systems could be modeled as largescale data stream systems, it is difficult to have a complete knowledge base. In this case, the systems need to not only continuously update their knowledge but also be able to retrieve the extracted information which is related to the given context. To address these issues, a context-based approach for detecting suspicious behaviour is proposed. In this approach, contextual information is exploited in order to make a better detection. The proposed approach utilises a data stream clustering algorithm in order to discover the behaviour classes and their frequency of occurrences from the incoming behaviour instances. Contextual information is then used in addition to the above information to detect suspicious behaviour. The proposed approach is able to detect observed, unobserved and contextual suspicious behaviour. Two case studies using video feeds taken from CAVIAR dataset and Z-block building, Queensland University of Technology are presented in order to test the proposed approach. From these experiments, it is shown that by using information about context, the proposed system is able to make a more accurate detection, especially those behaviours which are only suspicious in some contexts while being normal in the others. Moreover, this information give critical feedback to the system designers to refine the system. Finally, the proposed modified Clustream algorithm enables the system to both continuously update the system’s knowledge and to effectively retrieve the information learned in a given context. The outcomes from this research are: (a) A context-based framework for automatic detecting suspicious behaviour which can be used by an intelligent video surveillance in making decisions; (b) A modified Clustream data stream clustering algorithm which continuously updates the system knowledge and is able to retrieve contextually related information effectively; and (c) An update-describe approach which extends the capability of the existing human local motion features called interest points based features to the data stream environment.

A control and surveillance ITS location model to improve safety

Traffic safety in rural highways can be considered as a constant source of concern in many countries. Nowadays, transportation professionals widely use Intelligent Transportation Systems (ITS) to address safety issues. However, compared to metropolitan applications, the rural highway (non-urban) ITS applications are still not well defined. This paper provides a comprehensive review on the existing ITS safety solutions for rural highways. This research is mainly focused on the infrastructure-based control and surveillance ITS technology, such as Crash Prevention and Safety, Road Weather Management and other applications, that is directly related to the reduction of frequency and severity of accidents. The main outcome of this research is the development of a ‘ITS control and surveillance device locating model’ to achieve the maximum safety benefit for rural highways. Using cost and benefits databases of ITS, an integer linear programming method is utilized as an optimization technique to choose the most suitable set of ITS devices. Finally, computational analysis is performed on an existing highway in Iran, to validate the effectiveness of the proposed locating model.

Empirical evaluation of an autonomous vehicle in an urban environment

Operation in urban environments creates unique challenges for research in autonomous ground vehicles. Due to the presence of tall trees and buildings in close proximity to traversable areas, GPS outage is likely to be frequent and physical hazards pose real threats to autonomous systems. In this paper, we describe a novel autonomous platform developed by the Sydney-Berkeley Driving Team for entry into the 2007 DARPA Urban Challenge competition. We report empirical results analyzing the performance of the vehicle while navigating a 560-meter test loop multiple times in an actual urban setting with severe GPS outage. We show that our system is robust against failure of global position estimates and can reliably traverse standard two-lane road networks using vision for localization. Finally, we discuss ongoing efforts in fusing vision data with other sensing modalities.

Intelligent distribution planning and control incorporating microgrids

This paper proposes a comprehensive approach to the planning of distribution networks and the control of microgrids. Firstly, a Modified Discrete Particle Swarm Optimization (MDPSO) method is used to optimally plan a distribution system upgrade over a 20 year planning period. The optimization is conducted at different load levels according to the anticipated load duration curve and integrated over the system lifetime in order to minimize its total lifetime cost. Since the optimal solution contains Distributed Generators (DGs) to maximize reliability, the DG must be able to operate in islanded mode and this leads to the concept of microgrids. Thus the second part of the paper reviews some of the challenges of microgrid control in the presence of both inertial (rotating direct connected) and non-inertial (converter interfaced) DGs. More specifically enhanced control strategies based on frequency droop are proposed for DGs to improve the smooth synchronization and real power sharing minimizing transient oscillations in the microgrid. Simulation studies are presented to show the effectiveness of the control.

Persistent ocean monitoring with underwater gliders : adapting sampling resolution

Ocean processes are dynamic, complex, and occur on multiple spatial and temporal scales. To obtain a synoptic view of such processes, ocean scientists collect data over long time periods. Historically, measurements were continually provided by fixed sensors, e.g., moorings, or gathered from ships. Recently, an increase in the utilization of autonomous underwater vehicles has enabled a more dynamic data acquisition approach. However, we still do not utilize the full capabilities of these vehicles. Here we present algorithms that produce persistent monitoring missions for underwater vehicles by balancing path following accuracy and sampling resolution for a given region of interest, which addresses a pressing need among ocean scientists to efficiently and effectively collect high-value data. More specifically, this paper proposes a path planning algorithm and a speed control algorithm for underwater gliders, which together give informative trajectories for the glider to persistently monitor a patch of ocean. We optimize a cost function that blends two competing factors: maximize the information value along the path, while minimizing deviation from the planned path due to ocean currents. Speed is controlled along the planned path by adjusting the pitch angle of the underwater glider, so that higher resolution samples are collected in areas of higher information value. The resulting paths are closed circuits that can be repeatedly traversed to collect long-term ocean data in dynamic environments. The algorithms were tested during sea trials on an underwater glider operating off the coast of southern California, as well as in Monterey Bay, California. The experimental results show significant improvements in data resolution and path reliability compared to previously executed sampling paths used in the respective regions.

Predicting wave glider speed from environmental measurements

In the ocean science community, researchers have begun employing novel sensor platforms as integral pieces in oceanographic data collection, which have significantly advanced the study and prediction of complex and dynamic ocean phenomena. These innovative tools are able to provide scientists with data at unprecedented spatiotemporal resolutions. This paper focuses on the newly developed Wave Glider platform from Liquid Robotics. This vehicle produces forward motion by harvesting abundant natural energy from ocean waves, and provides a persistent ocean presence for detailed ocean observation. This study is targeted at determining a kinematic model for offline planning that provides an accurate estimation of the vehicle speed for a desired heading and set of environmental parameters. Given the significant wave height, ocean surface and subsurface currents, wind speed and direction, we present the formulation of a system identification to provide the vehicle’s speed over a range of possible directions.

Implementable efficient time and energy consumption trajectories design for an autonomous underwater vehicle

In this paper we consider the implementation of time and energy efficient trajectories onto a test-bed autonomous underwater vehicle. The trajectories are losely connected to the results of the application of the maximum principle to the controlled mechanical system. We use a numerical algorithm to compute efficient trajectories designed using geometric control theory to optimize a given cost function. Experimental results are shown for the time minimization problem.

Path planning using surface shape and ground properties

This paper presents a path planning technique for ground vehicles that accounts for the dynamics of the vehicle, the topography of the terrain and the wheel/ground interaction properties such as friction. The first two properties can be estimated using well known sensors and techniques, but the third is not often estimated even though it has a significant effect on the motion of a high-speed vehicle. We introduce a technique which allows the estimation of wheel slip from which frictional parameters can be inferred. We present simulation results which show the importance of modelling topography and ground properties and experimental results which show how ground properties can be estimated along a 350m outdoor traverse.

Acoustic hazard detection for pedestrians with obscured hearing

Pedestrians’ use of mp3 players or mobile phones can pose the risk of being hit by motor vehicles. We present an approach for detecting a crash risk level using the computing power and the microphone of mobile devices that can be used to alert the user in advance of an approaching vehicle so as to avoid a crash. A single feature extractor classifier is not usually able to deal with the diversity of risky acoustic scenarios. In this paper, we address the problem of detection of vehicles approaching a pedestrian by a novel, simple, non resource intensive acoustic method. The method uses a set of existing statistical tools to mine signal features. Audio features are adaptively thresholded for relevance and classified with a three component heuristic. The resulting Acoustic Hazard Detection (AHD) system has a very low false positive detection rate. The results of this study could help mobile device manufacturers to embed the presented features into future potable devices and contribute to road safety.

Controlled drift: an investigation into the controllability of underwater vehicles with minimal actuation

This paper is directed towards providing an answer to the question, ”Can you control the trajectory of a Lagrangian float?” Being a float that has minimal actuation (only buoyancy control), their horizontal trajectory is dictated through drifting with ocean currents. However, with the appropriate vertical actuation and utilising spatio-temporal variations in water speed and direction, we show here that broad controllabilty results can be met such as waypoint following to keep a float inside of a bay or out of a designated region. This paper extends theory experimen- tally evaluted on horizontally actuated Autonomous Underwater Vehicles (AUVs) for trajectory control utilising ocean forecast models and presents an initial investi- gation into the controllability of these minimally actuated drifting AUVs. Simulated results for offshore coastal and within highly dynamic tidal bays illustrate two tech- niques with the promise for an affirmative answer to the posed question above.

The effect of lumbar support on the trunk muscle activity and pelvic tilt in a passenger vehicle car seat

In order to gain a competitive edge in the market, automotive manufacturers and automotive seat suppliers have identified seat ergonomics for further development to improve overall vehicle comfort. Adjustable lumbar support devices have been offered since long as comfort systems in either a 2-way or 4-way adjustable configuration, although their effect on lumbar strain is not well documented. The effect of a lumbar support on posture and muscular strain, and therefore the relationship between discomfort and comfort device parameter settings, requires clarification. The aim of this paper is to study the effect of a 4-way lumbar support on lower trunk and pelvis muscle activity, pelvic tilt and spine curvature during a car seating activity. 10 healthy subjects (5 m/f; age 19-39) performed a seating activity in a passenger vehicle with seven different static lumbar support positions. The lumbar support was tested in 3 different height positions in relation to the seatback surface centreline (high, centre, low), each having 2 depths positions (lumbar prominence). An extra depth position was added for the centre position. Posture data were collected using a VICON MX motion capture system and NORAXON DTS goniometers and inclinometer. A rigid-body model of an adjustable car seat with four-way adjustable lumbar support was constructed in UGS Siemens NX and connected to a musculoskeletal model of a seated-human, modelled in AnyBody. Wireless electromyography (EMG) was used to calibrate the musculoskeletal model and assess the relationship between (a) muscular strain and lumbar prominence (normal to seatback surface) respective to the lumbar height (alongside seatback surface), (b) hip joint moment and lumbar prominence (normal to seatback surface) respective to lumbar height (alongside seatback surface) and (c) pelvic tilt and lumbar prominence (normal to seatback surface) respective to the lumbar height (alongside seatback surface). This study was based on the assumption that the musculoskeletal human model was seated at the correct R-Point (SgRP), determined via the occupant packaging toolkit in the JACK digital human model. The effect of the interaction between the driver/car-seat has been investigated for factors resulting from the presence and adjustment of a 4-way lumbar support. The results obtained show that various seat adjustments, and driver’s lumbar supports can have complex influence on the muscle activation, joint forces and moments, all of which can affect the comfort perception of the driver. This study enables the automotive industry to optimise passenger vehicle seat development and design. It further more supports the evaluation of static postural and dynamic seat comfort in normal everyday driving tasks and can be applied for future car design to reduce investment and improve comfort.

Optimal sitting and sizing of distributed generators considering plug-in electric vehicles

Some uncertainties such as the stochastic input/output power of a plug-in electric vehicle due to its stochastic charging and discharging schedule, that of a wind unit and that of a photovoltaic generation source, volatile fuel prices and future uncertain load growth, all together could lead to some risks in determining the optimal siting and sizing of distributed generators (DGs) in distributed systems. Given this background, under the chance constrained programming (CCP) framework, a new method is presented to handle these uncertainties in the optimal sitting and sizing problem of DGs. First, a mathematical model of CCP is developed with the minimization of DGs investment cost, operational cost and maintenance cost as well as the network loss cost as the objective, security limitations as constraints, the sitting and sizing of DGs as optimization variables. Then, a Monte Carolo simulation embedded genetic algorithm approach is developed to solve the developed CCP model. Finally, the IEEE 37-node test feeder is employed to verify the feasibility and effectiveness of the developed model and method. This work is supported by an Australian Commonwealth Scientific and Industrial Research Organisation (CSIRO) Project on Intelligent Grids Under the Energy Transformed Flagship, and Project from Jiangxi Power Company.