Location-based Method Research Articles

Electricity accounting in life cycle assessment: the challenge of double counting

PurposeIn grid electricity consumption models, the location-based method uses regional average emission factors to account for environmental impacts. The market-based method is based on contractual agreements, verifying the exclusive claim on electricity from specific energy sources. An inconsistent application of these methods in life cycle assessment (LCA) and GHG accounting can lead to double counting. Especially, double counting electricity associated with rather low environmental impacts, such as renewable energy, might lead to impact underestimations. The aim of this paper is to identify, describe and propose solutions to double counting challenges.MethodsA four-step procedure is carried out. First, the specifications on grid electricity mix selection in frequently applied standards for LCA and GHG accounting are analysed. Besides the ISO norms for LCA (14040/44) and carbon footprinting (14064/67), the GHG Protocol and the Product and Organizational Environmental Footprint (PEF/OEF) are considered. Based on this analysis, challenges of double counting electricity from specific sources are identified. In the third step, potential solutions for avoiding double counting are proposed. The last research step consists of an illustrative case study to demonstrate the calculation of market-based electricity mixes and identify potential adjustments necessities for LCA application.Results and discussionA parallel application of the location-based and the market-based method poses the main double-counting challenge. Thus, avoiding double counting demands consistent method application throughout the whole life cycle. Whereas this is relatively straightforward for the location-based method, consistent market-based method application is more challenging. LCAs rely on average life cycle inventory processes, which mostly include location-based electricity mixes. However, for consistent market-based method application throughout the life cycle, electricity-related environmental impacts in the inventory system also need to be market-based. This would demand a partial recalculation of LCI datasets using market-based residual electricity mixes. Besides illustrating the calculation of market-based electricity mixes, the case study is used to identify and propose solutions for two main challenges for residual mix application in LCA: countries without residual mix and electricity under a double marketing ban.ConclusionDouble counting of electricity from specific energy sources is a challenge, since it can lead to under- or overestimations of environmental impacts. Both the location-based and market-based method can avoid double counting. However, parallel or inconsistent applications of both methods lead to double counting. In order to avoid double counting, there is a need to enable and use consistent electricity accounting rules in LCA and GHG accounting.

Workload leveling based on work space zoning for takt planning

This paper focuses on a location-based method used in construction, namely the Work Density Method (WDM) for takt planning. The contribution to knowledge is presenting a problem formulation and a mathematical optimization algorithm to divide a work space into zones while leveling work densities across trades in a process. Given a work space and the number of zones in which to divide that space, the so-called WoLZo problem is to identify the shape and dimensions of each zone while minimizing the peak in the trades' workloads per zone. Increasing the number of zones tends to result in lowering the peak, but application of the WoLZo algorithm shows that this trend levels out due to the spatial distribution of work densities. Application to work density maps of different granularity sheds light on zoning work spaces and sharpens one's intuition on how many zones to select. These findings inform data collection pertaining to work density as needed when planning construction work on the basis of takt.

Distance- rather than location-based temporal judgments are more accurate during episodic recall in a real-world task

ABSTRACT Definitions of episodic memory typically emphasise the importance of spatiotemporal frameworks in the contextual reconstruction of episodic retrieval. However, our ability to retrieve specific temporal contexts of experienced episodes is poor. This has bearing on the prominence of temporal context in the definition and evaluation of episodic memory, particularly among non-human animals. Studies demonstrating that rats rely on elapsed time (distance) rather than specific timestamps (location) to disambiguate events have been used to suggest that human episodic memory is qualitatively different to other species. We examined whether humans were more accurate using a distance- or location-based method for judging when an event happened. Participants (n = 57) were exposed to a series of events and then asked either when (e.g., 1:03 pm) or how long ago (HLA; e.g., 33 min) a specific event took place. HLA judgements were significantly more accurate, particularly for the most recently experienced episode. Additionally, a significantly higher proportion of participants making HLA judgements accurately recalled non-temporal episodic features across all episodes. Finally, for participants given the choice of methods for making temporal judgements, a significantly higher proportion chose to use HLA judgements. These findings suggest that human and non-human temporal judgements are not qualitatively different.

Location-Based Method to Identify Industrial Clusters in Beijing-Tianjin-Hebei Area in China

AbstractThe extant quantitative methodologies to identify industrial clusters usually lack the combination of both industrial linkages and enterprise proximity information. To rectify this situatio...

Optimised handoff mechanism using RFID tags for a communication-based train control system

In a communication-based train control system, one of the most important aspects is ensuring that a mobile station is connected continuously to the zone controller to ensure continuous and precise transmission of the train's location, speed and other relevant information. Due to the continuous motion of the train handoffs between different APs is necessary to remain connected. This work aims to propose a location-based handoff method which will minimise handoff latency and provide better connections. The location-based handoff mechanism uses the concept of placing RFID tags at predetermined locations. Handoff is triggered at the location where the RFID tag is present and a connection is made to the new AP based on the channel information obtained. The architecture used in the scenario is a WiMAX architecture and connections are maintained with the help of routers contained within the mobile stations. The proposed handoff method provides better handoff latency and error free transmission in a system where precise information transmission is necessary.

A location-based method for restricting the flooding DoS effect in WLANs

WLANs are prone to flooding denial-of-service (DoS) attacks. Present research work proposes a novel method to enhance WLAN security by restricting flooding DoS effect on the access point (AP). Each node in the proposed method listens transmission of other neighbouring nodes and sends at regular interval a linked list to AP, containing the identifiers (IDs) of all nodes from which it hears. AP stores the linked list information and runs location-based coordinate calculation (LBCC) algorithm. LBCC finds the expected coordinates of each node and probable location area with the expected coordinates as centre. Flooding DoS attack by the attacker is restricted at the AP using node’s probable location area. The method is validated by simulation experimentations with varying topologies. Further, geometrical analysis of the method is also provided.

Mobile air monitoring data-processing strategies and effects on spatial air pollution trends

Abstract. The collection of real-time air quality measurements while in motion (i.e., mobile monitoring) is currently conducted worldwide to evaluate in situ emissions, local air quality trends, and air pollutant exposure. This measurement strategy pushes the limits of traditional data analysis with complex second-by-second multipollutant data varying as a function of time and location. Data reduction and filtering techniques are often applied to deduce trends, such as pollutant spatial gradients downwind of a highway. However, rarely do mobile monitoring studies report the sensitivity of their results to the chosen data-processing approaches. The study being reported here utilized 40 h (> 140 000 observations) of mobile monitoring data collected on a roadway network in central North Carolina to explore common data-processing strategies including local emission plume detection, background estimation, and averaging techniques for spatial trend analyses. One-second time resolution measurements of ultrafine particles (UFPs), black carbon (BC), particulate matter (PM), carbon monoxide (CO), and nitrogen dioxide (NO2) were collected on 12 unique driving routes that were each sampled repeatedly. The route with the highest number of repetitions was used to compare local exhaust plume detection and averaging methods. Analyses demonstrate that the multiple local exhaust plume detection strategies reported produce generally similar results and that utilizing a median of measurements taken within a specified route segment (as opposed to a mean) may be sufficient to avoid bias in near-source spatial trends. A time-series-based method of estimating background concentrations was shown to produce similar but slightly lower estimates than a location-based method. For the complete data set the estimated contributions of the background to the mean pollutant concentrations were as follows: BC (15%), UFPs (26%), CO (41%), PM2.5-10 (45%), NO2 (57%), PM10 (60%), PM2.5 (68%). Lastly, while temporal smoothing (e.g., 5 s averages) results in weak pair-wise correlation and the blurring of spatial trends, spatial averaging (e.g., 10 m) is demonstrated to increase correlation and refine spatial trends.

인간 적응형 가전기기를 위한 거주자 심박동 기반 신체활동량 추정

Intelligent homes consist of ubiquitous sensors, home networks, and a context-aware computing system. These homes are expected to offer many services such as intelligent air-conditioning, lighting control, health monitoring, and home security. In order to realize these services, many researchers have worked on various research topics including smart sensors with low power consumption, home network protocols, resident and location detection, context-awareness, and scenario and service control. This paper presents the real-time metabolic rate estimation method that is based on measured heart rate for human adaptive appliance (air-conditioner, lighting etc.). This estimation results can provide valuable information to control smart appliances so that they can adjust themselves according to the status of residents. The heart rate based method has been experimentally compared with the location-based method on a test bed.

An Approach to Representing Movement Data

A moving object is an object of which position changes continuously in space. The movement of an object is represented by the change of its position through space and the change of its attributes over time or location. In temporal geography, the model of space-time cube (STC) represents movements as polylines time-ordered connecting spatio-temporal positions of moving objects. The space-time cubes do not represent attributes of moving objects, which change over time or along trajectories. Several authors used two cubes to represent a moving object, one for spatio-temporal positions and another for attributes changing over location. This article proposes an approach to representing multivariate data of moving objects in a Cartesian coordinate system. The main idea of this article is to join the coordinate system representing location data with the coordinate system representing attributes by sharing the time axis. In this case, attributes that are recorded by the location-based method are inferred visually through the space-time path of the movement. This approach can apply for two cases, given route and ungiven route. For movements of ungiven route, the visualization system combines a 3-dimension system representing locations with a 2-dimension system representing attributes. For movements of given route, the visualization system is a 3-dimension Cartesian coordinate system, where the trajectory is extended as an axis representing the distance from departure place to current place, two other axes indicate time and attributes. In both modes, colors are employed as dimensions to show various attributes. The two modes can be switched reciprocally in a system tracking moving objects on ground.

Automatic anatomical labeling method of cerebral arteries in MR-angiography data set.

To improve the accuracy and robustness of 2D/3D registration of digital subtraction angiography images and magnetic resonance angiography (MRA) data, we have developed an automatic method for anatomical labeling of the cerebral arteries in MRA data. The anatomical labeling method is a location-based method which segments an artery tree to branches and classifies the branches into labeled segments, i.e., internal carotid arteries (ICA), basilar artery (BA), middle cerebral arteries (MCA), A1 segments of the anterior cerebral artery (ACA(A1)), other segments of the anterior cerebral artery (ACA), posterior communication arteries (PcomA) and posterior cerebral arteries (PCA), according to their location. Arteries were extracted from MRA data for this labeling method by the region-growing technique. Fifteen cases were examined to evaluate the method accuracy. The number of correctly segmented voxels in each artery segment was determined, and the correct labeling percentage was calculated based on the total number of voxels of the artery. Mean percentages were as follows: ACA, 82.7%; Right (R-) ACA(A1), 47.1%; Left (L-) ACA(A1), 46.1%; R-MCA, 80.4%; L-MCA, 74.1%; R-PcomA, 0.0%; L-PcomA, 3.3%; R-PCA, 60.3%; LPCA, 66.9%; R-ICA, 90.7%; L-ICA, 90.7%; BA, 89.9%; and total arteries, 84.1%. The ACA, MCA, ICA and BA were consistently identified correctly.

MoveMine

With the maturity and wide availability of GPS, wireless, telecommunication, and Web technologies, massive amounts of object movement data have been collected from various moving object targets, such as animals, mobile devices, vehicles, and climate radars. Analyzing such data has deep implications in many applications, such as, ecological study, traffic control, mobile communication management, and climatological forecast. In this article, we focus our study on animal movement data analysis and examine advanced data mining methods for discovery of various animal movement patterns. In particular, we introduce a moving object data mining system, MoveMine, which integrates multiple data mining functions, including sophisticated pattern mining and trajectory analysis. In this system, two interesting moving object pattern mining functions are newly developed: (1) periodic behavior mining and (2) swarm pattern mining . For mining periodic behaviors, a reference location-based method is developed, which first detects the reference locations, discovers the periods in complex movements, and then finds periodic patterns by hierarchical clustering. For mining swarm patterns, an efficient method is developed to uncover flexible moving object clusters by relaxing the popularly-enforced collective movement constraints. In the MoveMine system, a set of commonly used moving object mining functions are built and a user-friendly interface is provided to facilitate interactive exploration of moving object data mining and flexible tuning of the mining constraints and parameters. MoveMine has been tested on multiple kinds of real datasets, especially for MoveBank applications and other moving object data analysis. The system will benefit scientists and other users to carry out versatile analysis tasks to analyze object movement regularities and anomalies. Moreover, it will benefit researchers to realize the importance and limitations of current techniques and promote future studies on moving object data mining. As expected, a mastery of animal movement patterns and trends will improve our understanding of the interactions between and the changes of the animal world and the ecosystem and therefore help ensure the sustainability of our ecosystem.