Time-dependent Attributes Research Articles

Spatial Functional Outlier Detection in Multivariate Spatial Functional Data

Multivariate spatial functional data consists of multiple functions of time-dependent attributes observed at each spatial point. This study focuses on detecting spatial outliers in spatial functional data. Firstly, we develop a new method called Mahalanobis Distance Spatial Outlier (MDSO) to detect functional outliers in the data. The method introduces the multivariate functional Mahalanobis semi-distance and multivariate pairwise functional Mahalanobis semi-distance metrics based on the multivariate functional principal components analysis to calculate the dissimilarity between functions at each spatial point. Via simulation, we show that MDSO performs better than the other competing methods. Secondly, MDSO has been extended to detect spatial functional outliers as well. The functional outliers can now be categorized as global or/and local functional outliers. The appropriate number of neighbors and the cut-off point for the degree of isolation are determined via simulation. Finally, we demonstrate the application of the MDSO on a water quality data set obtained from Sungai Klang basin in Malaysia. The results can be used to support the authority in making better decisions on the management of the river basin or other spatial data with time-independent attributes.

Optimal Coordination Of Multiple Setting Group Type Overcurrent Relays

Relay coordination optimization issues are multi-objective, multi-constrained problems that aim to reduce each relay's operation time while satisfying all relevant constraints. Fulfillment of the coordination time interval (CTI) and time limitations for relay operations are the coordination constraints. To solve coordination optimization issues, decision-making variables are defined such that the objective function is minimized with due satisfaction of necessary constraints. The majority of the most recent research that is currently available defines relay coordination for fixed network topologies, but in practice, it varies for a variety of reasons, including maintenance and element failures. Over current relays now have the capability of storing multiple relay settings but the number of relay settings that can be stored in relays is far less than the possible number of network configurations, which is why a suitable clustering technique such as K-Means can be used to cluster out various network topologies using a proper clustering index. Because both the objective function and the constraints are time dependent, time dependent attributes such as average relay operating time are used as the clustering index instead of the conventional index fault current deviation.

A framework for efficient multi-attribute movement data analysis

In the first two decades of this century, the amount of movement and movement-related data has increased massively, predominantly due to the proliferation of positioning features in ubiquitous devices such as cellphones and automobiles. At the same time, there is a vast number of requirements for managing and analyzing these records for economic, administrative, and private purposes. Since the growth of data quantity outpaces the efficiency development of hardware components, it is necessary to explore innovative methods of extracting information from large sets of movement data. Hence, the management and analysis of such data, also called trajectories, have become a very active research field. In this context, the time-dependent geographic position is only one of arbitrarily many recorded attributes. For several applications processing trajectory (and related) data, it is helpful or even necessary to trace or generate additional time-dependent information, according to the purpose of the evaluation. For example, in the field of aircraft traffic analysis, besides the position of the monitored airplane, also its altitude, the remaining amount of fuel, the temperature, the name of the traversed country and many other parameters that change with time are relevant. Other application domains consider the names of streets, places of interest, or transportation modes which can be recorded during the movement of a person or another entity. In this paper, we present in detail a framework for analyzing large datasets having any number of time-dependent attributes of different types with the help of a pattern language based on regular expression structures. The corresponding matching algorithm uses a collection of different indexes and is divided into a filtering and an exact matching phase. Compared to the previous version of the framework, we have extended the flexibility and expressiveness of the language by changing its semantics. Due to storage adjustments concerning the applied index structures and further optimizations, the efficiency of the matching procedure has been significantly improved. In addition, the user is no longer required to have a deep knowledge of the temporal distribution of the available attributes of the dataset. The expressiveness and efficiency of the novel approach are demonstrated by querying real and synthetic datasets. Our approach has been fully implemented in a DBMS querying environment and is freely available open source software.

Applying Time-Dependent Attributes to Represent Demand in Road Mass Transit Systems.

The development of efficient mass transit systems that provide quality of service is a major challenge for modern societies. To meet this challenge, it is essential to understand user demand. This article proposes using new time-dependent attributes to represent demand, attributes that differ from those that have traditionally been used in the design and planning of this type of transit system. Data mining was used to obtain these new attributes; they were created using clustering techniques, and their quality evaluated with the Shannon entropy function and with neural networks. The methodology was implemented on an intercity public transport company and the results demonstrate that the attributes obtained offer a more precise understanding of demand and enable predictions to be made with acceptable precision.

Index-supported pattern matching on tuples of time-dependent values

Lately, the amount of mobility data recorded by GPS-enabled (and other) devices has increased drastically, entailing the necessity of efficient processing and analysis methods. In many cases, not only the geographic position, but also additional time-dependent information are traced and/or generated, according to the purpose of the evaluation. For example, in the field of animal behavior research, besides the position of the monitored animal, biologists are interested in further data like the altitude or the temperature at every measuring point. Other application domains comprise the names of streets, places of interest, or transportation modes that can be recorded along with the geographic position of a person. In this paper, we present in detail a framework for analyzing datasets with arbitrarily many time-dependent attributes. This can be considered as a major extension of our previous work, a comprehensive framework for pattern matching on symbolic trajectories with index support. For an efficient processing of different data types, a variable number of indexes of four different types that correspond to the data types of the attributes are applied. We demonstrate the expressiveness and efficiency of our approach by querying a real dataset representing taxi trips in Rome and, particularly, with a broad series of experiments using trajectories generated by BerlinMOD combined with geological raster data.

A Time-Dependent Approach to Evaluate Capacity Value of Wind and Solar PV Generation

Contribution of renewable energies in power systems is increasing due to continuous growth of wind and solar generators. Because of intermittency and uncertainty of these resources, conventional reliability evaluation methods are not applicable and different techniques have been developed to model these generators. However, most of these methods are time-consuming or may not be able to keep time dependency and correlations between renewable resources and load. Therefore, this paper intends to improve the existing methods and proposes a fast and simple approach. In this approach, wind power, photovoltaic (PV) generation, and electricity demand have been modeled as time-dependent clusters, which not only can capture their time-dependent attributes, but also are able to keep the correlations between these data sets. To illustrate the effectiveness of this framework, the proposed methodology has been applied on two different case studies: 1)IEEE RTS system and 2)South Australia (SA) power network. The developed technique is validated by comparing results with sequential Monte Carlo technique.

PREHISPANIC JEDDITO YELLOW WARE: AN OVERVIEW

This paper summarizes the state of knowledge of Jeddito Yellow Ware bowls for several attributes related to typology including external design elements (glyphs), village of manufacture using Neutron Activation Analysis (NAA), chronology through seriation using several time-dependent attributes, and provenance.

Pseudo dynamic transitional modeling of building heating energy demand using artificial neural network

This paper presents the building heating demand prediction model with occupancy profile and operational heating power level characteristics in short time horizon (a couple of days) using artificial neural network. In addition, novel pseudo dynamic transitional model is introduced, which consider time dependent attributes of operational power level characteristics and its effect in the overall model performance is outlined. Pseudo dynamic model is applied to a case study of French Institution building and compared its results with static and other pseudo dynamic neural network models. The results show the coefficients of correlation in static and pseudo dynamic neural network model of 0.82 and 0.89 (with energy consumption error of 0.02%) during the learning phase, and 0.61 and 0.85 during the prediction phase, respectively. Further, orthogonal array design is applied to the pseudo dynamic model to check the schedule of occupancy profile and operational heating power level characteristics. The results show the new schedule and provide the robust design for pseudo dynamic model. Due to prediction in short time horizon, it finds application for Energy Services Company (ESCOs) to manage the heating load for dynamic control of heat production system.

첨두 및 비첨두시 VMS 교통정보의 가치 변화 연구

ITS사업을 통한 교통정보제공편익은 크게 두 가지 측면으로 볼 수 있는데, 공공측면의 경우 교통정보를 교통관리의 수단으로 활용하였을 때 나타나는 통행시간 절감 편익이나, ITS사업의 경제적 타당성 평가시 교통정보제공편익은 독립적인 편익으로 인정받지 못하고 첨단교통관리(ATMS)의 부산적 편익으로 간주되어 왔다. 이용자 측면에서는 전방 교통정보 제공에 따른 운전자의 불확실성 감소이다. 운전자는 교통정보를 통해 통행의 불확실성이 감소한다고 판단할 경우 이에 대해 지불의사를 가지므로 이를 근거로 교통정보의 가치를 추정할 수 있다. 최근 교통정보의 편익을 계량화하려는 노력은 많아지고 있으나, 교통정보가치의 다변성과 정보 자체의 속성을 고려하지 못하고 단일 값으로 제시하고 있어 교통정보제공편익을 과대추정하거나 왜곡할 우려가 있다. 따라서 합리적인 교통정보제공편익을 산정하기 위해서는 교통정보 가치의 세분화가 필요하다. 이에 본 연구에서는 VMS가 제공하는 교통정보의 가치를 추정하되 통행시간대(첨두/비첨두)에 따른 세분화를 통하여 보다 합리적으로 교통정보의 가치를 산정하고, 추정결과에 대한 가설검정을 통해 가치 세분화의 통계적 유의성을 밝히고자 한다. The benefit of traffic information on variable message sign can be divided into two. At the public level, the benefit of ATIS is the travel time saving, which is not only induced from ATIS, but also mixed with that of ATMS. In the economic appraisal of ITS, the benefit of ATIS has so far been regarded as the derived benefit from ATMS. At the user level, the benefit of ATIS is reduced driver uncertainty through the forward traffic status information. User can benefit from the information on VMS and therefore may have the willingness to pay for it. Recently attempt to qualify the value of information on VMS was increased, but there was a danger of distorting or over-estimates of the ATIS benefit because the related studies didn't consider the time-dependent attributes of traffic information and provided the single value. Estimates of the time-varying value should be needed for a rigorous economic appraisal of ATIS. In this study, we varied the value of information on VMS according to peak and non peak trip and verified the hypothesis that time-varying of value was statistically significant.

Policy Management for Secure Data Access Control in Vehicular Networks

The state-of-the-art research in vehicular network security does not address the need for low latency message access control in vehicular applications with tight connection time and message delay requirements. In existing security solutions, the major limitation is that no trust establishment mechanisms that adapt to rapidly changing scenarios and highly mobile environments (mainly because of key management delay, processing overhead, and changing communication peers). To address this issue, we present a policy management framework for secure data access control in vehicular networks. Our solution address two interrelated research areas to achieve efficiency and scalability for data access control and policy management in highly dynamic vehicular networks. The main contributions are in two-fold: (a) efficient key management and group-based policy enforcement using attribute-based cryptography; and (b) dynamic security policy management framework and methodology to manage credentials based on role, time, location and other situation dependent attributes. Our solution utilizes efficient attribute-based cryptography algorithm to achieve unprecedented speedups in message processing time to meet the real-time requirement. To demonstrate the effectiveness of our proposed solution, a systematic and comprehensive evaluation is produced to valid our proposed solution.

The representation and implementation of time-dependent inundation in large-scale microscopic evacuation simulations

Multi-agent simulation has increasingly been used for transportation simulation in recent years. With current techniques, it is possible to simulate systems consisting of several million agents. Such multi-agent simulations have been applied to whole cities and even large regions. In this paper it is demonstrated how to adapt an existing multi-agent transportation simulation framework to large-scale pedestrian evacuation simulation. The underlying flow model simulates the traffic-based on a simple queue model where only free speed, bottleneck capacities, and space constraints are taken into account. The queue simulation, albeit simple, captures the most important aspects of evacuations such as the congestion effects of bottlenecks and the time needed to evacuate the endangered area. In the case of an evacuation simulation the network has time-dependent attributes. For instance, large-scale inundations or conflagrations do not cover all the endangered area at once. These time-dependent attributes are modeled as network change events. Network change events are modifying link parameters at predefined points in time. The simulation framework is demonstrated through a case study for the Indonesian city of Padang, which faces a high risk of being inundated by a tsunami.

Reliability Evaluation for Stochastic and Time-dependent Networks with Multiple Parking Facilities

This paper presents a fixed-point model for evaluating the reliability of stochastic and time-dependent networks with multiple parking facilities. The proposed model combines a supply model that simulates the time-dependent attributes of road and parking supplies and their variations with a demand model that simultaneously considers heterogeneous travelers’ choices on departure time, route and parking location. In the proposed model, travelers are differentiated by their values of time, and parking locations are characterized by facility type. Schedule reliability and parking reliability are introduced as new performance indices for the evaluation of the level of service of a road network during time of day. A heuristic solution algorithm that uses a combination of the Monte Carlo simulation approach with the method of successive averages is proposed to estimate these two reliability measures. Numerical results show that the reliability performances of road networks are significantly influenced by the network congestion level and the capacities of road and parking facilities. The proposed model provides some new insights for assessing the impacts of various transport policies and infrastructure improvements at a strategic level.

Routing of a hazmat truck in the presence of weather systems

This paper focuses on the effects of weather systems on hazmat routing. We start by analyzing the effects of a weather system on a vehicle traversing a single link. This helps characterize the time-dependent attributes of a link due to movement of the weather systems. This analysis is used as a building block for the problem of finding a least risk path for hazmat transportation on a network exposed to such weather systems. Several methods are offered to solve the underlying problem, and computational results are reported. We draw two conclusions from this paper. First, it is possible to determine the time-dependent attributes for links on a network provided that some assumptions on the nature of the weather system are made. Second, heuristics can provide effective solutions for practical size problems while allowing for parking the vehicle to avoid weather system effects.

Walk-off ring-down spectroscopy: attaining ultrafast resolution by converting time into space

A new technique for monitoring time-resolved phenomena is demonstrated by which a probe beam is directed into an optical cavity. Spatially separated output pulses are produced by directing the incident beam into the cavity in such a way that it walks in a direction transverse to the optical axis as it propagates between the two mirrors. This effect may be achieved either by a plane-parallel resonator oriented at a nonzero angle of incidence or by a wedge cavity, for which one of the mirrors is set at an angular offset. The spatially resolved output train may then be related to the time-dependent attributes of the resonator and the molecules it encloses. Time resolution is controlled by the separation of the mirrors, wedge geometry, and the angle of incidence. A time resolution of better than 4 ps is achieved using a 500 µm mirror separation and a 20° angle of incidence.

Dynamic inventory database management (DIDM): a summary of an internet-based solution for managing complexity of inventory databases

In recent years, fast growth in the development and application of e-business technologies has generated vast interest among practising managers and management scholars in inventory database management. E-business technologies enable an inventory manager to make coordinated decisions on the basis of regularly updated and timely ordered information. The dynamic features of the inventory items and inventory locations make a rationally organised time schedule of regular and irregular updates for inventory databases difficult. Using a game theory strategy of optimal regular updates for dynamic databases can significantly lower costs associated with the database update process. This article describes an internet-based solution of creation, maintenance, update, and query of a computerised inventory database for inventory items and their locations with time-dependent (dynamic) and time-independent (static) attributes. Further research opportunities in inventory database management using e-business applications are discussed.

Modeling Temporal Dynamics for Business Systems

Research in temporal database management has viewed temporal dynamics from a structural perspective, posing extensions to the entity-relationship (E-R) model to represent the state history of time-dependent attributes and relationships. We argue that temporal dynamics are semantic rather than structural and that the existing constructs in the E-R model are sufficient to represent them. Practitioners have long used E-R models without temporal extensions to design systems with rich support for temporality by modeling both things and events as entities — a practice that is consistent with the original presentation of the E-R model. This approach supports methodologies that leverage narrative and human cognitive processing capabilities in the development and verification of data models. Furthermore it maintains modeling parsimony and facilitates the representation of causality — why a particular state exists.

GIS and Transportation: Status and Challenges

The evolution of GIS-T is characterized in three stages: the map view, the navigational view, and the behavioral view. The static nature of the map view favors applications related to inventory and description, and raises difficult questions of accuracy and interoperability. The navigational view adds concerns for connectivity and planarity, and the storage of time-dependent attributes. Navigation also raises issues of representation related to scale, including the need for lane-level connectivity. The behavioral view stems from the work of Hagerstrand, treating transportation events as dynamic and occurring within the largely static transportation space. Appropriate representations for the behavioral view have still to be worked out. In all three cases the legacies of prior technologies and perspectives are still evident. The paper presents a series of research challenges, dealing with standards, representation, unambiguous communication, economic models, response to new technologies, and application of knowledge gained from GIS-T and ITS research to other fields.

The Wisdom of the Body Revisited.

Being selected a Cannon lecturer by the American Physiological Society is a singular honor under any circumstances, but a particularly poignant one for me because of my origins in Cannon’s old Department at the Harvard Medical School. In fact, when I began my career there as an instructor 45 years

The evaluation of time-dependent attributes

The evaluation of time-dependent attributes

Adaptive neural nets for generation of artificial earthquake precursors

A novel methodology for generation of artificial earthquake precursors was tested on Southern California earthquake data in reverse and real time modes. When it was tried as a real time generator of earthquake precursors, it successfully predicted the June, 1992, Landers earthquake. The methodology is based on the use of adaptive neural nets (ANN) that process a set of time-dependent attributes calculated in a moving time-window. The most important of them is a danger function. The structure of the neural net is defined by the properties of input data in the moving time window. Thus, the neural net continuously adapts its structure to the time variant properties of the input attributes. The main problem the authors encountered in training the neural net on the earthquake data was the small size of the training set compared to the number of parameters that describe the structure of the ANN. To prevent instability and over-fitting in the training session, the authors used a technique similar to the damping method in least squares approximation.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>