domain: spi info

text:
Daniela Andone

Daniel Merezeanu

MODERN POWER PLANT CONTROL PHILOSOPHIES

Keywords: Distributed Control Systems, Control Philosophies, Integration

The architecture of today's microprocessor-based distributed control and data acquisition systems allows integration, control and monitoring of various plant control systems from a centralized operator interface. This paper describes the conclusions of the authors resulted from studying of many Distributed Control System documentation and the approach used in establishing the design of DCS implementation in several romanian Power Plants. Key aspects associated with the control system architecture, integration with plant control systems, and graphical operator interface are investigated. This paper also summarizes future trends expected in this area of control and monitoring integration technology based on this experience.

INTRODUCTION

Today's microprocessor-based distributed control system (DCS) allow implementation and integration of various plant controls. These systems allow the coordination of plant control and monitoring functions from a centralized operator interface. Also these systems allow new power plant control philosophies to be established which are dependent upon reability, operability, maintainability, and economic factors. Power plant control concepts are not constrained, as in the past, by the application of today's control system technology. This paper looks at new power plant control designs possible with today's technology, investigates the potential designs of plant-wide control system integration, suggests alternatives that can improve the reliability of the design, and reviews the type and format of information required by the plant operator. This paper also describes the design approach, review, and implementation process that are utilized in developing a plant-wide integrated control system within the economic constraints of today's projects. Future trends expected in this area of control and monitoring integration technology are also summarized.

DCS CONCEPT

A DCS offers effective solutions for the main aspects of automation:

• Process control -with a range of controllers, ranging from a small PLC units to advanced controllers for logic and regulatory control.

• Operator interaction -by means of man-machine interfacing equipment, ranging from low-cost, monochrome video terminals, to advanced operator stations featuring the latest technology in user interfacing: high-resolution color graphics and windowing based on industry standards, such as X Window System. • Engineering -is supported by engineering stations and software packages for tasks such as configuration, documentation and fault tracking. Off-line engineering as well as on-line configuration are supported. • Information management -is supported by powerful functions providing the right information for decisionmaking. An open platform allows for inclusion of standard for production planning, optimizing control, or administration. • Communication -features fieldbuses as well as wide and local area networks, with a set of data links, and communication stations for connection to external computers and communication networks, as well as connecting control networks to the plant network. The following key words sumarize the DCS philosophy: • Integration -functional, structural and informational integration. • Distribution -the system consists of controllers, OS/ES stations and IMS stations. The real -time process dadabase is entirely distributed to the controllers. These are functionally complete and able to perform logic control, regulatory control and advanced calculations. The control system can be structured according to the process requirements without restrictions imposed by limitations in the control system. Geographical distribution is catered for by a fieldbus, to which distributed I/O units, controllers and variable-speed drives can be connected. In order to extend the control network over long distances, communication bridges, radio links and even satellites can be employed. • Reliability -in some cases, extremely high availability is required in parts of, or the entire control system. To meet such requirements, critical parts at all levels can be duplicated.

FUNCTIONS AND ARCHITECTURE FOR DCS

Functions

The DCS is a fully integrated, microprocessor-based, distributed control system, which serves as the main plant control and data acquisition system. Acording to the international concepts the DCS must fullfill the following functions [ The DCS interfaces with these control systems to allow main control room operator control and monitoring of the respective systems: • turbine EHC system • sootblower control system • coal handling system • wastewater treatment system

Architecture

The DCS architcture consist of a redundant data highway with 'drops' connected to it that perform specific functions. These drops are located in the electrical equipment room, main control room, or computer room, depending on function. The functionally distributed and redundant processing units (DPUs) are centrally located in the electrical equipment room, each unit is programmed to perform specific tasks for the plant control and data acquisition applications assigned to it. Operator interface to the system is via VDU and keyboard operator consoles located in the main control room with limited hardwired backup provided. The DCS interfaces with several personal-computer-based plant monitoring systems to allow importing of status and alarm information from the DCS to the plant monitoring system:

• Unit efficiency monitoring system • Chemical Analysis Monitor The DCS is furnished with the main control console center located in the main control room. Touchscreen function is used to select an item on the screen. Each operator console contains battery-backed random-access memory (RAM) for storage of the graphics. Each console can access and control all plant functions; however, dedicated graphics are preprogramed at each console. The engineer's console provides all software engineering tools and hardware devices required to configure and maintain the DCS program. The logger drop collects, formats, and prints process data in custom periodic, trip, and sequence-of-events logs. The historical storage drop collects process data from the data highway for trending and transfers the information to the magnetic tape unit for long term storage. The universal programmable controller interface (UCPI) is a programmable protocol gateway that networks host programmable controller-based plant control systems to the DCS data highway. The station interfaceunit (SIU) is a programmable protocol gateway, similar to the UPCI; however, it networks non-PLC host devices to the DCS data highway. Power Plant Control philosophy provide two architectural concepts: "athomic" and "molecular". We use the "athomic" concept in the situation of Turbine control and "molecular" concept in the case of control functions achieved by DPU and PLC. It is important to use the "atomic" concept in case of control critical systems from the response time point of view. A block diagram of the DCS is represented in Figure 1.

DPU functional partittioning

To maximize the capability of the DCS DPUs and enhance the reliability of the control system, control and data acquisition functions performed by the DCS were distributed among several DPUs. It controls were partitioning by first defining the control loops and services to be controlled by the DCS. Logical control groups were then developed, based on plant control application and the design of the mechanical system.

Alarming

The DCS is the primary system for monitoring plant process variables and conditions and for alarming abnormal situations. As a backup system to the DCS, a hardwired visual annunciator system is provided. All DCS alarms are assigned a priority level from 0 to 3 based on their critically to the process and the required operator reaction time to alarm. Annunciator inputs are derived from independent field contacts or DCS generated digital outputs. If derived from a field contact a parallel field is monitored by the DCS.

COMPLEX ADAPTIVE SYSTEMS AS MODELS FOR DISTRIBUTED CONTROL

Recently, the succes of Complex Adaptive Systems (CAS) researchers in using intelligent agent simulations, with some form of self-modifiability, to model natural processes has led engineers to apply these same methods to the control of manmade systems whose complexity would otherwise lead to exponentially increasing computational demands. The simplest kind of DCS would combine remote sensors and actuators to form regulators, and adjust their set points or biases with signals from a central location. Replacing the regulators with conventional feedback or PID controllers can improve their response to changing local conditions, but as these distributed controllers increase in number or the interaction among their functions intensifies, it becomes extremely difficult to supervise and coordinate their switching points.

As complexity increase and decision-making approaches real-time, the computational burden on the central computer increase, until massively parallel processing is required. But even before this point is reached, there can be a significant benefit in distributing both the computational load and the detailed decision-making among the local sensors and actuators, which have both the data and the ability to act on it. Mathematical and computational techniques of CAS provide new tools for the engineering design of distributed control so that both centralized decision-making and the communication burden it creates can be minimized. The basic approach to analyzing a CAS is to model its components as independent adaptive "agents" -partly cooperating and partly competing with each other in their local operations while pursuing global set by a minimal supervisory function.

The study has found that, given the capability for merely rudimentary intelligent behavior, the agent will selforganize in a way that benefits the CAS even as it benefits them individualy [START_REF] Foouad | Non-Linear Dynamic Behavior of Modern Power Networks[END_REF]. To use this method for DCS of a complex plant system, the designer (1) creates a "fitness function" for the agents that represents required goals and constraints for the plant being controlled, and (2) includes in the design of the agents themselves a sufficient variety of resources and strategies from which they can select combinations to use that plant even as the uncontrollable parameters affecting the plant change.

The agents "evolve" by "adapting" their behavior in both competitive and cooperative ways to meet general goals which are assigned by the designer but whose details may also change through the same "evolutionary" process [START_REF] Foouad | Non-Linear Dynamic Behavior of Modern Power Networks[END_REF].

In the context of a DCS , each individual agent represents a local controller, comprising one or more sensors and/or actuators, a microprocessor with some memory and twoway communications. This configuratioon could be created by adding some computational power to what would otherwise be a simple regulator or a PID controller with remotely adjustable bias, set point and/or gain. Conceptually, the added computational power would came from the distribution of the processors and the memory in a central computer made up parallel processor [START_REF] Margolus | Ultimate Computers[END_REF].

It is better to design a control system that can adapt its own behavior reasonably and safety, if not always optimally.

Given the time to act, experienced human operators do this remarcably well. Distributed intelligent control with adaptive agents can provide the same robust response to as fast-changing process in real-time.

PLANT CONTROL SYSTEM DESIGN PHILOSOPHY

Initially all plant systems were reviewed from (1) type of control system, (2) method of primary control and logic implementation, (3) type of operator interface and (4) application and degree of local control capability. For each plant system, the type of control system to be utilized was determined based on the control logic requirements of the system and the location of the primary control system operator interface [START_REF] Wood | Power Generation, Operation and Control[END_REF]. Several scenarios are possible:

• DCS control and DCS VDU-operator interface • Non-PLC control system and DCS VDU operator interface • PLC control and DCS VDU operator interface • PLC control and DCS/local VDU operator interface • PLC control and local VDU/control panel operator interface. For example, all boiler control, burner management and digital controls can be designated to be performed by DCS. Several plant systems such sootblowing system, coal handling and wastewater tretment system utilize PLC control. The sequential digital logic, normally required for these systems, is well suited for PLC control, and be economically advantageous over DCS.

PLANT CONTROL SYSTEM AND DCS INTERFACE

To provide the operator with control and monitoring capability for system that utilized control system independent of the DCS, datalinks were established between the DCS and host control systems. The datalink gateway to the DCS was configured using the universal programmable control interface (UPCI) or the station interface unit (SIU) drop as described previously. Process values and messages from the host system and those developed by the DCS for transfer to the host device are presented as operating information for display on DCS OS/ES stations. The UPCI turns the existing host data acquisition, or process device into a DCS drop. Any data value originated by the host device can be selected to be broadcast over the DCS data highway. We notice at the communication level the tendency of using different buses under a common standard: in the case of fieldbus -PROFIBUS, and in the case of Plant Network -ETHERNET.

Turbine -EHC

The turbine EHC is an example of an non-PLC control system connected to the DCS as shown in Figure 2. The primary purpose of establishing this datalink was to graphically emulate the EHC control panel installed on the main control console center. Turbine control via DCS operator stations was deemed necessary to maintain the integrated plant control philosophy. The DCS performs all the turbine prestart check logic before allowing the turbine ECH to initiate the auto startup mode.

Coal Handling

The coal handling system are controlled by PLC. Coal handling conists of transporting from the coal pile to the coal crushers and transport to the coal silos in the boiler room. Control of coal handling is not considered the responsibility of the main control room operator, and therefore, only local control of this system is provided. Figure 3.

Sootblowing System

The sootblowing system is controlled by a PLC, which is located, together with the PLC I/O rack, in the plant electrical equipment room.

Water Treatment System

One last example of control systems integration consists of processes that are controlled only at the equipment. The condensate polisher and makeup demineralizer are PLC systems with local operator consoles as shown in Figure 4.

GRAPHICAL OPERATOR INTERFACE DESIGN

The design, configuration, and testing of DCS graphic displays were considered the most important design of the DCS. The design process for the DCS graphic displays included the following steps:

• organize plant systems into groups and subgroups • develop initial plant system envelopes and display layouts • apply preapproved design concepts to develop complete and coded graphics • configure and test graphics using an actual database.

GRAPHIC DISPLAY DESIGN

Standardized design concepts has to be developed before starting the graphic displays. The design criteria established uniformity in the format of the graphic. The more important aspects of graphic display design are described below:

• screen organisation -the basic screen is subdivided into two regions: the main screen and the subscreen (with information or messages such alarms) • process value display -represented both quantitatively and qualitatively. • color coding -were used to separate esential (device status or parameter) and nonesentialy (static devices) information.

FUTURE TRENDS

The design of a DCS system is influenced by the following factors:

• plant operating procedures • reability issues • project cost factors • state-of-the-art technologies.

In applying these state-of-the-art design to the plant control system, reliability of the design is of major concern and steps were taken to ensure that the plant availability will not be compromised.

As with any new design, maintaing project costs within the established engineering, design, construction, and testing budges are also importance. Using CAS as models for DCS would allow a fully automated management of future power plant in real time.

CONSIDERATIONS

This concept of control system is a forward step in the design of integrated plant control systems. This concept is based on the authors experience in studying, design and implementation of several DCS. A vision of future trends in power plant control system design can be described by reviewing the following issues:

• plant control system integration -the ideal power plant control and information system design is to connect all plant systems to a common data highway. With the establishment of a global database, all plant operation and maintenance can be performed from elements connected to this data highway • microprocessor PLC system -PLC will continue to perform plant control functions for specific functions where PLCs are most beneficial. These include systems that are sequentially controlled and operated (digital logic) and have minimal requirements for modulating control and analog process measurements. • geographical distribution of Processor/remote I/Oplacing the control processors and/or I/O within the plant at the process to minimize cabling is a costeffective approach and is now a common design practice. • operator interface design -OS stations will improve with graphic capabilities with expanding technologies in VDU design. • advanced control technologies -such multivariable control algorithms, self-tuning controllers and integration of diagnostics and predictive maintenenance information are presently being used and will continue to be practical, considerations in the future distributed control system design.

• in combination with explorations of intelligent sensors and fast control devices, a fully automated management of power plant in real time could be provide. This system will be: -measured by localy authonomous intelligent sensors -modeled as a hierarchy of competing and cooperating adaptive agents -computing in parallel, distributed in space -autoomatically controlling local operations, guided by global optimization criteria.

Fig. 1 -

 1 Fig. 1 -Distributed Control System Block Diagram

Fig. 3 .

 3 Fig. 3. PLC Control and DCS VDU/Local Control Panel Operator Interface

Fig. 4 -

 4 Fig. 4 -PLC Control and Local Operator Interface

  In most of the cases there are control systems which are delivered by the supplier or are realised with PLCs.

	1]:
	-operative functions: operating, monitoring, closed loop
	control, open loop control, protection, operating guide
	-quasi-operative functions: configuring, diagnosis,
	maintenance
	-non-operative functions: engineering (including
	docummentation), logging (protocols), archiving.
	The DCS performs the following functions:
	• boiler control
	• burner management
	• motor and solenoid valve control
	• boiler, turbine-generator, and balance-of-plant data
	acquisition
	• based alarming
	• logging, including sequence of events
	• historical storage


Angelo Furno

Razvan Stanica

Marco Fiore
email: marco.fiore@ieiit.cnr.it

A Comparative Evaluation of Urban Fabric Detection Techniques Based on Mobile Traffic Data

Keywords: mobile traffic data, time series analysis, urban fabric detection, land use classification

Mobile traffic data has been recently used to characterize the urban environment in terms of urban fabric profiles. While showing promising results, the existing urban fabric detection solutions are built without a clear understanding of the detection process chain. In this paper, we distinguish and analyze the different steps common to all urban profiling techniques. By evaluating the impact of each step of the process, we are able to propose a new solution that outperforms the state of the art techniques. Our approach uses the weekly periodicity of human activities, as well as a median-based filtering technique, resulting in a better clustering in terms of both coverage and entropy, as shown by results obtained on two large scale mobile traffic datasets covering the urban areas of Milan and Turin, in Italy.

Introduction

The surge of mobile traffic, at a staggering 146% compound annual growth rate during the past decade [START_REF] Cisco | Visual Networking Index -Forecast and Methodology[END_REF], is a clear evidence of the success of mobile communication technologies, with similar trends in developed and developing countries [START_REF]Emerging Nations Embrace Internet, Mobile Technology[END_REF].

The popularity of mobile devices makes the customer base of individual mobile operators a statistically reliable sample group for macroscopic studies across different research disciplines. This has been repeatedly proven in the literature, where data captured by mobile network probes and containing fine-grained information on the activities of millions of individuals have been leveraged for many and varied purposes. Significant examples include the estimation of country-wide population distribution [START_REF] Douglass | High Resolution Population Estimates from Telecommunications Data[END_REF] and mobility patterns [START_REF] Gonzalez | Understanding Individual Human Mobility Patterns[END_REF][START_REF] Calabrese | Estimating Origin-Destination Flows using Mobile Phone Location Data[END_REF], the identification of ethnolinguistic groups [START_REF] Blondel | Fast Unfolding of Communities in Large Networks[END_REF][START_REF] Blumenstock | Neighborhood and Network Segregation: Ethnic Homophily in a Silently Separate Society[END_REF], or the inference of economic level of geographical regions [START_REF] Smith | Ubiquitous Sensing for Mapping Poverty in Developing Countries[END_REF]. We refer the interested reader to the recent surveys in [START_REF] Blondel | A Survey of Results on Mobile Phone Datasets Analysis[END_REF][START_REF] Naboulsi | Large-scale Mobile Traffic Analysis: A Survey[END_REF] for a thorough discussion of these and more topics.

In this paper, we focus on one specific application of mobile traffic analysis, i.e., the detection of urban fabrics. By the term urban fabric we denote the type of infrastructures (e.g., roads, transportations, sports facilities, industrial plants) and human activities (e.g., work, residential, commercial, leisure) that characterize a given metropolitan area: it has thus a larger meaning than traditional land use.

Our work builds on previous studies on related subjects. The existence of a relationship between urban fabrics and the associated mobile traffic was first unveiled by a study in Lisbon, Portugal, which showed that the temporal dynamics of mobile traffic are similar in residential and suburban areas, whereas they are unique in areas including major transport arteries [START_REF] Almeida | Spatial and Temporal Traffic Distribution Models for GSM[END_REF]. Subsequent analyses proved that base stations that serve higher mobile traffic demands at noon, evening or night, respectively, reside in urban areas of diverse nature [START_REF] Trestian | Measuring Serendipity: Connecting People, Locations and Interests in a Mobile 3G Network[END_REF]. Similar conclusions were drawn for mobile traffic hotspots during working days and weekends [START_REF] Vieira | Characterizing Dense Urban Areas from Mobile Phone-Call Data: Discovery and Social Dynamics[END_REF].

The findings above have fostered attempts at detecting urban fabrics from mobile traffic directly. Indeed, an efficient inference technique would have important practical applications. As a matter of fact, traditional approaches to land use detection are expensive and time consuming, which results in imprecise and often outdated databases; an automated detection from mobile traffic data could significantly mitigate the problem.

Two such detection techniques have been proposed to date, in [START_REF] Soto | Automated Land Use Identification using Cell-Phone Records[END_REF] and [START_REF] Cici | On the Decomposition of Cell Phone Activity Patterns and their Connection with Urban Ecology[END_REF], respectively. In this work, we aim at providing a comprehensive evaluation of these approaches, as well as at comparing them with several novel methodologies we propose. To that end, we first present the different urban fabric detection techniques we consider, in Sec. 2. We then introduce in Sec. 3 the two reference scenarios we employ in our performance assessment, each consisting of mobile traffic and ground truth datasets. The results of the comparative evaluation are provided and discussed in Sec. 4, and conclusions are drawn in Sec. 5. Ultimately, our study identifies the solution that yields the most accurate detection of urban fabrics in heterogeneous urban scenarios.

Traffic clustering for urban fabric detection

We first report on the two state-of-the art approaches to cluster mobile traffic time series for land-use detection, which we refer to as Soto [START_REF] Soto | Automated Land Use Identification using Cell-Phone Records[END_REF] and Cici [START_REF] Cici | On the Decomposition of Cell Phone Activity Patterns and their Connection with Urban Ecology[END_REF]. We then introduce an original technique for urban fabric detection, based on the computation of the median week signature (MWS). We also detail several variations of the basic MWS approach, which allow further exploring the design space of detection solutions.

In the following, we consider a generic dataset D, describing the communication activity of a set of mobile subscribers for a set of days d = {d}. The mobile activity in D is described as the aggregate of the traffic generated by all users in a same area during a given time interval; the size of the area and duration of the interval determine the spatial and temporal granularity of the dataset, respectively. More precisely, we name unit area the spatial aggregation level: the geographical region a = {a} is thus tessellated into unit areas a, which can map to, e.g. cell sectors, base transceiver stations, or grid elements. The time granularity is instead characterized by the duration of a time slot, i.e., the interval during which user activity is aggregated in each unit area. Each day d ∈ d is thus divided into a set t = {t} of time slots t.

Not only the spatio-temporal granularity, but also the nature of subscriber activity in such a mobile traffic dataset D can vary. It depends on the type of activity that is recorded by the operator, and representative options include, e.g., the number or duration of calls, the number of short text messages (SMS), the volume of Internet traffic, the kind of mobile services consumed by the users. For the specific purpose of land use or urban fabrics detection, voice and texting volumes yield the most significant information, and are thus the type of activity used by solutions in the literature [START_REF] Soto | Automated Land Use Identification using Cell-Phone Records[END_REF][START_REF] Cici | On the Decomposition of Cell Phone Activity Patterns and their Connection with Urban Ecology[END_REF]. Indeed, they are a better indicator of human occupations than Internet traffic, which is much more homogeneous over time due to the applications that continuously run in background. Therefore, in this paper we adhere to the common practice, and consider that the dataset D = {v a (d,t)} contains elements v a (d,t) that describe the total volume of voice and texting activity incoming in and outgoing from each unit area a at time slot t of day d.

As a final remark, we stress that all approaches for urban fabric detection from mobile traffic data rely on the same processing chain, consisting of the following steps.

algorithm is run so as to separate groups of signatures that have similar shapes, i.e., that are representative of equivalent subscriber activities.

Next, we will review existing and novel approaches to urban fabric detection, decomposing them according to the three steps above.

Soto

The Soto approach [START_REF] Soto | Automated Land Use Identification using Cell-Phone Records[END_REF] considers mobile traffic signatures that correspond to the average mobile traffic volume observed during (i) a working day, and (ii) a weekend day. We refer to these as average weekdayweekend signatures. Formally, the set of days d is split into two sets d wd and d we , which contain all Mondays-to-Fridays, and all Saturdays and Sundays, respectively. Then, the element associated to t in the signature of a unit area a is s a (wd,t) = 1

|d wd | ∑ v a (d,t)∈D d∈d wd v a (d,t) (1)
for time slots t during working days, and

s a (we,t) = 1 |d we | ∑ v a (d,t)∈D d∈d we v a (d,t) (2)
for time slots t during weekends. The signature of a is then

s a = d∈d t∈t s a (d,t) . (3)
In (3), d is the condensed set of days, which, in the case of Soto approach, is d = {wd, we}. Also, indicates the concatenation of all elements in a set: s a is thus the concatenation of all elements referring to the average working day and to the average weekend day. Signatures then go through a standard score normalization phase, where each time slot signature obtained in (1) and ( 2) is normalized with respect to the mean and standard deviation of all those referring to the same unit area. Formally, for the signature element of unit area a at time slot t

ŝa (d,t) = s a (d,t) -µ(s a ) σ(s a ) , (4)
where d ∈ d = {wd, we}, whereas µ(s a ) and σ(s a ) denote the mean and standard deviation of the set of elements concatenated in the signature s a . Then, the normalized signature ŝa is simply obtained by concatenation of ŝa (d,t) for all d ∈ d and t ∈ t, as in [START_REF] Douglass | High Resolution Population Estimates from Telecommunications Data[END_REF].

As far as distances between signatures are concerned, Soto considers the Euclidean distance between the corresponding ordered vectors. Given the signatures of two unit areas a and b, their distance is

d ab = ∑ d∈d ∑ t∈t ( ŝa (d,t) -ŝb (d,t)) 2 , ( 5
)
where d is always the same condensed day set {wd, we}.

Finally, the clustering of signatures is performed in Soto by running a k-means algorithm over the set of ŝa , a ∈ a, using (5) as the k-means distance measure. The algorithm requires the parametrization of k, i.e., the desired number of clusters: in Soto, k is selected according to the validity index proposed in [START_REF] Ray | Determination of Number of Clusters in k-means Clustering and Application in Colour Image Segmentation[END_REF]. In their considered dataset, the best results are obtained with k=5.

Cici

The Cici solution [START_REF] Cici | On the Decomposition of Cell Phone Activity Patterns and their Connection with Urban Ecology[END_REF] consider a whole-time-series signature for each unit area. In other words, the signature of area a is

s a = d∈d t∈t s a (d,t) , (6)
and the number of elements that compose it is not bounded, but depends on the timespan of the dataset D. In addition, Cici applies a Fast Fourier Transform (FFT) to the signature above, so as to clean it from irregular patterns. Specifically, once converted to the frequency domain with FFT, only the highest power frequencies are kept, and the time signal is reconstructed with and Inverse FFT (IFFT) from the selected frequencies.

The filtering returns the Seasonal Communication Series (SCS) of the original signature. Normalization of whole-time-series SCS-filtered signatures is then performed using the standard-score approach in (4). However, in the case of Cici, d ∈ d, since signatures do not condense days, but include the full time series. The similarity between two such signatures is computed in Cici using the Pearson correlation coefficient, which, for two unit areas a and b, is computed as

p ab = ∑ d∈d ∑ t∈t (s a (d,t)-µ(s a ))(s b (d,t)-µ(s b )) ∑ d∈d ∑ t∈t (s a (d,t)-µ(s a )) 2 • ∑ d∈d ∑ t∈t (s b (d,t)-µ(s b )) 2 . ( 7
)
In the case of Cici, there is no day condensation and all days are considered separately in the signature, thus d = d in [START_REF] Smith | Ubiquitous Sensing for Mapping Poverty in Developing Countries[END_REF]. The distance measure is based on [START_REF] Smith | Ubiquitous Sensing for Mapping Poverty in Developing Countries[END_REF] and defined as

d ab = 1 -p ab (8)
Concerning clustering of signatures, Cici adopts an agglomerative hierarchical clustering, i.e., the linkage clustering with average distance criterion. This hierarchical clustering outputs a whole family of solutions that can be expressed through a dendrogram: it thus returns a richer information than a single clustering solution, as in the case of, e.g., k-means. However, this also implies that some criterion must be adopted to select the best clustering among all those in the family. To that end, Cici evaluates the skewness of the cluster sizes at the different levels of the dendrogram build by the hierarchical clustering: selecting the level with minimum skewness allows grouping unit areas into clusters of relatively comparable sizes.

It is important to note that, by using the lowest-skewness criterion, the number of generated clusters can be high, in the order of hundreds. Since this makes the analysis cumbersome, Cici limits the analysis to the 10 largest clusters, which are expected to represent the most relevant urban fabrics in the considered region.

MWS: clustering the median week signatures

The Soto approach is based on a signature definition that is very compact but omits too much information when confronted to the original data [START_REF] Cici | On the Decomposition of Cell Phone Activity Patterns and their Connection with Urban Ecology[END_REF]. On the other hand, the signature employed in Cici has a number of elements equal to that of the original time series, and, intuitively, is a loss-less representation of the same. However, when considering months of mobile traffic activity, clustering Cici signatures incurs into the wellknown curse of dimensionality [START_REF] Hinneburg | Optimal Grid-Clustering: Towards Breaking the Curse of Dimensionality in High-Dimensional Clustering[END_REF], and is in all cases very expensive from a computational standpoint.

We thus present a novel signature model that aims at combining the advantages of Soto and Cici signatures, while overcoming their limitations. Our median week signature (MWS) is based on two considerations.

• First, it has been repeatedly shown that there exists a strong weekly periodicity in human occupations [START_REF] Zang | Mining Call and Mobility Data to Improve Paging Efficiency in Cellular Networks[END_REF][START_REF] Calabrese | Estimating Origin-Destination Flows using Mobile Phone Location Data[END_REF], which implies that most of the diversity in mobile traffic activity occurs within a one-week period. We thus speculate that a signature describing the typical weekly behavior of the mobile demand at one unit area contains the vast majority of the significant information about the nature of that area. This allows defining a compact, week-long signature that avoids the dimensionality problems of the Cici model, and does not lose important knowledge as in Soto.

Tab • Second, we deem the median to be a more reliable statistical measure than others used in Soto or Cici (e.g., the average or the absolute values), when it comes to assessing the typical activity in mobile traffic. As a matter of fact, the median is much more robust to outliers, which are frequent in mobile traffic due to special events of social, political, sports, or cultural nature [START_REF] Shafiq | A First Look at Cellular Network Performance during Crowded Events[END_REF][START_REF] Paraskevopoulos | Identification and Characterization of Human Behavior Patterns from Mobile Phone Data[END_REF][START_REF] Naboulsi | Classifying Call Profiles in Large-scale Mobile Traffic Datasets[END_REF].

The MWS is computed according to these guidelines, as follows. The whole set of days d is divided into seven sets, each containing elements of the dataset D that refer to one day of the week, from Monday to Sunday. In other words,

d mon ∪ d tue ∪ d wed ∪ d thu ∪ d f ri ∪ d sat ∪ d sun = d.
Then, the element associated to time slot t in the signature of unit area a is

s a (mon,t) = µ 1/2 ({v a (d,t) | d ∈ d mon }) , (9)
for time slots t corresponding to Mondays, and equivalently for all other days. In (9), µ 1/2 (•) represents the median of the set within parenthesis. Then, the MWS is defined as the concatenation in [START_REF] Douglass | High Resolution Population Estimates from Telecommunications Data[END_REF], where d = {mon,tue, wed,thu, f ri, sat, sun} is the condensed set of days. Taking the MWS model as a pivot, we explore the design space of a complete solution for urban fabrics detection, as discussed next.

First, we assess the impact of SCS filtering, proposed in [START_REF] Cici | On the Decomposition of Cell Phone Activity Patterns and their Connection with Urban Ecology[END_REF] by considering both the case where the MWS is passed through the FFT/IFFT procedure described in Sec. 2.2, and the case where MWS is used as is.

Second, we evaluate two different techniques to normalize MWS. One option is the standard score normalization introduced above; in this case, MWS are normalized according to [START_REF] Gonzalez | Understanding Individual Human Mobility Patterns[END_REF], where d = {mon,tue, wed, thu, f ri, sat, sun}. The other option is daily normalization, where the signature element of unit area a at time slot t ŝa (d,t) = s a (d,t)

∑ t∈t s a (d,t) , (10)
where again d ∈ d = {mon,tue, wed,thu, f ri, sat, sun}. Thus, daily normalization normalizes each element with respect to the total activity during the weekday the element belongs to. Third, we combine MWS with both distance measures used in Soto and Cici, i.e., the Euclidean distance in [START_REF] Blondel | Fast Unfolding of Communities in Large Networks[END_REF] and the distance in [START_REF] Blondel | A Survey of Results on Mobile Phone Datasets Analysis[END_REF] based on the Pearson correlation coefficient; in both cases d ∈ d = {mon,tue, wed,thu, f ri, sat, sun}.

Finally, signature clustering is performed as in Cici, using the agglomerative hierarchical algorithm described in Sec. 2.2.

A summary of the different solutions we test in Sec. 4 is provided in Tab. 1. We stress that we test Soto with both k=5 and k=10 since the former value is that originally used in [START_REF] Soto | Automated Land Use Identification using Cell-Phone Records[END_REF] and the second is aligned with the number of large clusters considered by all of the other solutions.

Datasets

In order to evaluate the different approaches described in Sec. 2 we consider two citywide case studies, employing datasets provided by Telecom Italia Mobile (TIM) as part of its Big Data Challenge initiative, in the two editions of 2014 [START_REF]Telecom Italia Big Data Challenge[END_REF] and 2015 [START_REF]Telecom Italia Big Data Challenge[END_REF].

Milan, 2013

The first dataset is referred as Milan-2013, and describes the communication activity of TIM subscribers in the conurbation of Milan, Italy, for a two-month period in November and December 2013. The dataset differentiates among incoming and outgoing calls, providing information about their number and duration.

The dataset also contains the number of received and sent SMS and amount of Internet traffic. Traffic load information are provided every 10 minutes and reported with respect to a tessellation of the surface of the city of Milan in cells. Each cell has a 235 × 235 m 2 size, i.e., an area of 0.055 Km 2 , and maps to a unit area in our analysis. We consider an area of approximately 150 Km 2 containing 2726 cells of the tessellation in the following. The Milan-2013 dataset is the same used in [START_REF] Cici | On the Decomposition of Cell Phone Activity Patterns and their Connection with Urban Ecology[END_REF] for the evaluation of the Cici approach with respect to Soto. In order to perform a fair comparison, we focused on the same subset of cell-phone activity as in [START_REF] Cici | On the Decomposition of Cell Phone Activity Patterns and their Connection with Urban Ecology[END_REF]: this relates to a 4-week period ranging from November 4 th , 2013 to December 1 st , 2013, where the available data related to incoming/outgoing calls and incoming/outgoing SMS is summed up, so as to avoid data sparsity, and according to the fact that they have the same scale and are comparable.

We also considered the same ground-truth information † used in [START_REF] Cici | On the Decomposition of Cell Phone Activity Patterns and their Connection with Urban Ecology[END_REF], which was retrieved from publicly available databases [START_REF]Milan open data[END_REF]. Such ground-truth data conveys information on urban infrastructures and land uses that can be associated to different kinds of human activities. Specifically, it reports, for each unit area, the number of business activities, sport centers, universities, schools and bus stops, as well as the percentage of green area covering the area and the number of people living within it. Fig. 1a shows a map of Milan, including the set of analyzed unit areas, together with markers for university, green areas and business presence.

Turin, 2015

The second dataset, named Turin-2015, describes the mobile traffic activity in March and April 2015 for the city of Turin, Italy. Also in this case, a tessellation of the city is provided by the operator in order to describe communication activity per cell. Differently from the Milan dataset, traffic load is provided every 15 minutes and cells present heterogeneous sizes. In our analysis, we considered an area of approximately 150 Km 2 , as for Milan, containing 261 cells, i.e., unit areas, with variable size, ranging from a minimum of 255 × 325 m 2 to a maximum of 2 × 2.5 Km 2 . As a traffic load measure we considered the sum of incoming † http://odysseas.calit2.uci.edu/doku.php?id=public:urbanecology. and outgoing calls and SMS, as in the Milan scenario. For the sake of consistency with the Milan case, we limited our analysis to one month of communication data, by considering a 4-week period ranging from the 1 st to 28 th of March 2015.

We built ground truth data for each of unit area in Turin-2015 using the open data published by the local municipality [START_REF]Turin open data[END_REF]. We selected information related to the latitude-longitude coordinates of schools, universities, and business activities and, as in [START_REF] Cici | On the Decomposition of Cell Phone Activity Patterns and their Connection with Urban Ecology[END_REF], we associated them to individual unit areas. We also leveraged open data on green areas and population distribution in order to determine their presence in each unit area ‡ . Fig. 1b shows the considered unit areas for the city of Turin, together with a representation of some ground truth data, related to universities, business activities and green areas.

Results

In this section, we provide a complete comparative evaluation of the techniques described in Sec. 2 on the reference datasets in Sec. 3. First, we present the metrics we consider in order to assess the quality of the urban fabric detection, and then show the results we obtain.

Metrics

We evaluate the quality of the unit area classification with respect to the available ground-truth data according to the following metrics, borrowed in part from [START_REF] Cici | On the Decomposition of Cell Phone Activity Patterns and their Connection with Urban Ecology[END_REF].

Density. The density D G (c, c) is a measure of the frequency of ground-truth elements of a given class G within a cluster c ∈ c, where c is the set of clusters determined by the current urban fabrics detection approach. Let us define as k G the set of elements of class G (e.g., the set of universities) in the ground-truth data; also, 1 c (k) is an indicator function that is one if a ground-truth element k ∈ k G ends up in unit areas belonging to cluster c, and zero otherwise. Formally, the density is then defined, for a given clustering c as

D G (c, c) = 1 |c| ∑ k∈k G 1 c (k), (11)
where |c| denotes the size of the cluster c ∈ c, i.e., the number of unit areas it includes. The density allows comparing different clusters for a same class G, so as to understand in which clusters elements of G are more frequent.

Entropy. The entropy H G (c) associated to a ground-truth class G for a given clustering c allows estimating the dispersion of G across the clusters defined by c. It is defined as [START_REF] Manning | Introduction to Information Retrieval[END_REF]

H G (c) = -∑ c∈c P G (c) log P G (c). (12)
In ( 12), P G (c) is the probability that a ground-truth element of class G falls into cluster c, i.e.,

P G (c) = 1 |k G | ∑ k∈k G 1 c (k). ( 13
)
Lower entropy is thus an indicator of a less random, i.e., more precise, assignment of ground-truth data of a given class to clusters defined by the detection strategy.

Coverage. The coverage C G (c) of G-class elements for a clustering c is defined as the percentage of ground-truth elements of class G included within those clusters of c that are the most relevant to G.

C G (c) = ∑ c∈c G P G (c). ( 14
)
‡ Green area and demographic data are associated to polygon shapes. This first required the computation of the intersections between the polygons and the unit area cells, and then the assignment to each unit area of an amount of green or population percentage proportional to the intersecting surface. Higher coverage indicates that the ground-truth data for a class G is better matched by those clusters that are deemed considered meaningful for G.

F-score. The F-score index allows determining a single, final score to each detection technique, by combining entropy and coverage for each class G, as follows

F G (c) = 2 • (1 -ĤG (c)) •C G (c) (1 -ĤG (c)) +C G (c) , (15)
where

ĤG (c) = H G (c) log(|c|)
is the normalized Shannon entropy. The F-score index ranges in [0, 1], with 1

indicating the best performance achievable by the given cluster set, with respect to ground truth class G.

Performance evaluation

We begin by comparing the first five urban fabrics detection solutions reported in Tab. 1. Fig. 2 and Fig. 3 thus provide results in terms of entropy, coverage and F-score for the state-of-the-art approaches of Soto-5, Soto-10, and Cici as well as for our proposed MWS-stdscr-pearson and MWS-stdscr-euclidean approaches. The two groups of plots refer to the cases of Milan-2013 and Turin-2015 datasets, respectively. As shown in Fig. 2a, regardless of the specific distance measure used, solutions based on the MWS model attain a significantly lower entropy than solutions based on the other signatures proposed by Soto and Cici. As mentioned in Sec. 4.1, this indicates a reduced level of randomness, and thus a more precise classification of unit areas with respect to the ground-truth data for Milan-2013. Also, the increased accuracy does not come at a cost in terms of coverage, in Fig. 2b. In fact, the entropy gain granted by MWS is associated to an increase in coverage, thus proving the higher effectiveness of a median-week representation of mobile traffic signatures of unit areas.

The result above is summarized in Fig. 2c, which depicts the F-score that considers both entropy and coverage. As one can expect, the F-score further evidences that solutions based on MWS improve current state-of-the-art techniques in urban fabrics detection: the advantage in terms of F-score constantly ranges between 10% and 15%.

The conclusions above are not specific to the Milan-2013 case study. Fig 3 shows that they hold also for the Turin-2015 dataset. The most notable difference emerging when comparing the two scenarios is observed in Fig. 3a: there Soto-5 outperforms all other approaches in terms of entropy. However, such entropy values are related to the lower number of classes obtained by this solution compared with all the other approaches (5 instead of 10): this introduces a bias in favor of Soto-5, since the calculation in ( 12) is a sum over all clusters. Indeed, when entropy values are normalized with respect to the number of clusters, as for the F-score in [START_REF] Ray | Determination of Number of Clusters in k-means Clustering and Application in Colour Image Segmentation[END_REF], approaches based on MWS still emerge as clear winners, with the only exception of the universities class, where Soto-10 gives the best results.

Having substantiated the advantage brought by MWS, an important result from Fig. 2 and Fig. 3 is the performance comparison between MWS-stdscr-pearson and MWS-stdscr-euclidean. Both solutions employ MWS, but they use different signature distance measures, i.e., Pearson correlation and Euclidean, respectively. Results show that the Pearson correlation distance achieves slightly better and more stable (see, in particular, the green ground-truth class in the Milan-2013 case) results with respect to Euclidean distance. This holds in both mobile traffic scenarios.

We further explore the design space of urban fabrics detection solutions, by testing the impact of a different normalization approach. Fig. 4a and Fig. 4b show the performance of the current state-of-the-art approaches of Soto-5, Soto-10, and Cici against those attained by the MWS model combined with daily normalization instead of the standard score used before. The two figures refer to the Milan-2013 and Turin-2015 scenarios, respectively. For the sake of brevity, we limit results to the F-score, which is a more comprehensive metric according to the discussion of previous results.

We still remark that MWS-based solutions tend to outperform techniques based on other signature models. In this case, however, slightly better performance is achieved by the MWS-daily-euclidean approach, and thus Euclidean distance appears to work better than Pearson correlation in combination with daily normalization. Again, results are consistent through different urban scenarios.

An ultimate comparison between the schemes that provide the best performance in the previous tests in provided in Fig. 5. There, we include the F-score attained by MWS-stdscr-pearson and MWS-dailyeuclidean, as well as that of Cici, which yielded a much more accurate detection than Soto in general. When confronting the three solutions above, MWS-stdscr-pearson emerges as the preferred approach, although the difference with respect to the other MWS-based technique is not large.

In addition, Fig. 5 that using the FFT/IFFT processing imposed by SCS does not improve, but instead degrades the performance of both MWS-stdscr-pearson and MWS-daily-euclidean. This suggests that the median week already provides a sufficiently de-noised representation of typical mobile traffic observed at a unit area: thus, further attempts at removing noise, e.g., using SCS, only risk to disrupt the embedded information.

In conclusion, we deem MWS-stdscr-pearson to be the best performing solution among those evaluated, and we consider that most of its gain is due to the MWS model it uses to represent unit area signatures. We stress that MWS is an original contribution of our work.

We provide additional results for the MWS-stdscr-pearson approach in Fig. 6, which shows an intuitive idea of the clustering achieved by this technique. We observe how the clusters, denoted by different colors, are spatially localized, i.e., tend to bring together (groups of) unit areas that are nearby. The accuracy is better in the Milan-2013 case, since the finer spatial granularity of cells allows for a more detailed view of mobile traffic activity.

The nature of the clusters is explained by Fig. 7. The figure portrays, for each urban scenario and groundtruth class, one plot showing the density -computed as in (11) -of corresponding elements across the 10 largest clusters. For example, the bottom-right plot in Fig. 7a shows that university campuses are almost all located in the orange cluster.

A detailed analysis of the urban fabrics identified by MWS-stdscr-pearson is however out of the scope of this work, which aimed at identifying the best detection techniques among a wide range of options. This analysis will be part of our future works.

Conclusion

Automatic detection of urban fabric profiles can be of significant interest when studying the evolution of our urban society and planning the shape of future smart cities.

In this paper, we study the usage of mobile traffic datasets to infer this information about the urban landscape. Starting from two state of the art solutions, we distinguish the major algorithmic steps of urban fabric detection techniques: building the mobile traffic signatures of different urban areas, computing the signature distance between these areas and, finally, clustering them to find activity classes.

Our detailed analysis of these solutions underlines their drawbacks, and pushes us to propose a novel approach to overcome these limitations, while still exploiting the strengths of these related studies. Our urban fabric detection technique uses the weekly periodicity of human activities, combined with a robust median-value based filtering, to obtain results much closer to the ground truth data.

This work opens a number of perspectives related to the analysis of urban fabric profiles. We are planning as future work a detailed analysis of the geographical clusters obtained by the proposed solution, using more datasets, possibly from different countries. This would allow us to compare the structure of different cities, and possibly find general patterns, i.e. areas showing a similar mobile usage throughout the world, as well as city-or country-specific patterns, related to cultural and social differences.

Fig. 1 :

 1 Fig. 1: Spatial tessellation into unit areas (i.e., cells in the legend), and ground-truth data for the Milan-2013 (a) and Turin-2015 (b) datasets.

  Specifically, let us define a subset of clusters c G ⊆ c that have higher-than-average density for ground-truth class G, i.e., c G = c ∈ c s.t. D G (c, c) > |k G |/ ∑ c∈c |c| . Then, the coverage is defined as

Fig. 2 :Fig. 3 :

 23 Fig. 2: Milan-2013. Performance comparison among Soto-5, Soto-10, Cici (lowest skewness at 92 clusters), MWSstdscr-pearson (lowest skewness at 63 clusters) and MWS-stdscr-euclidean (lowest skewness at 77 clusters).Soto-10 Soto-5 Cici MWM-stdscr-euclidean MWS-stdscr-pearson

  _ s c h o o ls s e c o n d a r y _ s c h o o ls s p o r t _ c e n t e r s u n iv e r s it ie s

Fig. 4 :

 4 Fig. 4: Milan-2013 (a) and Turin-2015 (b). F-score comparison among Soto-5, Soto-10, Cici (lowest skewness at 92 and 60 clusters), MWS-daily-pearson (lowest skewness at 120 and 60 clusters) and MWS-daily-euclidean (lowest skewness at 103 and 80 clusters).

  evaluates the SCS filtering used by Soto on the MWS-based solutions. It can be noted MWS-scs-stdscr-pearson MWS-scs-daily-euclidean MWS-stdscr-pearson Cici MWS-daily-euclidean b u s _ s t o p s b _ s c h o o ls s e c o n d a r y _ s c h o o ls s p o r t _ c e n t e r s u n iv e r s it ie s

Fig. 5 :

 5 Fig. 5: Milan-2013 (a) and Turin-2015 (b). F-score comparison among Cici, (lowest skewness at 92 and 60 clusters), MWS-stdscr-pearson (lowest skewness at 63 and 33 clusters), MWS-scs-stdscr-pearson (lowest skewness at 110 and 29 clusters), MWS-daily-euclidean (lowest skewness at 103 and 80 clusters), and MWS-scs-daily-euclidean (lowest skewness at 120 and 100 clusters).

Fig. 6 :

 6 Fig. 6: Geographical representation of the 10 largest clusters identified through MWS-stdscr-pearson.

(b) Turin- 2015 Fig. 7 :

 20157 Fig. 7: Per-ground-truth class densities of the 10 largest clusters identified through MWS-stdscr-pearson.

  . 1: Summary of the solutions for urban fabric detection from mobile traffic data.

	Name	Signature	Filtering Normalization Distance	Clustering
	Soto-5	average weekday-weekend -	standard score Euclidean	k-means, k=5
	Soto-10	average weekday-weekend -	standard score Euclidean	k-means, k=10
	Cici	whole time series	SCS	standard score Pearson correlation linkage
	MWS-stdscr-pearson	median week	-	standard score Pearson correlation linkage
	MWS-stdscr-euclidean	median week	-	standard score Euclidean	linkage
	MWS-daily-pearson	median week	-	daily	Pearson correlation linkage
	MWS-daily-euclidean	median week	-	daily	Euclidean	linkage
	MWS-scs-stdscr-pearson median week	SCS	standard score Pearson correlation linkage
	MWS-scs-daily-euclidean median week	SCS	daily	Euclidean	linkage

Mobile traffic signature. First, one defines some representation of the typical mobile traffic observed at a unit area, named a signature. This can map to the complete time series of traffic, or to a summary of this, e.g., a statistical measure or compressed representation. Also, since signatures need to be comparable, they are normalized according to a normalization rule.

Distance between signatures. Second, one computes how similar, or different, each signature is from all other signatures in the dataset. To that end, a signature distance measure is defined.