**4. Experimental results of a pilot project**

#### **4.1. Project overview**

6 Will-be-set-by-IN-TECH

the multiplications that are constantly executed during the energy metering process can be executed very quickly, and measuring energy with these general purpose microcontroller ICs

This technique is similar to the one implemented in hardware level, using the same acquisition methods and digital processing principles. However, using this approach, the system can be customized to operate in ultra-low power, as in the technique developed in [15] to design a battery powered energy meter using the microcontroler MSP430AFE2xx [16]. The system was configured to operate in a 60 Hz AC line and during every period of one second, make the measurements only during 3 cycles and stay sleeping for the others 57 cycles. The system calculates the RMS value of the product *V* × *I* and adds this value a register. Assuming that there is no significant change in the current and voltage during each period of less then one

1s

Awake period < 66,67 ms Sleeping period > 933,33 ms

Establishing mesh networks using wireless and Power Line Communications modems (PLC) concentrators/routers together with smart metering sensors which are connected to the home appliances can provide both accumulated and real-time information of the energy spent in every appliance in a house. Adding actuators to these smart sensors, it is possible to manage remotely the status (on-off) of a few important loads (like clothes dryer, air conditioning) in order to avoid that these loads be turned-on during a peak period. In this case the household has to buy the system, since the information has to be continuously monitored and the energy

Another possibility, which does not involve the costs associated with the ownership of the measurement system, are offered as a service by the utilities or an ESCO (Energy Service Company). The energy monitoring is performed through the installation of low-cost smart metering sensors to the home appliances, and after a sampling period (typically 2 weeks), the meters are read and a diagnosis report of the energy use is prepared by the company that is offering the service. When there is no need for getting real time information, the installation

An example of energy monitoring system of the typical appliances in a home was presented by [19]. This proposed monitoring system presents a limited performance because only electrical appliances which were connected to the outlets could be measured, since the monitoring system could not access the lighting equipment, which represents an important part of the

is extremely easy, since the devices do not need to form a mesh network.

t

can be very competitive when compared to the energy meter ICs.

second, the accumulated value in the register is equal to the energy.

i

**Figure 4.** Awake and sleeping periods

**3. Decentralized measurement methods**

savings will be dependent on this monitoring.

energy spent in many households.

Recently a pilot project, supported by ANEEL (the Brazilian Electrical Energy Regulating Agency) and AES Eletropaulo (the major power electric utility in the State of São Paulo) was developed by the Department of Electronics and Microelectronics of the School of Electrical and Computer Engineering, at the University of Campinas [7]. The objective was to develop and test a prototype of a hybrid (wireless and PLC) intelligent sensor network system which should be able to perform the breakdown of the electricity bill of the customers. The premise was that with detailed information in hands, consumers could understand better how much they spend the electrical energy in every single electrical device in a house and, this would lead to changes in habits and substantial savings in electrical energy.

The developed system was planned to be offered as a service by the ESCOs, so that both customers who want to know better how they use the electrical energy and energy-efficiency programs were potential candidates for using the service. In order to be practical, the developed system had to be simple to install and easy to be deployed in a residence or small business, without requiring any changes in the original electrical wiring. Although the electrical appliances which are connected to the mains outlet could be read using a small module containing an energy meter IC, it was mandatory that the energy spent on lighting should be monitored, and special sensors had to be designed for this application.

The core of the system was designed around a ZigBee wireless sensor network, and is composed by five types of modules: a coordinator, a displaying-processing unit (DPU) and three types of smart energy meters. A PLC module was also implemented, for special cases where the mesh wireless network is difficult to be implemented.

 System modules

The coordinator is the main element, and only one module is required per each mesh network. The coordinator is responsible for storing the information sent by the smart energy

#### 8 Will-be-set-by-IN-TECH 200 Energy Effi ciency – The Innovative Ways for Smart Energy, the Future Towards Modern Utilities Energy Measurement Techniques for Energy Efficiency Programs 9

meters, and also to sustain the ZigBee mesh network. It also measures and stores the AC voltage to accurately calculate the energy from clamp smart meters. Sometimes, due to the characteristics of the local being monitored or due to the building structure/construction, it is difficult to establish the communications between some end devices and the coordinator. If the devices in the network are too far away from each other, a second coordinator can be added to create and maintain a second ZigBee mesh network. The coordinators will communicate via Power Line Communications (PLC) and a master coordinator will concatenate the information of every smart energy meter.

The report has a filter that can show only one type of smart meter, two, or all tree of them. A horizontal graphic bar shows the energy consumption of each electrical device. This graphic shows the energy spent (in KWh) or the energy cost (in this software version in R\$ - Brazilian

Three types of smart energy meters were developed to measure and monitor the energy consumption of all electric devices in a house, without requiring any changes in the original electrical wiring installation. The first one, the power outlet adapter smart meter, is employed to measure and monitor the energy consumption of every device that can be plugged to a power outlet. The home appliance power cord is simply plugged to the smart energy meter

> Power outlet adapter smart meter type A

The outlet adapter meter measures the amount of energy consumed by the appliance it is monitoring, and send this information to the coordinator every five minutes. Because it is connected to the AC mains, it does not need batteries, even in the presence of a long power supply shutdown, since the data is stored in an internal flash memory. It also performs the function of a router in the wireless network, forwarding the information from the others smart

The second smart meter is a clamp smart meter, and it runs on batteries. It was designed to monitor resistive loads that demand currents over 15 A, as it is the case of electrical showers, heaters and other high current loads. The current clamp transforms the ac current in ac voltage proportional to the amplitude of the current, and the circuit of the meter calculates the time integral of the electrical current (in Ah). It is important to remember that only the current is measured and, therefore, the energy has to be calculated. This energy calculation is performed by the network coordinator, that multiplies the data (in Ah) sent by the smart clamp meter by

The lighting smart meter developed also runs on batteries. It is employed to measure and monitor the energy consumption of lighting devices which are not accessible by the clamp meter (for example, fluorescent lights in ceiling mounting fixtures). It uses a photodiode to detect the light status (on or off). When the photodiode detects that a light (or a set of lighting fixtures) was turned on, the microcontroller starts to integrate the amount of time that the light is kept on. The system uses one light smart meter for each lighting set that is controlled by one switch. During the system installation, the nominal power of all lights that are monitored by one photodiode is stored in the microcontroller's memory, and the energy consumed by

meters to the coordinator, if the smart meters cannot talk to the coordinator directly.

the average value of voltage measured by the coordinator's circuit.

the set of lights is then calculated by the software in the DPU.

Power cord

Energy Measurement Techniques for Energy Effi ciency Programs 201

module and the module is connected to the AC power outlet, as shown in Figure 8.

Real, but it can easily be converted to any currency) for each appliance.

Power outlet

**Figure 8.** Power outlet adapter smart meter.

The hybrid communication technology used (ZigBee + PLC) made the system very robust in terms of coverage. However, the PLC is used only to transfer the information from a coordinator to another and since reading a coordinator is a simple and fast process, two (or more) coordinators can be read with the same DPU, eliminating the PLC module and resulting in a lower cost system that can be easily installed.

**Figure 6.** PLC communication connecting two wireless networks.

The DPU is basically a PC that connects to the wireless network to retrieve the information stored in the coordinator. It is also responsible for processing the information according to the configuration file created during the set-up phase(by the person who is installing the system), linking each of the appliances to the IDs of the smart energy meters. After processing, the DPU displays the report in an user-friendly screen, showing the energy spent of items in a user-friendly way, like John's notebook, lighting of dining room, air conditioning of Rose's bedroom, etc.

(a) First tab - Configuration file (b) Second tab - Report

**Figure 7.** DPU running the reporting application.

The report has a filter that can show only one type of smart meter, two, or all tree of them. A horizontal graphic bar shows the energy consumption of each electrical device. This graphic shows the energy spent (in KWh) or the energy cost (in this software version in R\$ - Brazilian Real, but it can easily be converted to any currency) for each appliance.

Three types of smart energy meters were developed to measure and monitor the energy consumption of all electric devices in a house, without requiring any changes in the original electrical wiring installation. The first one, the power outlet adapter smart meter, is employed to measure and monitor the energy consumption of every device that can be plugged to a power outlet. The home appliance power cord is simply plugged to the smart energy meter module and the module is connected to the AC power outlet, as shown in Figure 8.

**Figure 8.** Power outlet adapter smart meter.

8 Will-be-set-by-IN-TECH

meters, and also to sustain the ZigBee mesh network. It also measures and stores the AC voltage to accurately calculate the energy from clamp smart meters. Sometimes, due to the characteristics of the local being monitored or due to the building structure/construction, it is difficult to establish the communications between some end devices and the coordinator. If the devices in the network are too far away from each other, a second coordinator can be added to create and maintain a second ZigBee mesh network. The coordinators will communicate via Power Line Communications (PLC) and a master coordinator will concatenate the information

The hybrid communication technology used (ZigBee + PLC) made the system very robust in terms of coverage. However, the PLC is used only to transfer the information from a coordinator to another and since reading a coordinator is a simple and fast process, two (or more) coordinators can be read with the same DPU, eliminating the PLC module and resulting

> Master Coordinator

ZigBee Network 1 ZigBee Network 2

PLC link

The DPU is basically a PC that connects to the wireless network to retrieve the information stored in the coordinator. It is also responsible for processing the information according to the configuration file created during the set-up phase(by the person who is installing the system), linking each of the appliances to the IDs of the smart energy meters. After processing, the DPU displays the report in an user-friendly screen, showing the energy spent of items in a user-friendly way, like John's notebook, lighting of dining room, air conditioning of Rose's

(a) First tab - Configuration file (b) Second tab - Report

Slave Coordinator

of every smart energy meter.

bedroom, etc.

in a lower cost system that can be easily installed.

DPU

**Figure 7.** DPU running the reporting application.

**Figure 6.** PLC communication connecting two wireless networks.

The outlet adapter meter measures the amount of energy consumed by the appliance it is monitoring, and send this information to the coordinator every five minutes. Because it is connected to the AC mains, it does not need batteries, even in the presence of a long power supply shutdown, since the data is stored in an internal flash memory. It also performs the function of a router in the wireless network, forwarding the information from the others smart meters to the coordinator, if the smart meters cannot talk to the coordinator directly.

The second smart meter is a clamp smart meter, and it runs on batteries. It was designed to monitor resistive loads that demand currents over 15 A, as it is the case of electrical showers, heaters and other high current loads. The current clamp transforms the ac current in ac voltage proportional to the amplitude of the current, and the circuit of the meter calculates the time integral of the electrical current (in Ah). It is important to remember that only the current is measured and, therefore, the energy has to be calculated. This energy calculation is performed by the network coordinator, that multiplies the data (in Ah) sent by the smart clamp meter by the average value of voltage measured by the coordinator's circuit.

The lighting smart meter developed also runs on batteries. It is employed to measure and monitor the energy consumption of lighting devices which are not accessible by the clamp meter (for example, fluorescent lights in ceiling mounting fixtures). It uses a photodiode to detect the light status (on or off). When the photodiode detects that a light (or a set of lighting fixtures) was turned on, the microcontroller starts to integrate the amount of time that the light is kept on. The system uses one light smart meter for each lighting set that is controlled by one switch. During the system installation, the nominal power of all lights that are monitored by one photodiode is stored in the microcontroller's memory, and the energy consumed by the set of lights is then calculated by the software in the DPU.

The system is designed to monitor a house for a period of one month, but it can also be used to monitor a fraction of that time and estimate the monthly cost of the electricity bill based on the acquisition period, for households with very stable and repetitive habits. A period of less than two weeks is not accurate, because some important energy hungry events may be missed, like when there is no need for using the cloth drier machine because the weather was very good in that week.

**Appliance Energy %**

Energy Measurement Techniques for Energy Effi ciency Programs 203

**Appliance Energy %** PC with amplified speakers 10.83% Home Theater 0.00% Electric rice cooker 1.58% Satellite TV receptor 2.57% Treadmill 2.24% PC with wireless dongle 19.40% PC + Wireless router + notebook 18.48% Cordless telephone 0.89% TV LCD 43" (bedroom) 16.71% Microwave 0.56% Lighting 120W (TV room) 0.05% Refrigerator + freezer (side-by-side) 16.36% TV LCD 53" (TV room) 9.00% Lighting 160W (kitchen) 1.30% Lighting 50W (closet) 0.02% **TOTAL 100.00%**

From the results of the field tests it was observed that habits and behavior are responsible for a lot of energy waste. From the results measured in these households it becomes clear that a detailed report with the breakdown of the electricity bill can result in significant electrical

Notebook 0.03% Hair Dryer 0.53% Washer/Dryer Machine 0.72% TV (living room) 11.26% Refrigerator 1 10.37% Microwave 1.04% Alarm clock/radio 12.20% Lighting 50W (over the piano) 0.03% Lighting 50W (bathroom) 0.00% Lighting 50W (kitchen) 22.55% Lighting 50W (office - over the PC) 7.47% Lighting 50W (office) 2.37% Refrigerator 2 15.48% Cordless telephone 12.23% TV (bedroom) 3.72% **TOTAL 100.00%**

**Table 2.** Results measured in a medium size apartment with 3 people

**Table 3.** Results measured in a large house with 3 people

energy savings just by changing habits.

#### **4.2. Field test results**

The system was tested in the field, and the results showed that most of the consumers waste a lot of energy due to habits that could be easily changed without any impact on the comfort or the quality of life.

Table 1 shows the results measured in a very small apartment, with one person who leaves the apartment early in the morning and returns only at 7:00 PM. It can be seen that this household spends 76% of the electricity with only two appliances: a Computer and a VCR. The PC is a high load in this household, with 64% of the monitored energy consumption. After the report on how the energy is used was presented, the resident reported that the PC is a high end device with powerful video cards and was kept on 24/7 just to eliminate the slow boot time. Concerning the VCR (which is responsible for about 12% of the consumption), it was informed that it is a very old model, that is never turned-off and is used only to change the channels in the TV, because the TV's remote control was not working. In this case, the energy savings could reach a level of almost 80% if the computer is turned off when it is not in use and a new TV remote control is bought.

**Table 1.** Results measured in a small apartment with 1 people

The results from another household, a medium size apartment with 3 people, is presented in table 2.

This test found that the resident has an old digital alarm clock/radio (which, obviously, was kept turned on 24/7) that presents a huge energy consumption. If this alarm clock was replace with a new and more economic model, it would pay itself back after a few months and energy savings in the order of 12% would be achieved.

The results measured in another interesting case, a large house with three people, are presented in table 3. This household shows a high energy consumption by computers and televisions. The residents reported that the treadmill was kept in stand by during all the acquisition period, and a TV set on a bedroom is always kept on during the whole night. The PCs are also left on continuously to avoid the slow boot time.

**Table 2.** Results measured in a medium size apartment with 3 people

The system is designed to monitor a house for a period of one month, but it can also be used to monitor a fraction of that time and estimate the monthly cost of the electricity bill based on the acquisition period, for households with very stable and repetitive habits. A period of less than two weeks is not accurate, because some important energy hungry events may be missed, like when there is no need for using the cloth drier machine because the weather was

The system was tested in the field, and the results showed that most of the consumers waste a lot of energy due to habits that could be easily changed without any impact on the comfort

Table 1 shows the results measured in a very small apartment, with one person who leaves the apartment early in the morning and returns only at 7:00 PM. It can be seen that this household spends 76% of the electricity with only two appliances: a Computer and a VCR. The PC is a high load in this household, with 64% of the monitored energy consumption. After the report on how the energy is used was presented, the resident reported that the PC is a high end device with powerful video cards and was kept on 24/7 just to eliminate the slow boot time. Concerning the VCR (which is responsible for about 12% of the consumption), it was informed that it is a very old model, that is never turned-off and is used only to change the channels in the TV, because the TV's remote control was not working. In this case, the energy savings could reach a level of almost 80% if the computer is turned off when it is not in use

> **Appliance Energy %** TV 17.93% Cell Phone + Charger 1.00% VCR 12.71% Refrigerator 3.78% PC 64.00% Light bulb 20W (kitchen) 0.58% **TOTAL** 100.00%

The results from another household, a medium size apartment with 3 people, is presented in

This test found that the resident has an old digital alarm clock/radio (which, obviously, was kept turned on 24/7) that presents a huge energy consumption. If this alarm clock was replace with a new and more economic model, it would pay itself back after a few months and energy

The results measured in another interesting case, a large house with three people, are presented in table 3. This household shows a high energy consumption by computers and televisions. The residents reported that the treadmill was kept in stand by during all the acquisition period, and a TV set on a bedroom is always kept on during the whole night. The

very good in that week.

**4.2. Field test results**

or the quality of life.

table 2.

and a new TV remote control is bought.

**Table 1.** Results measured in a small apartment with 1 people

PCs are also left on continuously to avoid the slow boot time.

savings in the order of 12% would be achieved.

**Table 3.** Results measured in a large house with 3 people

From the results of the field tests it was observed that habits and behavior are responsible for a lot of energy waste. From the results measured in these households it becomes clear that a detailed report with the breakdown of the electricity bill can result in significant electrical energy savings just by changing habits.

#### 12 Will-be-set-by-IN-TECH 204 Energy Effi ciency – The Innovative Ways for Smart Energy, the Future Towards Modern Utilities Energy Measurement Techniques for Energy Efficiency Programs 13

In the case presented in table 3, the electricity bill was monitored during the next six months after the results were presented. The family reported that, after the report was presented, all three PCs were turned-off during the night and the timer of the bedroom TV was set to turn-off the TV set at midnight.In the case of the The monthly savings in the electricity bills during these six months period were over 20%, when compared to the same month of the previous year. This result is a clear evidence that if a customer is informed of a habit that is wasting energy and this habit can be easily changed, a lot of energy can be saved and and this savings tend to be permanent.

two different-sized step changes are clearly present, providing characteristic signatures of the refrigerator and the microwave oven. Knowing the time of each on and off event, it is possible to determine the total energy consumption of the refrigerator and the microwave oven.

> 16 Microwave cycles

Energy Measurement Techniques for Energy Effi ciency Programs 205

Time

A method to construct taxonomy of electrical appliances based on load signatures is presented in [10]. In this work the authors suggests a 2-dimensional form of load signature denominated voltage-current (V-I) trajectory to characterize typical household appliances. The V-I trajectory load signatures consists in acquiring the steady-state voltage and current in one-cycle long, normalize them to eliminate the effect of the current magnitude in the size of V-I trajectory, and then plot the V-I trajectory. After creating the trajectories for the appliances, the shapes of

The proposed methodology for constructing the load taxonomy is summarized as: (1) the voltage and current waveforms of the household appliances are measured; (2) load signatures in the form of V-I trajectory are constructed; (3) shape features are extracted from the V-I trajectories; (4) hierarchical clustering method is applied to cluster the appliances; (5) the load

In [13] the authors proposed a methodology of using load signatures and Genetic Algorithms (GA) to identify electrical appliances from a composite load signal. They introduced a classification method to group the appliances and how to disaggregate the composite load signals by a GA identification process from a generated random combinations of load

The methodology consists of defining a signature for each appliance by averaging 50 consecutive one-cycle steady-state current waveforms. Then the current waveforms are grouped by the ratio of their fundamental (50Hz) component verses their Root-Mean-Square (RMS) total, after a Fast Fourier Transform (FFT) calculation. That means that the higher the

**Figure 9.** Characteristic signatures of a refrigerator and a microwave oven sensed on the same circuit In [12] the authors highlighted the fact that the complex electrical loads of today have signatures that vary with time, depending on their state and mode of use and that common appliances can have non-linear load characteristics. They propose a conceptual modeling to characterize an appliance based on three sets of signatures that are extracted from the appliance: steady state, transient state and operational pattern and therefore construct a

Refrigerator cycle #2

Refrigerator cycle #3

Power consumption

Refrigerator cycle #1

taxonomy for the appliances.

the trajectories of the appliances can be analyzed.

signatures from the groups of appliances.

taxonomy is constructed according to the clustering results.

The measurement system was designed to get the information from every appliance in the house in real time, but the customers reported that they were interested only in the two weeks final result. Thus, the ZigBee network could be removed from the smart meters and a simple peer-to-peer communication (from each smart meter to the DPU) could be used, reducing the power consumption of the measurement system and increasing the batteries' life.


**7. Crossover operator for MSA**

Besides, a special crossover operator is designed to generate a new solution from the current solution and the best solution so far in the rough search based on the representation in section 5.2. The margin and centre of the new solution (child) inherit the margin of the current solution (father) and the reversed centre of the best solution (mother), respectively. The reason to reverse the best solution is to get a different solution even two given solutions are the same solution.

**Figure 11.** An example of two layouts before the crossover operator: the current layout (F: father) and the best layout so far (M: mother)

For the detail of crossover, two sequences *Γ+* and *Γ–* are selected randomly from (*Γ1, Γ2, Γ3*) or (*Γ1, Γ2, Γ3, Γ4*, *Γ5*) for 3D packing. Let us denote the father as (*Γf +*, *Γf –*) which is selected from the current solution. The mother (*Γm+*, *Γm–*) is from the best solution so far. A number *i* is an integer randomly produced between 1 and *k/2*–1. The child of sequence pair (*Γc +*, *Γc –*) is given by *Γf +*[*0, i*] + *Γ'm+* + *Γf +*[(*k*–*i*–1), *k*–1] and *Γf –*[*0, i*] + *Γ'm–* + *Γf –*[(*k*–*i*–1), *k*–1], where *Γ'm+* and *Γ'm–* are the inverse of *Γm+* – *Γf +*[*0, i*] – *Γf +*[(*k*–*i*–1), *k*–1] and the inverse of *Γm–* – *Γf –*[*0, i*] – *Γf –*[(*k*–*i*– 1), *k*–1], respectively.

To make it clearer, let us take an example to explain the crossover operator. As shown in Figure 11, the left layout is represented by *Γ+*(*m3,m4,m2,m1,m5*) and *Γ -*(*m5,m1,m2,m3,m4)* as the father, which is the capital "F" in Figure 12. The right one is *Γ+*(*m1,m3,m4,m2,m5*) and *Γ -* (*m3,m4,m1,m5,m2*) as the mother, which is the reversed capital "M" in Figure 12. If we assume the *i* be 1, the child will be the layout *Γ+*(*m3,m2,m4,m1,m5*) and *Γ -*(*m5,m2,m1,m3,m4*) as the right layout of Figure 12*,* where *Γ+*(*m*3*, ..., m5*) and *Γ -*(*m5, ..., m*4) are from the father as the margin of left picture of Figure 12, and *Γ+*(*...,m2,m4,m1,...*) and *Γ -*(*...,m2,m1,m3,...*) are from mother with an inverse order as the centre of left picture of Figure 12.

**Figure 12.** The layout (child) after the crossover operator between the current layout (F: father) and the best layout so far (M: mother) with an inverse order


**DESCRIPTIVE FOOD SCIENCE**

Edited by **Antonio Valero Díaz** and **Rosa María García-Gimeno**

#### **Descriptive Food Science**

http://dx.doi.org/10.5772/intechopen.73156 Edited by Antonio Valero Díaz and Rosa María García-Gimeno

#### **Contributors**

Alma D. Alarcon-Rojo, Esmeralda Peña-Gonzalez, Iván García-Galicia, Luis Carrillo, Mariana Huerta-Jimenez, Raul Reyes-Villagrana, Hector Janacua-Vidales, Darko Velić, Daniela Amidžić Klarić, Natalija Velić, Ilija Klarić, Vlatka Petravić Tominac, Ana Mornar, Hamed Mirhosseini, Catherine Adley, Niamh Burke, Krzysztof A Zacharski, Paul Hogan, Mark Southern, Michael P Ryan, Poliana Mendes De Souza, Fabiana Regina Lima, Kássia Héllen Vieira, Miriam Aparecida De Aguilar Santos, Annemieke Pustjens, Saskia Van Ruth, Rita Boerrigter-Eenling, Alex Koot, Sara Martillanes, Javier Rocha-Pimienta, Jonathan Delgado-Adámez, Riaz Ahmad, Uzma Latief

#### **© The Editor(s) and the Author(s) 2018**

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Individual chapters of this publication are distributed under the terms of the Creative Commons Attribution 3.0 Unported License which permits commercial use, distribution and reproduction of the individual chapters, provided the original author(s) and source publication are appropriately acknowledged. If so indicated, certain images may not be included under the Creative Commons license. In such cases users will need to obtain permission from the license holder to reproduce the material. More details and guidelines concerning content reuse and adaptation can be foundat http://www.intechopen.com/copyright-policy.html.

#### **Notice**

Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published chapters. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book.

First published in London, United Kingdom, 2018 by IntechOpen eBook (PDF) Published by IntechOpen, 2019 IntechOpen is the global imprint of INTECHOPEN LIMITED, registered in England and Wales, registration number: 11086078, The Shard, 25th floor, 32 London Bridge Street London, SE19SG – United Kingdom Printed in Croatia

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**5. Gamification in cultural spaces**

*'Digital technologies, in particular interactive storytelling and gaming, have a great potential for assisting both the education and entertainment of visitors in museums'. Danks et al. [42]*

In this section, we will consider the ability of gamified systems situated in cultural spaces to elicit the forms of pleasure posited above, with this by extension providing a method to assess the capacity these games offer to motivate and prolong engagement. Testing the hypothesis of Danks et al. above, there is now a growing repository of papers that explore gamification applied to cultural spaces, predominantly museums ([43–47] e.g. with [48, 49] both offering literature reviews). Beyond this, Scheuer [50] offers a useful study relating to the form the vast majority of projects take, this being an 'add-on' game, which augments an existing exhibition

*Right Game, Wrong Place? A Case Study: Using a Gamified AR Application in a Heritage… DOI: http://dx.doi.org/10.5772/intechopen.107535*

rather than being developed as part of it. Furthermore, there is the consideration of purpose when applying these games, which the literature positions as almost universally related to learning, with motivations, when given any attention, generally seen to be derived from the act of learning. This is a position disputed by Martens et al. [51] who see the game as providing pleasure distinct from the wider goal of learning, with that pleasure providing the motivation to engage. However, in order to consider the characteristics of pleasure posited here, a reflection on specific gamified systems which have been, for the most part, directly experienced by the author will be most valuable to our purposes, with due effort made to offer examples in a representative range of forms.

## **5.1 High tea**

The first example is '*High Tea'* (Welcome Collection 2011). This browser-based strategy game was commissioned as part of the web presence for the 2010/11 Welcome Trust exhibition *High Society* with the *'aim of establishing new and meaningful engagement with the themes of the exhibition*' [52] and is still available to play at https://preloaded.com/work/wellcome-collection-high-tea/. In this game, users were asked to take on the role of the nineteenth century opium smuggler, developing a strategy to sell enough opium to the Chinese of the Pearl Basin to fund the purchase of tea to supply the expanding UK market. The clear intention here is to develop a compelling game whose mechanics not only motivate but also educate, with the motivating factor—the accrual of tea—being fundamental to the educational narrative. Exposing the dubious ethics of Victorian England and the historical appetite for narcotic substances—Opium or Caffeine—was key for the curatorial intentions of the wider exhibition.

This is a useful example for two reasons: the first relates to the mechanism of gamification in that the educational goal—the learning that is the primary task—is seamlessly integrated into the mechanics of the game. Points and scores are awarded in the form of currency or products (tea or opium), and levels and outcomes can be attained through the collection and distribution of these, but the process the player undertakes to achieve these goals is based on historical events and interactions. The game itself is thus the lesson. The playable moments that offer pleasure, primarily through achieving the increasingly challenging levels, simultaneously motivate continued engagement and deliver the educational goals of the game itself. The second is the distribution method chosen by the designers, who did not situate this within the gallery space, but made it available *via* online gaming platforms and their own proprietary web presence. This had the dual purpose of both widening, and making international, opportunities to consume, and of allowing players to interact in a context of their choosing, rather than being constrained to play during a visit to a cultural space.


**Author details**

*Antioxidants*

**5. Conclusions**

**Conflict of interests**

**Abbreviations**

There is no conflict of interest.

OS oxidative stress

CKD chronic kidney diseases PD peritoneal dialysis DM diabetes mellitus LPO lipoperoxides 8-IP 8-isoprostanes NO nitric oxide

SOD superoxide dismutase

anion responsible for the increase in lipid peroxidation [21]; however, in the patients included in the study, SOD was found to be significantly increased and TAC decreased in patients undergoing PD with DM and No-DM, which could suggest that peritoneal replacements could purge the systemic buffering levels of the TAC. In a recently reported study, the authors underwent to hemodialysis session in patients with ESRD; they found a significant reduction in TAC according to our findings [33]. The depletion of TAC found in patients undergoing PD is contrary to that reported by other authors in relation to increased levels of these systemic buffers in patients undergoing hemodialysis. [34]. The concentrations of bilirubin, uric acid, and plasma albumin are the main defense in the extracellular fluids generated during the normal metabolism or are ingested by the consumption of dietary products rich in antioxidants [35]. These extracellular antioxidants prevent the reaction of free radicals by sequestering transition metal ions by plasma chelation [36]; the TAC is able to determine these extracellular antioxidants. In the present study, TAC

was found to be significantly consumed in all patients included.

better hemoglobin response during the treatment of anemia [37].

counteract OS with the potential to preserve peritoneal function.

The addition of exogenous antioxidants to the management of patients who are in PD is an incomplete and little studied subject; however, this topic is interesting that is well worth considering in these patients. In a report of the literature, the authors studied the N-acetylcysteine (NAC) which is considered as a potential antioxidant with anti-inflammatory effects for dialysis patients. Vitamin C could play an important role in helping PD patients to use iron for erythropoiesis and achieve

When comparing patients in PD according to the presence or absence of DM, we found imbalance of markers of oxidative stress characterized by increased LPO products, serum decrease of 8-PI, dysregulation of the antioxidant defense system with significant decrease in TAC, and increase in SOD possibly in an attempt to compensate for the state of oxidative stress. In this population, increased levels of triglycerides and VLDL were observed, which favors the appearance of accelerated atherosclerosis with an increase in arterial stiffness, as well as a decrease in NO levels, which favors secondary endothelial dysfunction. These factors contribute to the increase of CVD in PD patients. Supplementation with external antioxidants could be an emerging strategy to

**346**

Leonardo Pazarín-Villaseñor1,2, Julio Alejandro Gutiérrez-Prieto1 , Javier Soto-Vargas1 , Renato Parra-Michel1 , Andrés García-Sanchez3 and Alejandra Guillermina Miranda-Diaz3 \*

1 Universidad de Guadalajara, Centro Universitario de Ciencias de la Salud, Especialidad en Nefrologia, Hospital General Regional Instituto Mexicano del Seguro Social, Guadalajara, Jalisco, Mexico

2 Servicio de Nefrología, División de Medicina Interna, Hospital Civil de Guadalajara "Dr. Juan I Menchaca", Guadalajara, Jalisco, Mexico

3 Departamento de Fisiología, Centro Universitario de Ciencias de la Salud, Universidad de Guadalajara, Guadalajara, Jalisco, Mexico

\*Address all correspondence to<EMAIL_ADDRESS>
© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


*2.4. ANN and Machine Learning Analysis*

In a computational environment, we randomly separated 80% of the data to train the ANN algorithm and 20% to test it. The number of spectral wavelengths (n = 360) was the same for each day. The spectral wavelengths were added as input for the ANN, and one hidden layer with n neurons was considered. A linear activation function was applied in the output layer. We adopted the Adam Optimizer with regularization of a = 0.0001. We used an open-source version of the RapidMiner v. 9.4 software.

To define the best hyperparameters, we performed a cross-validation method by separating our dataset into 10 folds. This separation was stratified and we used only the training dataset (80%). In this approach, one-fold is used to validate the algorithm performance while the remaining folds are used to train the model. The test is repeated until all 10 folds are used individually as validation data. An example of the training curve being adjusted to the 1st measurement day absorbance data is plotted below (Figure 2).

**Figure 2.** Example of the training curve with the difference in accuracy for the artificial neural networks (ANN) model.

We applied a hyperparametrization evaluation and detected that 100 neurons in hidden layers and a maximum number of interactions of 200 presented the ideal configuration without overfitting our model for most of the tests. Finally, we plotted an ROC (receiver operating characteristic) curve to evaluate the comparison between each classification and a confusion matrix of the ANN results. We evaluated the gain ratio and the F-score for each individual wavelength.

To test the robustness of the ANN, we compared it with other traditional machine learning algorithms, such as decision-tree; support vector machine (SVM); random forest (RF); naïve Bayes; and logistic regression. The number of training and testing remained the same. We also performed a hyperparametrization with these algorithms. The criteria for stopping was defined once it did not return in any practical gains for the classification accuracy (%). For this, we considered the individual characteristic of each classifier, like the number of trees, nodes and leaves, number of interactions, function degree, and others.

The decision tree and random forest models provide classification trees that rely on the idea of an overall accuracy improvement by adding the predictions of combined independent predictors [34]. SVM uses a regression approach to find separation lines and can be applied in many cases where there is a distinct margin of separation. Naïve Bayes is a probabilistic classifier that applies the Bayes' theorem with independence assumptions. Lastly, logistic regression is also a regression approach that bases itself in a sigmoid function to model the predicted classes [35].

The metrics used for evaluating the performance of each algorithm was the AUC (area under the curve), overall accuracy, F1-score, precision, and recall. We compared each one of them during the four stages of the spectral response measurement: 14th, 19th, 24th, and 29th days. Both the reflectance and the absorbance values were used separately as input features. The results are presented in the following section.


*2.1. Conventional Algorithm*

Section 2.1.1 briefly introduces two commonly used azimuth estimation algorithms on two hydrophones: The cross-correlation method and cross-spectrum method. In Section 2.1.2, the FDA algorithm in a MIMO radar is briefly introduced.