Structural health monitoring is recognized as a viable solution to increase aviation safety and decrease operating costs enabling a novel maintenance approach based on the actual condition of the airframe, mitigating operating costs induced by scheduled inspections. However, the net benefit is hardly demonstrated, and it is still unclear how the implementation of such an autonomic system can affect performance at aircraft level. To close this gap, this paper presents a systematic analysis where the impact of cost and weight of integrating permanently attached sensors—used for diagnostics- affect the main performance of the aircraft. Through a multidisciplinary aircraft analysis framework, the increment of aircraft operating empty weight is compared with the possible benefits in terms of direct operating costs to identify a breakeven point. Furthermore, the analysis allows to establish a design guideline for structural health monitoring systems returning a safer aircraft without any economic penalties. The results show that the operating costs are lower than those of the reference aircraft up to 4% increase in maximum take-off weight. Paper findings suggest to considering a condition monitoring strategy from the conceptual design stage, since it could maximize the impact of such innovative technology. However, it involves in a design of a brand-new aircraft instead of a modification of an existing one. Structural Health Monitoring (SHM) has been attracting the research community for the last decades in order to enable the design of lighter, safer and even cleaner aircraft. In principle, SHM is expected to avoid or reduce typical accommodations employed during design  and lifetime management (strict scheduled inspection), inducing a cost-effective maintenance . HoweverRecent literature has demonstrated the effectiveness of on-condition approaches for detecting damage  and estiIn this view, the complex composite behavior is accommodated by a hard damage tolerance criterion to ensure safety in every flight condition. As accidental damage is likely to occur, it drives the design of composite structures . It is gImpact induced damage in composite components is only one of the possible causes of airframe failures and probably that one where SHM can have the most promising impact. Indeed, many types of damage are likely to occur in commercial aircraft, such as cracks, bolt loosening, lack of bonding adhesion, and aging degradation . They arIt is worth pointing out that no assumption can be made about condition monitoring maintenance without looking into the reliability characteristics of the specific SHM system. In this view, Cottone et al.  applied Although the breakthrough promised by SHM integration is within the structure and the life cycle management, the advantages remain still ambiguous and limited to an abstract conceptualization. The literature indeed shows a lack of investigations oriented to postulate the effective benefit achievable by implementing any type of structural health management. Such a discussion is indeed limited to a few examples and always related to the implementation of condition-based maintenance. Pattabhiraman et al.  showed tGenerally, the literature has discussed the cost-benefit of integrating real time SHM ,28. HoweDespite the promising advantages, condition-based maintenance is hardly implemented in the airframe lifetime for several reasons, including the lack of accurate cost-benefit analysis, the standardization needed to comply with certification requirements and acceptance, the lack of clear requirements, and the need of tools supporting decision management ,32,33. HThe rest of the paper is organized as follows. The methodologies adopted to calculate costs and benefit of SHM are reported along with basic concepts about the multi-disciplinary analysis approach used to estimate the impact of SHM technology at aircraft level. Then the results are showed and finally discussed before the concluding remarks are summarized in the last section.The Materials and Methods section describes with sufficient detail the calculation of aircraft direct operating costs and aircraft flight performance needed to establish any cost/benefit at aircraft level by implementing SHM. The methodologies adopted to estimate such a performance is detailed also including a statement about aircraft structures to be monitored and the airframe SHM concepts.To provide a means to compare aircraft designs operating costs under a specific set of conditions;To assist airlines and the aircraft manufacturer in assessing the aircraft economic on given routes.In general, each airline has its own methods for estimating operating expenses according to their operation, flight patterns, fleet, and accounting procedures. Standing the wide range of different methods and approaches, during the design process it is required to adopt a standard method for the cost estimation . In a feSeveral methods are available in literature, but all derive from Standard Method of Estimating Comparative Direct Operating Costs of Turbine Powered Transport Airplanes , also knCapital cost: depreciation, insurance, interest;Crew cost: cockpit and cabin;Fuel cost;Maintenance cost: line, base, engine overhaul, aircraft components;Charges: landing, navigation, ground handling, noise, emissions.In the following subsections the equations chosen from the methods described above for the calculation of the DOC elements are reported. As schematically illustrated in Depreciation of the initial investment, that is the allocation of aircraft’s acquisition cost over a certain period;Interest charges on capital employed;Aircraft and passengers’ insurance.As already stated above, capital costs comprise the following financial items:Depreciation is the distribution of the reduction in value of an item over the useful service life. In Equation (1). TI is the total investment obtained as the sum of aircraft price and the spare costs that are that related to aircraft spare parts. The RV is the value of the aircraft at the end of the operating life. Parameters are explained in The evaluation of interest charges is a tough task since government agencies and banks can apply different charges and various fees to different customers. Charges are linked to world economic climate, local exchange rates, credits provided by national governments to airliners or to a manufacturer company to encourage the export. Equation (2) provides for a simple description of the interested cost per year, parameters are explained in The insurance company associates the probability of aircraft failure with technical risks. Together with the baseline risk there is the risk to lose an aircraft due to non-technical reasons such as terrorism . In the fuel  for the year in which the cost estimation is required. IATA website offers an up-to-date valuation (https://www.iata.org/en/publications/economics/fuel-monitor/ accessed on 12 May 2021). As can be noticed, fuel cost is expressed in units of volume; usually, fuel consumption per trip is given in units of mass, so it is necessary to know the jet fuel density to complete the cost estimation. Then direct operating cost associated to fuel can be the obtained by applying Equation (4). Parameters are explained in For a correct estimation, it is important to first know fuel price PLanding fees are incurred for use of the airfield with its runways. Navigation charges are incurred for use of radio navigation and direction by air traffic control. Ground handling may include the following services.charges) are broken down into landing (DOCldg), ground (DOCgrd) and navigation (DOCnav) charges. The former charges can be further divided in landing (DOCldg), noise (DOCnoise), and emission (DOCnox) related charges as suggested in [eco) is applied to accommodate the landing charges, as shown by Equation (5). This approach meets the requirements given by ICAO [eco is set either to 0.635 or to 1.0, whether the noise and emission charges are considered or not [In the model, the charges (DOCested in . However by ICAO . It is wd or not . The detd or not .(5)DOCcLanding charges. It is well known that an airline must pay a fee each time an aircraft of its fleet lands at an airport. By further looking into this issue, it is interesting to find that each airport has its own taxation rules for landing charges. This fact makes difficult to create a unique model able to describe the whole regulation. As stated above, the equation proposed by AEA is here introduced. It can be noticed that landing fees generally increase with maximum take-off mass. In Equation (6). Kldg is equal to 6 $/t for long range aircraft and 7.8 $/t for short-medium range airplane (MTOW is expressed in tons) [in tons) . ParametEn-route charges. The charges so far considered are all paid for by an airline when it enters the airspace or the ground of a specific airport. The fees treated in this section are still falling under competences of the aircraft operator but are paid to a specific authority which controls all the rest of the available airspace. In this document is analyzed the Euro- pean airspace, which is controlled by EUROCONTROL (https://www.eurocontrol.int/ accessed on 12 May 2021). Each aircraft operator which overflies EUROCONTROL Member States must pay a monthly bill. These charges are handled for EUROCONTROL by the Central Route Charges Office (CRCO) and are used to support air navigation facilities and services for safe operations. The CRCO collects four types of charges:En-route charges;Charges for terminal air navigation services;Air navigation charges;Communication charges.Due to their specific features, air navigation, terminal, and communication charges are not modelled in this work; the implementation will concern only en-route charges. A route charge is levied for each flight performed in the EUROCONTROL airspace, which is divided into en-route charging zones; its cost is given by Equation (7). Parameter explanation is reported in Ground-Handling charges. These kinds of charges are strictly related to passengers (or amount of cargo) by nature (airport estimates these costs as a function of weight). However, each airport imposes different fees depending on the geographical position, facility dimensions and other floating parameters. Luckily, the AEA method supplies a simple formula as shown in Equation (8) to consider this part of DOC. Parameter explanation is reported in Noise charges. Since the 1970s, the ICAO has set numerous Standards for aircraft’s noise emissions that are gathered in Annex 16 of [ex 16 of . In thesThose points are identified as: (i) fly-over, located 6.5 km—measured along take-off flight path— far away from the brake release point, (ii) sideline, located 450 m far away from the runaway axis in each direction—the highest noise measured at any of those points is considered as reference value—, and (iii) approach, 2 km far away from the runaway threshold along the approach flight path.approach, Lflyover, Llateral), the specific noise threshold applied to departure (Td) and arrival (Ta) airport, and the unit noise rates (Δd and Δa) expressed by Equations (9) and (10). When the latter rates are the same (noiseC), the noise induced landing charges can be estimated by Equation (11). Parameter explanation is given in Furthermore, the so-called cumulative levels are defined as the arithmetic sum of the certification levels at each of the three points. The responsible authorities of the various countries shall regulate noise emissions to comply the ICAO guidelines. According to the national laws, airports apply supplementary charges which increase with the noise level of the landing aircraft; this is done to encourage airlines to adopt quieter aircraft. Like landing and take-off fees, noise charges strictly depend on the airport considered. These two charge typologies are, in some cases, merged because both depend on aircraft’s weight. The methodology for noise charges calculation is the one recommended by the Transport Aircraft Noise Classification Group (TNAC) ; here, nEmissions charges. The pollutants considered in this document are NOx, CO, and HC. The term NOx is generally referred to nitrogen oxides, namely nitric oxide (NO) and nitrogen dioxide (NO2), which are the main responsible of air pollution; the acronym HC, on the other hand, indicates hydrocarbons, which are generated by burned or partially burned fuel and contribute to smog generation, while CO is the carbon oxide. Charges related to the emission of nitrogen oxides (NOx) per year are computed according to Equation (12). The estimation of the NOx mass emitted during Landing and Take-Off (LTO) cycle as proposed by Emission Related Charges Investigation as proposed by the Emission Related Landing Charges Investigation, which is an ECAC subgroup [subgroup . In simicm) as shown by Equations (13) and (14). The cost calculated is quite accurate only if an exact estimation of labour rate is known. Parameter explanation is provided in Crew cost includes the salaries for the cockpit and cabin staff. The approach here applied states that crew cost can be obtained simply by multiplying a proper Labour Rate (LR) by the number of crew members  are presented.In Equation (15), the high level costs are depicted as from reference . HoweverIn greater details, the relationships used to estimate maintenance costs are in the following Equations (16) to (20). Parameter’s explanation is provided in Structural Health Monitoring deals with the analysis of structural performance in view of on-condition maintenance as well as integrated oriented design. However, a health management strategy relies on a complex framework where damage detection is only the first crucial action. Indeed, further critical tasks are needed once diagnosis is achieved. In addition, different stages can be identified, and likewise different methodologies can be adopted to perform those tasks ,48,49. GUsing permanently distributed sensors, several approaches are available to interrogate the structure and extract features sensitive to a specific damage. They can be broadly divided in three groups: (i) vibration-based, (ii) wave propagation-based, and (iii) electromechanical impedance (EMI) techniques. The former approaches are both using the dynamic behaviour of the structure to collect useful information about its condition. The global vibration approach aims to detect presence and location of damage by analysing the frequency response functions (FRF) of the structure which is affected by damage mechanisms like delamination ,53,54. HOther countless “non-conventional” approaches are also available and usually characterized by non-contact techniques. Among them, vision-based SHM using computer vision techniques are successfully adopted to develop accurate and low-cost bridge monitoring systems. They can identify and quantify irregular behaviours in bridge safety by simply employing single or sparse cameras and image processing techniques ,62. The From data processing standpoint, different or multiple levels of diagnosis can be provided by a SHM system according to the information collected from the signals and the specific algorithm adopted to investigate the test datasets. However, it is worth achieving both diagnosis (current condition) and prognosis (remaining useful life) to enable a condition-based lifetime strategy. In detail, the health management can be broken down in four different steps : (i) damThis multi-level output still depends upon the condition monitoring approach envisioned. However, generally both diagnosis and prognosis output are needed. It is quite complex to provide a defined technological readiness level for such an approach, but the aim of many research and innovation projects is to move from an unproven concept  to the complete formulation of the concept, which will be optimized in terms of direct operating costs and emissions and verified by means of requirements compliance.The SHM system development, including employed technologies and methodologies, assumes a crucial role for the enhancement of maintenance strategies of commercial aircraft. The information about aircraft operations and system condition collected by the large number of sensors can be combined with maintenance tasks carried out, logistics data and expected load scenario to achieve predictive maintenance approach effectively depending upon the health diagnostics and prognostics of the aircraft. In addition, the SHM multi-level output can feed into an adaptive health management model to let the airliner have an optimized downtime schedule and fleet management minimizing overall costs. Indeed, the implementation of SHM within maintenance strategies allows moving to an “on-condition approach” the current scheduled inspections and making scheduled the inspections unscheduled so far (due to new warning and prognosis capabilities). As a philosophy the SHM based maintenance is pro-active more than reactive, like the actual maintenance approaches. The result is expected to move from a “preventive” to a “predictive maintenance” with the aim to obtain a safer, and cheaper aircraft.This latter goal relies on the fact that predictive maintenance, diagnostics and health monitoring aims to eliminate unscheduled groundings, eventually avoiding Aircraft On Grounds (AOGs) and the associated operational interruptions. Structural health management can tell some parts do not need an a-priori scheduled check. However, a full transition to health management will need more history, examples, and regulatory confidence so that the maintenance manual could become customized to the specific aircraft with every check based on its own operational history and current health status.Indeed, SHM implementation within maintenance strategy requires the settling of various aspects mostly related to aircraft and damage types as well as level of safety ensured by the actual maintenance scheduling, which is a combination of different inspection categories moving from transit check to A-/B-/C-/D-checks, respectively. From the transit to the D-check, the time to perform the maintenance tasks is increasingly longer as the time interval between two following checks. The SHM system can work in place of detailed visual inspection mostly adopted in A-check and C-check  . In addiTo feed the MDA implemented hereinafter, an exemplary SHM system is considered to achieve realistic results, but without prejudicing the generality of the methodological framework. This latter is independent of the SHM system adopted as further discussed in Results section. The considered system relies on the use of small lead zirconate titanate (PZT) transducers, which are light (about 2 g) and effective for GW or EMI interrogation. The current technology is well suited for cabling and requires an analog to digital converter device together with a switch system and a power amplifier to drive the PZT interrogation. The power electronics are available on the market and customizable into small boards capable to interface with up to 25 sensors , and a wTo provide a reliable estimation of possible benefits brought by SHM the impact such a technology has on aircraft performance must be well assessed at aircraft level. The introduction of permanently attached sensors to aircraft structures affects the mass of each of the aircraft components touched by this technology. At aircraft level the impact that a mass variation of a single component has is not linear according to the so-called snowball effect illustrated in greater required lift;larger wing;higher aerodynamic drag;therefore, the thrust must be increased;this leads to larger engines;this increases the weight again.If the weight of structures or systems is increased an increase of the total aircraft weight is expected. Leading to the following:This process is iterative and continues until the weight converges. Standing the above, the effects of extra masses introduced by SHM system must be well investigated at aircraft level by means of a Multi-Disciplinary-Analysis (MDA) framework. This will allow to provide a reliable estimate of advantages and drawbacks of applying a SHM technology.An MDA workflow couples the major aircraft design disciplines such as weight, balance, aerodynamics, performance, and DOC. The MDA loop begins with a well-educated first guess of the fuel required to fulfill a certain mission. To estimate the center of gravity excursion range, the balance analysis is carried out. Then, aerodynamic and stability modules perform the estimation of the trimmed aircraft lift and drag polar curves for each of the estimated center of gravity position. The last step of the MDA loop deals with the performance estimation through a simulation-based approach to calculate the new amount of fuel required to cover the specified mission range. Finally, an iterative loop is fulfilled to estimate the right fuel mass (and thus the aircraft maximum take-off weight) required for the selected mission profile. The loop converges when the first estimated fuel mass equals the one calculated at the mission profile analysis stage. Within the iterative loop to calculate the fuel mass illustrated in Mass estimation of weights and balance of each aircraft component and onboard systems are performed using the formulations and approaches suggested by Torenbeek in .Since this work seeks to highlight the net effects at aircraft level of the introduction of SHM technology, the abovementioned second convergence loop, concerning the thrust scaling and the engine mass updating, will be not performed. This allows for evaluating the negative effects on aircraft performance due to the additional mass introduced by the application of permanently attached monitoring sensors.The main motivation behind this research work is to measure the possible reduction of DOC by implementing SHM. References discussed in To evaluate the net possible benefits introduced by SHM technology on the DOC, this work proposes a parametric study by changing the density of SHM sensors in terms of number of sensors per square meter. The idea is to select an existing flying platform and to apply a number of health monitoring sensors to main components structures . The application of SHM sensors will introduce an extra mass triggering the mass snowball effect see . PreviouClearly, by aircraft design point of view, the introduction of SHM technology means to fully re-design the aircraft to preserve main performance like field lengths, time to climb, block fuel, block time, emissions, etc. The inclusion of SHM technology should be approached already at the early stages of aircraft design. To get the most out of such a technology, a multidisciplinary optimization should be accomplished at aircraft level to define the optimum aircraft solution compliant with a specific set of top-level requirements aiming at the minimum DOC or at the minimum environmental impact.This work is meant to be a first step towards the inclusion of the SHM technology at the early stages of aircraft design. The analyses here presented want to provide useful indications to quantify possible benefits in terms of DOC, brought by the adoption of SHM, and evaluating, at the same time, the detrimental effects of an increased aircraft weight in terms of performance without changing the target payload and mission profile.Since it is not possible to quantify the amount of SHM sensors needed to reduce the aircraft maintenance without getting together many management aspects and details about the methodology adopted for condition monitoring, the parametric investigation proposed in this work intends to lay out a reference design framework to conceive cost effective SHM systems. Indeed, the investigation is performed by changing the number of sensors per square meter applied on aircraft main components. That is to say, the results can be exploited and applied to any SHM system to predict the impact on the aircraft costs by assuming the actual sensor density (or mass) needed to ensure a certain maintenance benefit. Hence, in such an inverse approach, the SHM designer can verify the suitability and affordability of the system conceived simply by computing the weight penalty introduced. In other words, the aim is to define a break-even point between the aircraft mass increment and the reduction of DOC. At the same time, the investigation provides information about the aircraft performance degradation in order to evaluate if the loss in aircraft performance is worth the benefits in terms of DOC.Targeting the minimum impact into re-designing the aircraft embedding the SHM technology, the multidisciplinary investigation has been accomplished under two main assumptions. The first assumption consists of keeping constant the external dimensions of the aircraft. This means to keep unchanged the geometrical parameters of aircraft components . The other assumption consists of keeping constant the installed power (the power plant remains unchanged). In particular, the cost of engine replacement would have an impact such that to cancel all possible benefits introduced by SHM.The jet aircraft selected as reference platform with respect to which estimate the effects of SHM technology is similar to the Airbus A220-300. The reference aircraft is modeled through JPAD software assuming the set of TLARs of the Airbus A220-300. All the required data of the reference aircraft were retrieved from public sources such as the aircraft manual, the European Aviation Safety Agency (EASA) data sheets, public data archives, and the Base of Aircraft Data (BADA) database by looking at A220-300 like aircraft models ,78,79,80Thanks to the available data and through a digitization process of the A320-300 three views, the parametric model of the reference platform was prepared. This digital model was used to perform the multi-disciplinary analysis cycle (neglecting the static thrust update loop but including the mission fuel feedback loop) in order to trim the analysis tool to match the A320-300 performance and benchmarking the reliability of the analyses that can be carried out by means of JPAD software.To estimate aircraft DOC, the fuel price was assumed according to IATA fuel price monitor , and theTo validate the calculation case, Once the reliability of the MDA tools has been validated, the same software setup has been used to perform a parametric study about different values of the weights, coming from a different value of sensor density defined as number of sensors per square meters. In this work, an equal density distribution has been assumed on each aircraft component, which values vary from 0 (aircraft without SHM) to 50 sensors per square meter.Furthermore, for each sensor density value, the costs burden and weight increment at aircraft level were estimated as well. As concerns the aircraft performance, the authors choice was to monitor the main characteristics without imposing constraints since the basic idea is to analyze the impact on an existing aircraft, to find out what should be the cost benefit due to the SHM system rather than to design a new aircraft optimized for maintenance aspects.The proposed methodology for DOC turns out to be crucial in the early stage of a new aircraft program. In this respect, the aim of the research here presented is to offer a fast and reliable tool for considering cost related issues since the conceptual design phase.However, to perform a realistic estimation of the DOC, it is necessary to make several assumptions regarding peculiar parameters. As already stated, the reference aircraft is a jet aircraft similar to the Airbus A220-300. Starting from the capital costs, it is necessary to estimate the aircraft and the engine prices. In this case, the price has been estimated starting from the real price of the Airbus A220-300 plus the cost of SHM sensors (considering a return factor of 10% due to innovative aircraft).According to the technological level of the SHM solution, the sensor price is only a minimum part of the system cost. As mentioned above, to have a PZT based SHM system, it is indeed necessary to equip the whole aircraft with a wiring system (including wires and connectors) and a specific compact power electronics hardware  needed to enable sensor control and allow signal acquisition Considering the current market prizes, each sensor unit requires a dedicated local system whose cost is assumed as 230$ per piezoelectric transducer when PZT-based actuation and sensing SHM system is considered. In addition, there is an una-tantum integration cost along with a possible concurrent cost for the system management. The former is due to the cost of the system installation. This slightly increases the aircraft manufacturing costs but it can be reasonably included within the overall costs for aircraft systems integration. Instead, the latter is due to the maintenance of the system itself and it is included within the maintenance costs of the aircraft.In addition to the direct cost of the equipment, it is worth noting that each component moved on board has its own weight, which has been also characterized according to the considered SHM solution. Sensors, wires, and connectors along with hardware needed for multi-channel actuation and acquisition lead to a weight penalty of about 80 g per transducer. That mass returns a sort of cost penalty which has been underestimated in many cost-benefit estimations so far, where a certain cost per flight hour induced by the added mass is considered without accounting any effect on aircraft performance .Strictly related to prices is the spare cost, which could be defined as the sum of the cost of the engine and aircraft spare parts. Usually, the cost of aircraft spare parts is assumed as the 10% of the aircraft cost, while the engine spare part as the 30% of the engine cost . The TotAnother fundamental parameter is the Utilization (U), defined as the number of revenue hours per year . The Block Time (BT) is the total time spent from starting engines to engines off, while the Flight Time (FT) is equal to BT less ground maneuver time. The relationships between BT and FT, and the correlation for U is suggested by AEA . The schAll DOC items could be expressed in several ways based on the Utilization (U), Block Time (BT), and number of passengers. Each of them could give different information on the aircraft operation’s impact. In fact, the operating cost could be reported as hourly cost in US$/h or in a route cost obtained by multiplying the hourly DOC by the BT (US$/flight).This latter parameter can be divided by the block distance to get to the mile cost (US$/NM) and then by the maximum number of seats to define the seat mile cost ((US$)/(NM seat)). To have a further look into those costs, some manufactures quote them on cash basis by removing the capital costs from the total DOC. This is because airliners commonly lease the aircraft yearly and the related cost is rather accounted within the company balance sheet separatelyTo make clearer and summarize what is stated above, Results shown in 50% of line maintenance since it should not be necessary to perform any preventive actions thanks to the information gathered through the sensors;50% of base maintenance since it seems unrealistic the hypothesis for which it is possible to eliminate completely the so-called Check C (from regulation point of view).Since the maintenance cost is the sum of line, base, and engine overhaul (components costs are not considered in the present work), to evaluate the impact of the SHM technology on maintenance costs, an assumption is needed. Results discussed in this work are achieved by assuming that SHM allows for reducing the costs as follows:Anyway, this assumption has an almost negligible effect on the DOC estimation. The authors have performed analyses with other two assumptions: 40–60% and 30–70% of reduction of base and line maintenance respectively. Results illustrated in It is worth noting that to achieve the assumed reduction of the maintenance cost, the number of required sensors per square meter is unknown. This value relies on the specific methodology adopted to detect damage and apply health management. That is to say, it reflects the technological level of the SHM systems and may vary according to the innovation thereof. However, given the assumptions about cost and weight induced by sensor integration according to the density all through the airframe, it is possible to get to a quantitative information about the induced DOC. Indeed, these trends provide an aseptic quantification of the resulting operating costs according to the number of sensors placed on the aircraft and provide a first guideline to design an affordable SHM system. The same discussion can be undertaken considering the operative empty weight induced by sensor integration and DOC variation.To summarize the achieved results, it is worth highlighting once again that the parametric trends are drawn regardless of the SHM technological level. Hence, the break-even point returns the upper limit for the weight induced by the SHM system. That is to say, the mass of the new system that is integrated within the aircraft should not exceed the mass increment at the break-even point to prevent significant performance loss. This result is independent of the system type, laying the foundation for a new way to design the SHM system. So far, it is hardly recognized a-priori the value of the latter at aircraft level. Given this insight, the SHM designer already knows the weight constraints to get to an affordable system in terms of direct operating costs of the aircraft. Hence, it is a-priori well-known whether implementing SHM is convenient or not while conceiving a specific SHM solution. In light of this discussion, sensor density becomes the principal SHM parameter to be formulated to estimate costs induced by the system and understand the value of the system in terms of costs. Just to give an impression, Although the break-even point suggests a promising impact of the SHM at aircraft level, it is worth noting that around that value, the aircraft performance is relatively compromised, as reported in This paper is intended as a first effort to look into the evaluation of the effective benefits brought by the adoption of SHM technology. Nonetheless, the integration of SHM is something that needs to be considered since the early stages of aircraft design in order to develop new aircraft that could maximize the potential benefits of such a technology.The introduction of SHM technology enables to introduce an on-demand screening of the current airframe health increasing flight safety during the whole aircraft lifetime. However, integrating SHM systems demands the aircraft re-design to preserve main performance like field lengths, time to climb, block fuel, block time, emissions, etc. This paper approaches the problem through a multidisciplinary analysis at aircraft level to provide useful indications to quantify possible benefits in terms of DOC, brought by the adoption of SHM. The parametric analysis carried out on a A220-like aircraft shows that the operating costs of SHM-equipped aircraft could be lower than those of a reference aircraft till 4% MTOW increment brought by the introduction of the SHM system. In addition, the parametric trends are drawn regardless the SHM technological level. Hence, the break-even point returns the upper limit for the SHM system weight. That is to say, the mass of the new system that is integrated within the aircraft should not exceed the mass increment at the break-even point to prevent significant performance loss.This paper wants to be a first effort in investigating the net potential benefits from the implementation of a SHM technology. However, the integration of SHM is an aspect that must be considered since the early stages of aircraft design to develop new aircraft that could maximize the potential benefits of such a technology.


Pupil segmentation is critical for line-of-sight estimation based on the pupil center method. Due to noise and individual differences in human eyes, the quality of eye images often varies, making pupil segmentation difficult. In this paper, we propose a pupil segmentation method based on fuzzy clustering of distributed information, which first preprocesses the original eye image to remove features such as eyebrows and shadows and highlight the pupil area; then the Gaussian model is introduced into global distribution information to enhance the classification fuzzy affiliation for the local neighborhood, and an adaptive local window filter that fuses local spatial and intensity information is proposed to suppress the noise in the image and preserve the edge information of the pupil details. Finally, the intensity histogram of the filtered image is used for fast clustering to obtain the clustering center of the pupil, and this binarization process is used to segment the pupil for the next pupil localization. Experimental results show that the method has high segmentation accuracy, sensitivity, and specificity. It can accurately segment the pupil when there are interference factors such as light spots, light reflection, and contrast difference at the edge of the pupil, which is an important contribution to improving the stability and accuracy of the line-of-sight tracking. With the continuous development of computer vision and artificial intelligence technology, human-eye-tracking techniques are increasingly used in clinical medicine , psycholCurrently, there are many methods for pupil feature extraction, such as the threshold method ,8,9, regReference  improvedTo segment the pupil from the eye image, including the eye images with disturbed pupil features, it is necessary to detect and remove all other components of the eye. This paper proposes several improvements, mainly including the introduction of global distribution information in the form of a Gaussian model, combined with local intensity distribution information, and studies a fuzzy c-means clustering algorithm based on distribution information to more accurately segment the pupil pixels and for accurate pupil localization and sight estimation.The remainder of this paper is organized as follows: The algorithm flow chart proposed in this paper is shown in J is minimized by iteratively updating the affiliation and clustering centers. The objective function is as in Equation (1). Let i-th cluster, i-th cluster, m is the cluster fuzziness. The affiliation The standard FCM (fuzzy c-means) algorithm  can natuA typical eye-tracking-system eye camera captures an eye image much larger than the eye region and also contains non-eye regions such as eyebrows. Therefore, the eye image is cropped to locate the eye region in the preprocessing process . The eyex in the image, a larger degree of membership will assign it to a smaller one: The histogram of the grayscale distribution of the eye image has a relatively uniform and clear distribution of the intensity of the features in each part of the eye. The traditional fuzzy clustering algorithm often classifies the pixels with the relative intensity similar to the target class. For segmenting the pupil in the eye image, if the global distribution information is lacking, factors such as noise, high gray value, and intensity interference will have a greater impact on the segmentation result. In Equation (1), for each pixel j, To solve these problems, the global distribution information is introduced into the objective function of the FCM algorithm . When thWe define an adaptive local window filter whose weighting coefficient is determined based on the corresponding local space and gray level with the central pixel. The filtering process consists of two steps: the first step is to evaluate the neighboring pixels of the local window pixels and distinguish unreliable neighbors and reliable neighbors; the second step is to use these reliable neighbors to calculate the new pixel intensity value to produce a filtered image.The existing pixel reliability assessment for local window, compares the central value of the local 5 × 5 neighborhood with the intensity of 24 neighborhood pixel values in the neighborhood, obtaining the two deviations to assess the pixel reliability. When the appearance of the background and foreground changes consistently, this evaluation method can distinguish between reliable neighboring pixels and unreliable neighboring pixels. However, when the appearance of the background and foreground changes locally, this evaluation method ignores the difference between neighboring pixels in the neighborhood and cannot evaluate the reliability of neighboring pixels well . MeanwhiThe specific filtering process is as follows:(1) Define a local square window centered on pixel k and set the window size to 5 × 5 throughout the filtering process.r in r is evaluated as unreliable, otherwise it is evaluated as reliable. The local neighborhood pixel reliability is evaluated as shown in (2) Find the middle pixel value r and the central pixel k and r.(3) Define a local window filter and calculate its window weighting coefficient (4) The image is filtered using the described local window filter, and the filtering intensity Two examples of local filtering windows are shown in (5) Calculate the gray histogram of the filtered image to obtain the number of gray levels and the number of pixels with the same gray level.Algorithm1: The adaptive feature extraction of local distribution intensity algorithm.Input: grayscale eye imageOutput: filtered eye image(1). Define a local square window (2). Calculate the deviation (3). Calculate the local window weighting coefficient (4). Filter the imageThe adaptive feature extraction of local distribution intensity method is described as follows Algorithm 1:j, and C is the number of specified clusters. The segmentation of the eye image is mainly to achieve the extraction of pupil pixels. Therefore, the pixels are divided into pupil and non-pupil, and m represents the ambiguity of the cluster. To solve the optimal classification, by minimizing the objective function, the updated function of membership degree and clustering center can be obtained as in Equations (10) and (11):Pupil segmentation refers to the classification problem of dividing eye image pixels into pupil pixels and non-pupil pixels. We improve the method of the fuzzy clustering segmentation algorithm based on image distribution information used to detect pupil segmentation in eye images. Use the global distribution information to strengthen the affiliation of the objective function of the fuzzy clustering segmentation algorithm, perform reliable pixel evaluation on the local distribution information of the image, combine the intensity information and the spatial distribution information for filtering, perform clustering to segment the pupil, and obtain a new objective function as in Equation (9).nced FCM , ξ(xj) rAlgorithm 2: The process of the pupil clustering segmentation algorithm based on distribution information.Input: local neighborhood filtering featureOutput: cluster center and affiliation matrix(2). Initialize the cluster center and membership matrix;(3). Calculate the affiliation matrix and clustering centers during each iteration using Equations (10) and (11);(4). Judge the iteration stopping condition: if (1). Set the maximum number of iterations, the minimum error value, the number of clusters, the fuzzy index, and the value of the neighborhood gray-scale influence factor;The process of the pupil clustering segmentation algorithm based on distribution information is as follows Algorithm 2:When the proposed algorithm converges, the cluster center and the affiliation of each pixel belonging to the cluster center are obtained. In the gray histogram distribution of the eye image, the intensity value of the pupil is the lowest , and theThe dataset of our research experiment in this paper comes from two parts. Dataset I uses the gaze-tracking system of the research for eye image acquisition , sensitivity (SE), and specificity (SP) were used to objectively evaluate the results of pupil segmentation ,18,19. WOn the other hand, although our proposed method had to be verified in two datasets, the pupil segmentation results are relatively better than the same type of fuzzy segmentation algorithm and our group’s previous pupil detection method. In the actual eye-tracking process, locating the pupil and estimating the line of sight is a very complex process with many factors. We put forward that, based on the distribution of divided pupils from the global and local pixel changes, for future research to be more accurate with a variety of eye pupil segmentation, further consideration should be given to various factors in the eye image information to establish a better algorithm to improve pupil segmentation quality.In summary, the proposed method in this paper to segment pupil features can get a high-quality pupil region. We used four other methods to segment the same eye image. Eye image datasets are made of two parts: one part is our eye images, and the other part is from a public dataset; we only selected 500 images to work with. All of the segmentation results show that our proposed method can get better-quality pupil pixel features, especially, when segmenting low-quality eye images.In this paper, we proposed a fuzzy clustering pupil segmentation algorithm based on distribution information. Firstly, preprocessing the eye image, we cropped some non-essential interference factors such as eyebrows, eyelids, and shadows in the eye image, enhanced the contrast between pupils and non-pupil areas, and obtained grayscale eye images. Then, extracting the pupil features, we introduced the global and local distribution information to enhance the membership value and filter images to build a better fuzzy cluster objective function and be able to segment the pupil better. Next, according to the clustering segmentation result, to achieve the pupil threshold and binarization-processing eye image, the pupil characteristics were better highlighted. We could then use the pupil segmentation result to locate the pupil position. The experimental results show that the accuracy, sensitivity, and specificity of our proposed method are better than the existing fuzzy clustering and threshold segmentation methods. Especially for low-quality eye images, including low-contrast and high-intensity features intruding into the eye, as it could segment out the pupil features better. This lays a foundation for accurately detecting the pupil position and contributing to the improvement of the stability and accuracy of the gaze-tracking system.In future work, we will investigate other possible distribution information processing and fusion methods, such as Dempster–Shafer (D–S) evidence theory, for the distribution information of eye images, so that we can better segment the pupil information in eye images of different quality. We will further investigate how to segment pupils quickly using clustering algorithms and more generally for various low-quality eye image segmentation, improve the speed of segmentation computation, and accurately detect pupils for the line-of-sight estimation.


The lack of portability and high cost of multiplex real-time PCR systems limits the device to be used in POC. To overcome this issue, this paper proposes a compact and cost-effective fluorescence detection system that can be integrated to a multiplex real-time PCR equipment. An open platform camera with embedded lens was used instead of photodiodes or an industrial camera. A compact filter wheel using a sliding tape is integrated, and the excitation LEDs are fixed at a 45° angle near the PCR chip, eliminating the need of additional filter wheels. The results show precise positioning of the filter wheel with an error less than 20 μm. Fluorescence detection results using a reference dye and standard DNA amplification showed comparable performance to that of the photodiode system. Infectious diseases continue to impose a major burden to global health and the economy ,2,3,4. TEmerging technologies that increases the efficiency and reduces the time of diagnosis has led to the development of various rapid diagnostic platforms which can be employed at point-of-care (POC) ,11. By aDespite extensive efforts and improvement in global health, preventing death caused by infectious diseases in developing countries remains a challenge . VirusesThree major diagnostics methods used at POC are viral culture, serological diagnosis, and nucleic acid detection. Nucleic acid detection is favored over the other two methods owing to its simplicity that does not require highly skilled professional and expensive equipment as in viral culture, and eliminates the complex step of antibody synthesis that serological diagnosis requires ,15,16. IAlthough PCR is a simple and straightforward diagnosis method, the operation is mainly carried out in certain facilities such as in hospitals or central laboratories due to portability and cost limitations, which can be disadvantageous in emergencies ,24,25,26According to the World Health Organization (WHO), POC tests that are ideal for healthcare in resource-limited settings should meet the criteria of ‘ASSURED’, which stands for affordable, sensitive, specific, user-friendly, rapid and robust, equipment-free, and deliverable ,30. ReceCommercially available qPCR equipment shows high accuracy and sensitivity in detection. However, it is difficult to meet the ‘ASSURED’ criteria and use the equipment at resource-limited settings since it incorporates highly a sensitive fluorescence detection method, increasing the cost to 10-fold that of a conventional PCR platform ,35,36,37Smartphone cameras are also used as a major approach to lower the cost of fluorescence detection ,39,41,43The system proposed in this paper allows for low-cost thermal cycling using a previously reported PCR chip based on a printed circuit board (PCB) with attached thermistor and heater patterns ,45. A hiChlamydia trachomatis DNA. Together, these results demonstrate that the proposed multiplex qPCR is suitable as POC test equipment.The precision validation experiment proved that the proposed multiplex fluorescence detection mechanism can reliably detect the target fluorescence. To evaluate the fluorescence detection performance, four standard fluorescence dyes were selected and tested individually, and as a mixture to investigate the cross interference between the dyes. Finally, qPCR quantification during actual reaction was validated by amplifying and detecting a standard The PCR chip previously reported by our group consists of a PCB and a reaction chamber, where the reaction chamber is in the shape of a water drop and is flat as seen in The mechanism of the proposed system was determined using fluorescein (FAM), hexachloro-6-carboxyfluorescein (HEX), 6-carboxyl-X-rhodamine (ROX), and cyanine 5 (CY5). Therefore, the excitation unit includes light emitting diodes (LED) and excitation filters for the selected four fluorescence dyes. If a light bulb or a white LED is used for excitation, an additional excitation filter wheel is required for multiplex detection. Even if the excitation filter wheel and emission filter wheel is combined, the cost effectiveness, size, and simplicity of manufacture is restricted. In the proposed system, four appropriate LEDs to excite the fluorescence dyes are fixed near the PCR chip so that the incidence angles to the chip surface of their light passing through the excitation filters are 45 degrees, as shown in The cut-off wavelengths of the excitation filter and the emission filter are shown in To implement the filter wheel for the emission unit, the components shown in Fluorescence detection starts at the end of each annealing step of the PCR cycle, acquiring images of all four fluorescence in sequential order starting from the emission filter that is closest to the camera when set at home position. The filter holder is prepositioned at the home position before acquisition starts and returns after the images for all four fluorescence are acquired. Errors on the filter holder position will result in a misalignment of the base hole and the emission filter, narrowing the FOV. A test fixture in which the entity of the filter wheel can be seen through the camera was constructed to evaluate the motor precision. The fixture was made as a dark room with a white LED strip illumination to prevent influence from outside light sources. The cover and filter holder were removed from the filter wheel, and a blue circular sticker with the diameter of 9 mm was aligned with the base hole for image acquisition and analysis to determine spatial resolution around the base hole. Converting the image acquired from the camera to a HSV space shows clear distinction of the blue sticker compared to the background a. From tTo investigate the filter positioning precision, the filter holder and cover was assembled to the filter wheel, and a white paper was placed at the bottom of the base to distinguish the holes on the holder. The emission filter was not attached to the filter holder for this experiment. The images acquired through camera should be close to a perfect circle when the filter holder position is precise. On the other hand, the images will show an ellipse when there is a positioning error due to the overlap of the filter and base holes. This causes the variation of the centroid of the hole image. Therefore, the position error can be evaluated by image binarization and calculating the object centroid. The filter position error in pixels can easily converted to in micrometer with the resolution obtained before. Sequential acquisition of each four filter holes and repositioning to home position prior to the next acquisition cycle was repeated 84 times to determine the precision of positioning.To achieve accurate and consistent analysis of fluorescence during reaction, a process to crop just the chamber region from the captured fluorescence image is necessary. Since the PCR chip is physically fixed on the chip holder in front of the excitation unit and does not move throughout the reaction, the fluorescence can be analyzed with consistency using a calibration chip and obtaining the region of interest (ROI), i.e., the chamber.The process of obtaining the ROI using calibration chip is shown in Chlamydia trachomatis DNA was subject to PCR with the initial copy number of 106 per reaction, in a total reaction volume of 36 µL , which is a validated qPCR equipment that employs photodiode detectors as the fluorescence detection method.The qPCR performance of the proposed system was compared to that from real-time PCR chip system  of the proposed and reference system, which is obtained by comparing the logarithm of the fluorescence value acquired to a predefined threshold. In other words, the Cq is determined as the cycle number where the logarithmic curve of the fluorescence intersects with the predefined threshold. The log threshold was set to be 5.47 as provided by the reference system, and Cq was calculated using linear interpolation [q between the two systems, demonstrating that the performance of the proposed system is comparable to the reference system.polation . The resThis research proposes a compact and cost-effective multiplex fluorescence detection system utilizing the PCB-based PCR chip previously reported from our group . The porWhen using industrial cameras for fluorescence detection, the samples are excited with LEDs for a set amount of exposure time. However, it is difficult to synchronize the exposure time and LED on time for open platform cameras. In the proposed system, the LEDs were fixed close to the reaction chamber, reducing the LED power required to compensate for the longer exposure time up to three-fold. Since open cameras are designed to fit smart phones, the lens size is significantly smaller than that used in industrial cameras in which the smallest lens (S mount) has a lens thread of 12 mm, enabling miniaturization of the overall fluorescence detection system. Taking into account that industrial cameras are not cost-friendly, it is evident that the open platform cameras hold great advantage when developing a portable and cost-effective device.Although the photodiode sensor and the open platform camera are similar in cost owing to the development of smart phones, it is difficult to optimize the manufacture cost using a photodiode since an objective and/or ocular lens is required. The open platform camera has embedded optics that eliminates the need of external lens, also brining the advantage of ease of innovative development, assembly, and maintenance. For example, malfunctions such as reagent leakage or loading failures can be easily monitored since the reaction chamber can be observed simultaneously. In addition, this system holds great potential in delivering a more precise and accurate fluorescence data than the currently existing equipment using a different image processing algorithm and the fluorescence distribution profile within the reaction chamber. Given that open platform cameras with better performances are being developed along with the smart phone industry, the performance of the proposed system can be further improved.


The precision and reliability of the synchronous prediction of multi energy consumption indicators such as electricity and coal consumption are important for the production optimization of industrial processes  due to the deficiency of the coupling relationship of the two indicators while forecasting separately. However, the time lags, coupling, and uncertainties of production variables lead to the difficulty of multi-indicator synchronous prediction. In this paper, a data driven forecast approach combining moving window and multi-channel convolutional neural networks (MWMC-CNN) was proposed to predict electricity and coal consumption synchronously, in which the moving window was designed to extract the time-varying delay feature of the time series data to overcome its impact on energy consumption prediction, and the multi-channel structure was designed to reduce the impact of the redundant parameters between weakly correlated variables of energy prediction. The experimental results implemented by the actual raw data of the cement plant demonstrate that the proposed MWMC-CNN structure has a better performance than without the combination structure of the moving window multi-channel with convolutional neural network. The cement industry is considered as an energy intensive sector . The eneAccurate and reliable synchronous prediction of multi energy consumption indicators is complicated by the fact that the cement calcination process consists of multiple production process variables. The relationships between cement production process variables and energy consumption indicators have the characteristics of time-varying delay, uncertainty, and nonlinearity. For example, as the amount of raw materials increases, the electricity and coal consumption required for the calcination process increase. It takes 50–60 min, varying with the different conditions, for the cement raw material to be converted into calcined clinker. As a result, the electricity and coal consumption required for the calcination of raw materials in the rotary kiln cannot be calculated using the current amount of raw materials, and can only be manually adjusted by the operators based on historical experience. Using the current number of process variables to predict the energy consumption indexes will result in data misalignment and low accuracy. Therefore, the impact of the time-varying delay on the prediction of electricity and coal consumption of the cement calcination process needs to be considered. In production, a large amount of production process data are recorded, which makes it possible to synchronously predict electricity and coal consumption by data driven methods.On the basis of the collection of numerous industrial data and the development of data driven methods, various data driven forecasting approaches have been proposed for prediction or diagnosis in the industrial field. For example, fault detection using statistical regression , electriHowever, the above investigations all focused on a single index prediction strategy, which is insufficient for coal and electricity synchronous prediction in the cement calcination process because the coupling relationship of electricity and coal consumption cannot be obtained. The optimal control solution of the cement calcination process obtained from single index prediction model may not be in the actual solution space, which is invalid as it cannot be used to optimize the cement calcination process.Although many multi-task process studies have been done in many fields such as the forecasting of the maximum connections in wireless communication , the preAccording to the above analyses, a multi-indicator predication model was established to simultaneously predict the coal and electricity consumption in the cement calcination process. On one hand, this reduces the modeling burden in the practical application of industrial systems, which contributes to the efficient use of industrial system resources. On the other hand, it provides a precise prediction of coal and electricity consumption in the cement calcination process, which contributes to the reasonable production scheduling and energy planning of the cement calcination process. In addition, the multi-output prediction model can well explain the strong coupling relationship between the prediction indicators, which is consistent with the process mechanism of the cement calcination process. Therefore, research into this issue is of great significance to promote the development of the cement industry.The proposed model combines moving window with convolutional neural networks (CNN) to reduce the negative impact of time-varying delay on the prediction of cement energy consumption. The moving window integrates time-varying delay information hidden in time series data into the input layer of the CNN. As a result of powerful feature extraction capability, CNN is not only used in image processing , but alsThe multi-channel structure of CNN was designed to reduce the negative impact of the redundant parameters of weakly correlated variables on cement energy consumption prediction in which the single index prediction strategy was first used to predict the electricity and coal consumption separately, with the purpose of reducing the coupling relationship, and then, the coupling relationship was rebuilt before the final output in order to fuse the variables’ characteristics. The design of a multi-channel structure in this paper not only preserves the practical application advantages of multi-output models, but also reduces the negative impact of parameter redundancy on the energy consumption prediction.The combination of a moving window, multi-channel structure with CNN contributes the two characteristics of this paper:(1) A method combining moving window and multi-channel convolutional neural networks is proposed to accommodate time-varying delay information implied in the time series data according to the analysis of the mechanism of cement calcination. The time-varying delay is a kind of unique phenomenon in process industries such as the cement industry. The variable data in the previous period are accommodated by moving window, which is more efficient for feature extraction by CNN with a one-dimensional convolution kernel. The adoption of CNN effectively reduces the excessive parameters brought by moving windows and avoids complex timing matching.(2) The multi-channel structure of MWMC-CNN was designed to predict coal and electricity consumption simultaneously, which effectively addresses the parameter redundancy problem caused by correlated variables of the cement calcination process. In addition, the adverse impact on energy consumption indicators of the synchronous prediction caused by the coupling relationship of the cement production process variables is also eliminated. Finally, the coupling relationship of electricity and coal consumption was rebuilt to provide references for the optimization of the cement calcination process.The actual raw data of the cement plant were used to train the model and the experimental results demonstrate its superiority.The rest of this paper is organized as follows. In Due to the extensive usage of sensors, a large number of industrial data are recorded, which creates the basis for the optimization and operational control of the system by data driven methods. There have been many data driven prediction investigations for energy consumption based on statistical regression, SVM, tree-based method, ANN, and LSTM.Statistical regression is a direct method for data driven, which is a predictive model technique that studies the relationship between independent variables and dependent variables, and is widely used for short-term predictions in many industries. Bianco applied linear regression to predict urban power consumption  and CataSVM is a kind of typical machine learning method that has strong nonlinear ability, and the computational complexity depends on the number of support vectors, so it still has better performance even if the sample space has a higher dimension . These aTree-based methods divide the predictor space into sub-regions, and then the predicted results based on statistical indicators are obtained, which can be used not only for classification but also regression. Tree-based models are widely used to predict critical bus voltage and load in PV buses , the heaANN is a data-driven method of artificial intelligence that has a complex structure and is used for information processing problems that cannot be solved by theoretical analysis . It is aLSTM is good at processing time series prediction because of its special structure, which is relevant to time factor. Thus, LSTM is applied to predict energy consumption, for example, gas consumption . BesidesDeep learning not only shows excellent performance in image analysis , speech CNN is a popular structure of deep learning, which was first proposed by Y. LeCun et al. (1998) . The chaThe cement calcination system is the main energy consumption subsystem in the cement production process. Aiming at this subsystem, the relevant variables reflecting energy consumption and the time range of the moving window were selected for the proposed method. This section describes the mechanism of the cement calcination process and the reasons for selecting the parameters required for the model.The cement calcination process can be roughly divided into three parts: preheating and pre-decomposition, calcination, and cooling, during which the cement raw material is converted into calcined clinker. The cement calcination process is shown in First, the cement raw material is sent into the cyclone preheater to preheating. The cement raw material in the preheater is in suspension due to the action of the ID fan, which increases the contact area between the gas and raw material. The high temperature gas is discharged from the rotary kiln and the decomposition furnace exchanges heat with the cement raw material sufficiently, and the larger contact area contributes to the decomposition of carbonate in the raw material. Second, the cement raw material is sent into the rotary kiln that rotates at a constant speed for calcination, which contributes to the rapid decomposition of the carbonate of the cement raw material and the progress of other physical and chemical reactions in the rotary kiln. Finally, the high temperature clinker from the kiln is cooled by the grate cooler until the temperature can be withstood by a subsequent process. It takes about 50–60 min for the cement raw material to be converted into calcined clinker. Therefore, the electricity consumption of per ton cement production (ECPC) and the coal consumption of per ton cement production (CCPC) of the current batch are reflected by the production process variables in the past hour.As shown in From the above analysis, we can see that the electricity and coal consumption of the cement calcination process are affected by the amount of cement raw material and the energy consumption of the past period. Therefore, the amount of cement raw material, the ECPC, and CCPC at historical moments were selected for the prediction of coal and electricity consumption. In addition, the coal consumption is impacted by the temperature in the preheater, decomposition furnace, and rotary kiln, so the process variables selected for coal consumption prediction were as follows: primary cylinder temperature, decomposition furnace coal consumption, decomposition furnace temperature, kiln temperature, secondary air temperature, and kiln head coal consumption. Electricity consumption is influenced by process variables such as kiln current and fan speed, so the ID fan speed, EP fan speed, Kiln average current were selected for the prediction of electricity consumption.According to the above analysis, time-varying delay, parameter redundancy, and coupling are the main barriers in the forecasting of coal and electricity consumption simultaneously, thus we propose a MWMC-CNN structure that employs a multi-channel CNN model and moving window technique to solve the time-varying delay and data redundancy problem of energy consumption prediction in the cement calcination process. The moving window technique was employed to establish the input layer of the MWMC-CNN model to solve the time-varying delay problem. The multi-channel structure was adopted in the proposed model instead of the traditional structure to solve the parameter redundancy problem. This section describes the components of the proposed MWMC-CNN model and the specific steps of the energy prediction algorithm for the cement calcination process.The structure of the proposed model can be divided into three parts. The first part is a time-varying delay data input layer, where the cement calcination process variables are processed in time series. The second part is the data feature extraction layers in which different cement calcination process variables are convolved and pooled separately. The third part is a regression prediction layer that integrates the output data of two independent channels and finally outputs the predicted energy consumption value. Then, the Adam algorithm propagates back errors that come from the output values of MWMC-CNN and the training tags to layers of MWMC-CNN, and then modifies the weights until the errors are less than the expected values. The structure of the MWMC-CNN model is shown in The moving window technique was employed to establish the input layer of the MWMC-CNN model to solve the time-varying delay problem. The establishment can be divided into three parts: variables selection, window parameter selection, and time series moving window construction.The first step in this section is to select the key variables that affect the energy consumption of the cement calcination process and normalize the selected variables data According to the analysis of the energy consumption of the cement calcination process in The selected six variables for the A channel were as follows: ID fan speed .A input channel by the time series moving window are as follows:A channel with s rows and 6 columns, which means that the input sequence contains 6 variables in the past s sampling times.The data selected as the B input channel by the time series moving window are as follows:B channel with s rows and 9 columns, which means that the input sequence contains 9 variables in the past s sampling times.The data selected as the Weak correlation exists among some input variables, which reduces the prediction accuracy. The multi-channel structure of CNN was designed in the data feature extraction layers of the proposed model to solve the parameter redundancy problem between weakly correlated variables.A channel is used to extract the time series information about electricity consumption during the cement calcination process, and the B channel is used for the coal consumption.As shown in iw is Convolution on time series data using one-dimensional convolution kernels is considered to be efficient. The one-dimensional convolution kernel relu function after being convolved by n convolution kernels, and n feature maps are output as the input of the pooling layer. Every feature map is equivalent to a data matrix. To compress the characteristics of feature maps, the one-dimensional pooling kernels were used as follows:The input time series data are activated by the k value in the column and the pooling stride is k. In the proposed method, the dimension of input feature maps is k. The length of feature map matrices is reduced while the number of which is unchanged after the one-dimensional pooling.One-dimensional average pooling takes an average for every A channel, which is constructed by repeating the processes multiple times, and the structure of the B channel is similar to A except for the size of the input data matrix.The above processes are one convolution layer and one pooling layer of the Although the interference problem of weakly correlated variables does not exist in the regression prediction layer, it exists in the process of convolution and pooling, thus the multi-channel structure was adopted by the proposed model to solve this problem. The fully connected layer integrates the time-varying delay features extracted from the two CNN channels, rebuilding the coupling relationship of coal and electricity consumption. Therefore, the time-series data that have been convolved and pooled multiple times in each channel are integrated into a column as the input of the fully connected layer. As shown in The output data of fully connected layer are linearly weighted and summed to obtain the outputs of the proposed model as follows:w and b by calculating the first moment estimation and second moment estimation of the gradient. The Adam algorithm is used to perform backpropagation to adjust the parameters by inversely adjusting the weights w and the biases b of the multi-channel convolutional neural network model proposed in this paper. For the regression prediction problem, it is convenient to use the mean square error as the objective function to measure the gradient increases or decreases with error, which contribute to the convergence of the model. The study of this paper is the synchronous regression prediction problem of coal and electricity consumption in the cement calcination process. The MSE (mean squared error) is performed as the objective function of the proposed model, which is implemented as follows:Traditional CNN usually uses the stochastic gradient descent method for back propagation, but it is prone to overfitting and local optimal solutions. To prevent these disadvantages, it can be replaced by the Adam algorithm , which iw and bias b by the Adam algorithm. The default parameters of the Adam algorithm are preserved, which include the learning rate r  Initialization:(Step 1.1) Normalization:(Step 1.2) Moving Window Processing:(Step 2) Convolution and pooling:(Step 2.1) Convolution and activation:A channel: B channel: (Step 2.2) Pooling:A channel: B channel: Repeat Step 2.1–2.2 several times in each channel.(Step 3) Full connection and output:(Step 3.1) Full connection:(Step 3.2) Output:Algorithm 2 Parameter adjustment algorithm of MWMC-CNNObjective function:Parameter Initialization:whilenot converged do:    L sets of data)                            end whilereturnThe parameter update process of Adam algorithm for every element. The above analysis is the forward propagation process of the proposed MWMC-CNN model, which can be divided into three parts: the moving window input part, the convolution and pooling part, and the fully connected output part. In the MWMC-CNN training process, the Adam algorithm is used to fine-tune parameters. This section gives a full description of the pseudo code of the MWMC-CNN model Algorithms 1 and 2. The details of the formulas in the algorithm are in p = 0.03 and g = 0.01; and a LSTM model with two LSTM layers where each layer contains 48 hidden units to predict the electricity consumption and coal consumption of the cement calcination process. The experimental results were compared and analyzed to verify the superiority of the proposed model. In total, 12,500 datasets containing the electricity and coal consumption and cement calcination process variables stated in In this section, we used a MWMC-CNN model with two channels where each channel contained three convolutional layers and three pooling layers; a CNN model with three convolution layers and three pooling layers; a LSSVM model in which m is the size of the forecasting sample.The root mean square error (RMSE), the mean relative error (MRE), and the mean absolute error (MAE) are used as the indicators of predictive performance, whose formulae are respectively shown as:The depth of the CNN and the size of the convolution kernel affect the performance of the proposed model. In order to find the parameters that make the proposed model perform better, we conducted a parameter adjustment experiment to compare the errors of the MWMC-CNN with different depths and different convolution kernel sizes under the actual cement calcination process data. The experimental results are shown in We performed ten trials on MWMC-CNN with different sizes of the convolution kernel. All the above comparison experiments were optimized by the same optimization algorithm with the same parameters except for the depth and kernel size. The mean value of prediction errors of MWMC-CNN with six layers are represented by the dark-colored rectangles, and the line segment at the top of rectangle represents the variance of the experimental prediction error. The corresponding mean values and variances of the experimental prediction errors of MWMC-CNN with four layers are represented by light-colored rectangles and line segments above them, respectively.As shown in In order to show the intuitiveness of the experimental results, the MWMC-CNN, CNN, LSSVM, and LSTM were trained and tested under the same datasets, respectively. As a kind of highly competitive shallow network, LSSVM is widely used in industry field, so it is reasonable to choose LSSVM for the comparison. As a kind of time-dependent model, LSTM is designed to extract time series information and simulate long-term dependencies and short-term dependencies. The performance of time series information extraction and hidden feature mining of the proposed model can be verified by a comparison with LSTM and CNN, respectively. The experimental results are shown in In order to evaluate whether LSSVM, CNN, LSTM, and MWMC-CNN learned the characteristics of training data, we carried out prediction experiments using the training dataset. The more overlapped the two lines, the more characteristics have been learned. The training result curves of the electricity and coal consumption of the four models are shown in To evaluate the actual prediction abilities of the four prediction models, we tested the models using the test dataset, which was different with training dataset. The test result curves of the electricity and coal consumption of the four models are shown in In As shown in In this paper, we proposed a multi-channel convolutional neural network (MWMC-CNN) structure with a moving window based on the analysis of the mechanism of the cement calcination process and coupling relationship between the cement production process variables, which reduces the impact of time-varying delay in the cement calcination process on prediction accuracy while reducing the burden of industrial system modeling. The experimental results demonstrate the outstanding performance of the proposed model compared with the CNN model. In addition, according to different industrial mechanisms, the proposed model can be expanded to more output or input to deal with more complicated time series data, which provides a reference for the multi-output soft measurement modeling of the process industry and time series analysis of industrial big data. In our future studies, the proposed method will be expanded to predict the consumption of other kinds of energy or the energy consumption of other production processes, which provides a reference for energy scheduling and intelligent optimization control of the process industry.


The study of protein–protein interaction and the determination of protein functions are important parts of proteomics. Computational methods are used to study the similarity between proteins based on Gene Ontology (GO) to explore their functions and possible interactions. GO is a series of standardized terms that describe gene products from molecular functions, biological processes, and cell components. Previous studies on assessing the similarity of GO terms were primarily based on Information Content (IC) between GO terms to measure the similarity of proteins. However, these methods tend to ignore the structural information between GO terms. Therefore, considering the structural information of GO terms, we systematically analyze the performance of the GO graph and GO Annotation (GOA) graph in calculating the similarity of proteins using different graph embedding methods. When applied to the actual Human and Yeast datasets, the feature vectors of GO terms and proteins are learned based on different graph embedding methods. To measure the similarity of the proteins annotated by different GO numbers, we used Dynamic Time Warping (DTW) and cosine to calculate protein similarity in GO graph and GOA graph, respectively. Link prediction experiments were then performed to evaluate the reliability of protein similarity networks constructed by different methods. It is shown that graph embedding methods have obvious advantages over the traditional IC-based methods. We found that random walk graph embedding methods, in particular, showed excellent performance in calculating the similarity of proteins. By comparing link prediction experiment results from GO(DTW) and GOA(cosine) methods, it is shown that GO(DTW) features provide highly effective information for analyzing the similarity among proteins. Proteomics essentially refers to the study of the characteristics of proteins on a large scale, including the expression level of proteins, the functions of proteins, protein–protein interactions, and so forth. The study of proteome not only provides the material basis for the law of life activities but can also provide the theoretical basis and solutions for elucidating and solving the mechanism of many diseases . HoweverGene Ontology (GO)  describeGO has been widely studied in the field of biology . GO termThis study merged the three categories of ontologies and GO annotations into a large graph called the GO Annotation (GOA) graph. We used three categories of ontologies transformed into a GO graph. Effective graph analysis on GOA and GO graphs can improve our understanding of the structure and node information of GO and proteins. Using the GOA information of the proteins, the similarity among proteins can be calculated, and the relationship between proteins can be predicted. In recent years, graph learning-based analytical methods have made remarkable progress in bioinformatics and other fields . At presTo explore the performance of graph embedding methods in measuring protein similarity based on GO and GOA, we used four typical graph embedding methods to learn the features of GO terms and proteins. These methods can be divided into two categories. The first category is the random walk method, such as the DeepWalk and Node2Vec methods. The DeepWalk method uses the truncated random walk strategy to obtain the sequence of nodes and point embedding obtained from learning with Word2Vec . Node2Ve1. The GO protein annotations were obtained from the UniProt GOA website2. The Yeast dataset contained 2,887 proteins, and the Human dataset contained 9,677 proteins. The GO data were then preprocessed based on the following processes. First, since several GO terms annotate a protein, term–term relations of GO terms and term–protein annotations between GO terms and proteins were combined into a GOA graph. Second, the GO terms were then transformed into an undirected, unweighted GO graph, regardless of the type and direction of the relationship. We summarize the numbers of GO terms and edges in We downloaded GO data in Open Biomedical Ontologies (OBO) format from the GO Consortium WebsiteBased on different graph embedding methods, the feature of GO terms and proteins was learned into vector representations by fusing GO and GOA graph topologies, respectively. Thus, we could capture the global information based on the graph embedding method, and its learned vectors could calculate the similarity between proteins by the DTW distance and cosine similarity.L). The more neighborhood nodes (higher-order neighborhood nodes) two nodes have, the more similar they are. p and q to control the random walk and adopts a flexible biased random walk procedure that smoothly combines BFS and DFS to generate node sequences. ci are generated based on the following distribution:In this paper, we used the methods of graph embedding based on random walk and deep learning to learn the features of GO terms and proteins through fusing the topology of GO and GOA graphs, respectively. Random walk-based methods include DeepWalk  and Nodetx is the transition probability between nodes t and x, and Z is the normalization constant. According to the node context information, node sequences are generated by setting the sizes of the hyper-parameters p and q to control the random walk strategy. The Skip-gram model is used to obtain the vector representation of the nodes. The random walk graph embedding of nodes reflects the local and global topology information of nodes in the graph.where πSi in the model, where Si is compressed by the auto-encoder, the feature is then reconstructed. Finally, its loss function is defined as follows:The second kind of embedding method is SDNE, which proposed a new semi-supervised learning model. Combining the advantages of first-order and second-order estimation, SDNE can capture the global and local structural properties of the graph. The unsupervised part uses a deep auto-encoder to learn the second-order similarity, and the supervised part uses a Laplace feature map to capture the first-order similarity. LINE is another method based on deep learning, which optimizes the first-order and second-order similarities . The firg is defined by the negative log-likelihood and is given byIn this paper, we chose two typical IC-based methods to measure the semantic similarity of GO terms, based on p(g) is the frequency of term g and its offspring in a specific GO annotated corpus. N represents the total number of annotated proteins in the corpus. If there are 50 annotated proteins in a corpus and 10 of them are annotated by term g, the annotation frequency of term g is p(g) = 0.2.where Jiang and Conrath and Rel’s methods rely on comparing the attributes of terms in GO. Jiang and Conrath’s method considered the fact that the semantic similarity between two terms is closely related to the nearest common ancestor corresponding to the two terms. The semantic similarity between two terms is estimated by calculating the amount of IC in the nearest common ancestor. Jiang and Conrath’s and Rel’s similarities are expressed as follows:gc is the most informative common ancestor of g1 and g2 in the ontology. Given two proteins Pm and Pn annotated with GO terms Gm = {g1,⋯,gi} and where gm and term g′n, which could have been calculated using IC-based similarity methods.where gi, we use SDNE . For constructing the matrix Dm×n, the element D represents the distance between points vm and v′n and can be expressed as follows:We let W through several lattice points in the matrix. The shortest path is the distance between the set of vectors Vm = {v1,v2,…vm} and We used the DTW distance method to find a path Pi, the low-dimensional feature ωi is directly learned from the GOA graph, which contains the information of term–term and term–protein relations. We use the cosine distance of the proteins’ vector ω to measure the similarity of the proteins. Cosine distance can be expressed as follows:For any protein G, we let E be the complete set of Er in the network. The remaining 80% of the edges Es are then divided into Ep and Et, where Es = Ep ∪ Et, EP ∩ Et = ∅, and E = Er ∪ Es. Given a link prediction method, each pair of unconnected node pairs vx and vy is given a link probability of two nodes. Sorting all the node pairs according to the score value in descending order, we have the top node pair with the highest link probability. The calculation process of the AUC value is presented in L prediction edges. If m prediction edges exist, sort the link probability score value in descending order. If m of the top L edges are in the Et, the precision is defined as follows:When it is difficult to use a unified standard to measure the advantages and disadvantages of a network model, link prediction can be used as a unified comparison method for the similarity nodes in the network. It provides a standard to measure the reliability of the structure of the network. In the comprehensive evaluation, we use two commonly used evaluation indicators, AUC  and AUCPM = E−Er, where m is the number of edges predicted by the prediction algorithm. The recall index is defined as follows:The number of existing edges in the network The similarity between nodes is an essential precondition for link prediction, and the more similar the two nodes are, the more likely that a link exists between them. The similarity of network-based structural information definition is called structural similarity. Link prediction accuracy based on structure similarity depends on whether the structure similarity can grasp target structure characteristics. In the link prediction task, there are many methods to calculate the structural similarity between nodes, such as the following:vx and vy in the protein similarity network, their neighbors are defined as Γ(x) and Γ(y), and the similarity of the two nodes is defined as the number of their CN. The index of CN is defined as follows:Common Neighbors (CN)  similariS represents the similarity matrix and A represents the adjacency matrix of the graph. CN index is based on local information similarity index.where Based on the common neighbors and considering the influence of the node degree at both ends, the Jaccard (JC) similarity index  is propoResource Allocation (RA)  index coKz is the degree of the common neighbors of nodes vx and vy. The calculation process of the RA similarity index is shown in vx and vy.where We downloaded the human yeast protein interaction network from the String database. We then mapped the proteins to the UniProt database, filtered out those proteins that could not be found in the UniProt database, and removed duplicate edges. After filtering, the Yeast dataset consisted of 2,877 proteins with 228,468 interactions, and the Human dataset consisted of 6,882 proteins with 892,054 interactions. Finally, to verify the validity of our calculated protein similarity network, we compared protein similarity and the actual PPI network coincidence degree.This paper only shows the Human dataset experiment results in We selected the protein similarity networks (τ > 0.4) and compared them with the PPI dataset downloaded from the String database to analyze the coincidence degree of the Human and Yeast protein networks. Furthermore, we compared the edge coincidence of the protein similarity network based on different graph embedding methods (as shown in By comparing the GO(DTW) and GOA(cosine) methods, it can be seen that the Node2vec graph embedding method performed best in the GO graph. SDNE and LINE methods performed better in the GOA graph, and there was little difference between them in the GOA graph and GO graph. However, Node2vec and DeepWalk performed better in the GO graph. In general, the performance of protein similarity calculation based on different graph embedding methods in the GO graph was better than in the GOA graph. As shown, using graph embedding methods can be effective in calculating protein similarity in GO and GOA graphs. We also proved that using the DTW method to calculate different dimensional protein vector similarities is feasible.The features of GO terms are learned from the GO graph based on different graph embedding methods, and the similarity among proteins is calculated. By selecting the top 5%, middle 5%, and the last 5% of the protein similarity network data, the link prediction is computed for the filtered protein similarity network, and the AUC and AUCPR values are calculated  method is also better than other graph embedding methods. For the Yeast protein similarity network, we also performed the same experiment and obtained the same experimental conclusions as described above. We found that SDNE graph embedding methods also showed excellent performance in the Yeast dataset  method performed better than GOA(cosine), and most of the protein similarity networks calculated by the GO(DTW) method are denser than those calculated by the GOA(cosine) method.We performed three different link prediction similarity index experiments on the top 5% of the protein similarity network and found that based on different similarity indexes, the difference in the AUC value is small, which indicates that the calculated protein similarity network structure has improved  structure of system definition, and describe the functions of proteins. Therefore, it is important to understand protein function based on GO terms to describe protein similarity.In this paper, by fusing the GO terms’ topology information, we learned the features of GO terms and proteins into vector representations in GO and GOA graph based on different graph embedding methods. Then, the similarity of proteins was calculated based on these vectors using DTW and cosine similarity. Finally, protein similarity networks were screened by selecting different percentages, and a link prediction experiment was used to evaluate the prediction accuracy of different networks. The experimental results indicate that the graph embedding method is better than the IC-based method in protein similarity calculation. Among the two graph embedding methods, the performance of the GO(DTW) method is better than that of the GOA(cosine) method. This is because the GO terms and proteins are treated equally in the GOA graph, and some information may be ignored when learning protein low-dimensional embedding. Therefore, the coincidence degree between the protein similarity network calculated by the GOA(cosine) method and the actual PPI data is not as high as that calculated by the GO(DTW) method. There are potential limitations to our method. First, we transformed directed graphs into undirected graphs, which might result in a loss of structural information. We also treated the GO terms and the proteins equally in the GOA graph, which may ignore some information. Therefore, in our future study, we plan to learn the protein representations in the graph by combining the information in the directed graph and by considering representation learning of heterogeneous graphs that contain GO terms and proteins.The original contributions presented in the study are included in the article/YZ conceived the idea and prepared the experimental data. ZW and YZ debugged the code, conducted the experiments, interpreted the results, and wrote and edited the manuscript. SW and JS advised the study and reviewed the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.