The rapid development of the mobile Internet coupled with the widespread use of intelligent terminals have intensified the digitization of personal information and accelerated the evolution of the era of big data. The sharing and publishing of various big data brings convenience and also increases the risk of personal privacy leakage. In order to reduce users’ privacy leakage that may be caused by data release, many privacy preserving data publishing methods have been proposed by scientists in both academia and industry in the recent years. However, non-numerical sensitive information has natural semantic relevance, and therefore, synonymous linkages may still exist and cause serious privacy disclosures in privacy protection methods based on an anonymous model. To address this issue, this paper proposes a privacy preserving dynamic data publishing method based on microaggregation. A series of indicators are accordingly designed to evaluate the synonymous linkages between the non-numerical sensitive values which in turn facilitate in improving the clustering effect of the microaggregation anonymous method. The dynamic update program is introduced into the proposed microaggregation method to realize the dynamic release and update of data. Experimental analysis suggests that the proposed method provides better privacy protection effect and availability of published data in contrast to the state-of-the-art methods. However, big data is a double-edged sword. Big data publishing without reasonable privacy protection is likely to cause the leakage of sensitive information which may endanger the personal safety of users and of their property, affect personal reputation and physical and mental health, or lead to discriminatory treatments6.Big data is rich in sources, diverse in variety, large in volume,and dynamically updated, and therefore, it contains immense value and information. Big data mining and analysis can be applied to social demographic surveys, public health research, transportation planning, social public opinion analysis, business model surveys and adjustments, agricultural output prediction, biological information analysis, and many other areas of technological innovation and application. Therefore, it has recently attracted the attention of governments, industries, and research departments around the worldK-anonymity privacy protection model8 to deal with privacy leakage. Subsequent theoretical and practical results suggest that although the K-anonymity privacy model cuts off the connection between an individual and his/her record in the published data, there still exists some connections between an individual and his/her sensitive information. For instance, if all the records within the same equivalent class have the same or similar sensitive values, the attacker will directly obtain the sensitive information of all the individuals in the same equivalent class. The l-diversity privacy protection model10 proposed by Machanavajjhala et al. requires that each equivalent class contains at least l different sensitive values, thereby reducing the ability of the malicious attackers to infer sensitive information. However, non-numerical sensitive information has natural semantic relevance and it is,therefore, difficult to avoid synonymous linkage through different sensitive values. For example, Table Age, Sex, Zipcode} and sensitive attribute {Disease}. Table Traditionally, privacy preserving data publishing was realized by deleting identifying attributes that can uniquely identify an individual. Sweeney and Samarati proved that the data subsequent to deleting of the identity attribute may still disclose an individual’s privacy through linking attacks. Therefore, they proposed the Besides, big data publishing is not set in stone. Compared with periodic static data publishing, i.e., census, industry reports, and health statistics , many big data publishing scenarios are characterized in terms of dynamic changes and real-time updates. For instance, in case of location and trajectory information, real-time traffic and weather forecasts, and epidemic monitoring, the data should be updated/deleted/modified with different time intervals or frequencies. The dynamic release and update of big data pose new challenges to privacy preserving data publishing. It may break the privacy protection model built on the published data and increase the complexity of the implementation of anonymous models. In addition, it may aggravate the relevance of published data and external knowledge, degrade the effect of the privacy protection model, and even lead to disclosure of privacy.In order to prevent this kind of privacy leakage caused by synonymous linkage of sensitive values, this paper proposed a privacy preserving dynamic data release algorithm based on microaggregation. Our principal contributions are as follows: (1) A series of indicators are designed to evaluate the synonymous linkages between the non-numerical sensitive values in turn facilitating an improvement in the clustering effect of the proposed microaggregation anonymous method; (2) The improved microaggregation algorithm is proposed to enhance the privacy protection effect of the published data by minimizing the distance between records and the total number of linkages, and for maximizing an increase of entropy; (3) The dynamic update program is introduced into the proposed microaggregation method to realize the dynamic release and update of data.The rest of the paper is organized as follows. Section 2 provides an overview of some related studies pertinent to privacy preserving data publishing methods for static and dynamic datasets. Section 3 introduces the basic indicators used in the traditional microaggregation method. Section 4 depicts the salient ideas and design indicators of our proposed algorithm. The proposed dynamic data release algorithm against synonymous linkage (DRASL) has been put forward in Section 5. Section 6 depicts the experimental results, whereas, Section 7 concludes our paper.K-anonymity privacy protection model is the most widely used one. The core idea of the K-anonymity model is to express the precise value of the quasi-identifier (QI) attribute in a generalized form9. The quasi-identifiers are defined as the attributes that cannot directly identify a unique individual, but is sufficiently relevant and can be combined with other attributes to identify a specific individual. The records in the original data table can be segregated into multiple equivalent classes by generalizing the exact value of the quasi-identifiers into a certain value range. Each equivalent class contains at least K  there are some semantic correlations between the discrete nominal attribute values, the taxonomy tree can be used to define the distance between those values, and (2) the discrete values of a nominal attribute have no relationships whatsoever, proximity measure can be adopted to estimate the distance between such attribute values. The discrete ordinal attribute is an attribute whose possible values have a meaningful order or ranking amongst them but the magnitude between different values is not known.Continuous attributes are quantitative attributes as they may take on any value within a finite or infinite continuous interval. Age is a continuous attribute, Zipcode is a discrete nominal attribute with semantic correlations, Sex and Religion are discrete nominal attributes with non-semantic correlations,and Capitalgain is a discrete ordinal attribute with 3 values {moderate, good, excellent}. A series of distance metrics have been defined to assess the relations of records with multiple attributes.Table Distance for continuous attribute [15]). For any continuous attribute C in data table T, the distance between two values max(C) and min(C) refers to the maximum and minimum value of a continuous attribute C.. For any semantic correlation nominal attribute T, the distance between two values . For any non-semantic correlation nominal attribute N in data table T, the distance between two values p is the total number of non-semantic correlation nominal values exists in N, . For any ordinal attribute O in data table T, the distance between two values rank(v) is the rank of value v in the ordinal attribute O in ascendant order, and |O| is the number of distinct values in ordinal attribute O.(Distance between two records [23]). For a data table T with continuous attributes QIA| is the number of quasi-identifiers in data table T.. Given an equivalent group GID, GID’ represents the equivalent group after adding a record r, the increase of information entropy can be defined as:G with the added record r, H(G) is the information entropy of G, and G.(Microaggregation metric [19]). The microaggregation clustering metric is defined as a function to decide which record is the best choice to join the equivalent group during microaggregation clustering. The function can be be defined as:r, d and , we predefined a catalogue of related diseases for the sensitive attribute Disease . For any sensitive attribute SA, the number of linkage between two values . For a set of sensitive values n sensitive values (Let’s considerate the previous Table entclass1pt{minimaProbability mass synonymous linkage). For any n sensitive values n is the total number of sensitive values (Microaggregation metric against synonymous linkage). In order to minimize the synonymous linkage of sensitive values during the microaggregation process, we introduced the probability mass synonymous linkage on the basis of traditional microaggregation clustering metric. The new function can be defined as:, when some records have changed and are required to be updated, we first delete the old ones form their corresponding equivalent group and then insert the new ones into the most suitable equivalent group and adjust them dynamically. The above functions ensure that our proposed privacy preserving microaggregation algorithm applicable for both the static and the dynamic data publishing scenario.T and a privacy protection parameter K, the first release algorithm returns the anonymous data table GID according to the predefined catalogue of sensitive values. Lines 6-22 illustrates the main process of microaggregation, where the improved microaggregation metric against synonymous linkage . For a input data table Algorithm 2 depicts the dynamic insertion process of the proposed DRASL algorithm. The main steps were derived from the method in [20], but using the improved microaggregation metric against synonymous linkage . r are not only different but also not synonymous linked, because there is Let’s consider the previous clustered equivalent groups shown in Table Lines 3–5, when the size of the equivalent group K, Algorithm 3 will be recalled and the deleted record r and equivalent group Lines 6–19 are the key processes of dynamic adjustment for record deletion. When the size of the equivalent group K, the algorithm deletes GID (Line 7). In Lines 9–10, a record r will be randomly selected and removed from Lines 11–13 evaluate which equivalent group is most suitable for the record r to join in according to the improved microaggregation metric defined in Definition r is added to the corresponding group Lines 14–15), and Algorithm 3 will be recalled to update the clustered equivalent groups GID (Lines 16–18). Algorithm 4 presents the pseudo code of the dynamic adjustment record deletion process of the proposed DRASL algorithm. In Lines 3–7). If the modification happened on sensitive value, Algorithm 3 will be called back to protect the semantic diversity of the modified sensitive value (Lines 8–15).In this section, we propose the microaggregation dynamic adjustment algorithm for record modification, as shown in Algorithm 5. If the modification happened on quasi-identifiers, the old record should be deleted by calling back Algorithm 4 and the modified new record should be inserted into a most suitable equivalent group by calling back Algorithm 2 (l-diversity (Entropy l-diversity)10, the anonymization approach based on t-closeness (t-closeness)11, the one pass K-means algorithm (OKA)25, the improved K-anonymity algorithm based on clustering (IKA)26, and the data privacy protection algorithm based on microaggregation (DPP)19.In order to evaluate the effectiveness and efficiency of the proposed algorithm, we analyze and compare the proposed DRASL algorithm with some state-of-the-art existing algorithms from the term of privacy protection effect, availability of published data, and execution time. The baseline methods include, but are not limited to, the anonymization approach based on https://archive.ics.uci.edu/ml/datasets/Adult) obtained via the UCI machine learning repository, wherein we retained only the attributes, Age, Workclass, Occupation, Education, Capitalgain, Race, and Gender. The sensitive value is Disease which is randomly generated from 32 different diseases based on a predefined catalogue for sensitive attributes (as depicted in Table All the algorithms were implemented in Python and carried out on Huawei Elastic Cloud Server 8vCPUs |32GB| pl2.2xlarge.4 under Windows Server 2016 Standand 64bit for T4 with TESLA. The dataset used for the experiments were composed of seven quasi-identifier attributes and one sensitive attribute. The quasi-identifier attributes were originally selected from the Adult dataset (K-anonymity model primarily protects a user’s sensitive information through the group masking effect which reduces the probability of an attacker to obtain the sensitive information of a certain individual. However, there are always some correlations among the values belonging to the same type of sensitive attribute. Even if all the sensitive values in the same group are different from one another, the semantic relevance between them is inevitable. Therefore, the attacker may not be able to precisely identify the record of a targeted victim but could infer a victim’s sensitive value via the semantic relevance within the same published group. This is referred to as the synonymous linking phenomenon between the sensitive values as already discussed in this paper. The stronger the synonymous linkage is, the weaker is the group masking effect and the larger is the possibility of privacy disclosure.Privacy preserving data publishing method based on the GID be the final set which contains the all the equivalent groups, |GID| is the total number of equivalent groups, and GID can be defined as:In this paper, we use the total number of probability mass synonymous linkage to evaluate the privacy protection effect of sensitive attribute on the published data. Let l-diversity method and the t-closeness method are typically designed for anonymous static data publishing. Therefore, the data update process of these two methods is realized by performing corresponding number of record insertion, deletion, and modification on the static dataset.Figures K increases, the total number of probability mass synonymous linkages of all the algorithms gradually decreases. In the case of dynamic update for records insertion and deletion, different numbers of records are added or deleted respectively and the size of the published group is set to be According to the definition described in Equation 13, the probability mass synonymous linkages will decrease with an increase in the total number of sensitive values within the same published group. Accordingly, we can observe from Fig. l-diversity method mandates to maintain the different values of sensitive attributes. However, it ignores the semantic relevance between them and is vulnerable to skewness attack and similarity attack. The t-closeness method makes the distance between the distribution of sensitive attributes in an equivalent class and the distribution of attributes in an entire data table to not exceed the threshold t. Nevertheless, it still cannot fundamentally prevent the synonymous linkage attack. Therefore, the proposed DRASL algorithm can provide better privacy protection for the published data from the aspect of preventing synonymous attacks.In all of the above dynamic update situations, the proposed DRASL algorithm is superior in contrast to the other algorithms in almost all of the cases. The primary reason is that the proposed DRASL algorithm is based on the criteria of minimizing synonymous semantic linkages during the process of selecting records and adjusting the aggregation of equivalent groups. The DPP method fulfills the clustering process with the criteria of minimizing an increase in entropy. It does not consider the semantic relevance between the sensitive values at all, and therefore, performs the worst amongst all the comparison. The Entropy K-anonymity model reduces the availability of the published data to a certain extent. The primary reason is that the generalization operation carried out on the quasi-identifiers directly reduces the accuracy of the published data. The greater the degree of generalization is, the lower is the availability of the published data. In this paper, we use the average information loss to evaluate the availability of published data generated via different clustering and microaggregation algorithms.Privacy preserving data publishing method based on the G be an equivalent group and |G| be the total number of records. The amount of information loss that occurs in G can be defined as:G according to Definition Let GID be the set of all the equivalent groups and |GID| be the total number of equivalent groups in the set GID. The average information loss of the set GID is defined as:T| signifies the total number of records in the data table T.Let k increases, the average information loss of all the algorithms gradually decreases. In the case of dynamic update of records insertion, deletion, and modification, the change in average information loss is much more complicated. Since the size of the published group is set to be Figures mentclasspt{minimaBoth the DPP algorithm and the proposed DRASL algorithm use the insertion of forged records to realize the dynamic update and adjustment of the released data. Table K anonymous privacy protection for data release. However, their specific clustering process and evaluation indicators are different from one another.Figures l-diversity method, the t-closeness method, the DPP method, and the proposed DRASL algorithm follows a same principle, i.e., each of the equivalent group begins with a randomly selected record and continuously select the most appropriate record to join the group in accordance with different criteria. For the Entropy l-diversity method and the DPP method, the criteria is to minimize the increase in entropy. For the t-closeness method, the criteria is to keep the deviation between the distribution of sensitive attributes in an equivalence class and the distribution of attributes in an entire data table within a threshold t. On the contrary, our proposed DRASL algorithm takes the comprehensive effect of the distance between the records, the increase in entropy, and the number of synonymous linkages between sensitive values into consideration during the clustering process. As the clustering progresses, each equivalent group needs to incorporate records until the number of records reaches at least K. For a dataset with x records, all of the above mentioned methods have a time complexity to the order of l-diversity method, the t-closeness method, and the DPP method, and is second only to the OKA algorithm.The clustering process of the Entropy x records, the OKA algorithm first splits all the records into K-anonymity. Therefore, it has a time complexity of the order of For a dataset with For the IKA algorithm, the calculation of the distance between the first centroid and the remaining records should be carried out for K-anonymity model, l-diversity model, and their improvement strategies cannot effectively prevent the semantic linkages between the non-numerical sensitive values, thereby leading to privacy leakage problems. To address such an issue, this paper proposes a dynamic data publishing algorithm based on microaggregation. A series of indicators are designed to evaluate the synonymous linkages between the non-numerical sensitive values and to improve the clustering effect of the proposed microaggregation anonymous method. The dynamic update program is introduced into the proposed microaggregation method to realize the dynamic release and update of data. Experimental analysis suggests that the proposed method provides a better privacy protection effect and availability of published data in contrast to some state-of-the-art methods.The research on privacy preserving data publishing is indispensable for the further innovation and development of the promising paradigm of big data. However, data publishing methods based on The privacy preserving microaggregation data publishing method proposed in this paper can be applied to a number of data publishing scenarios encompassing both numerical and categorical attributes. With the help of a distributed computing framework, the proposed method can also be run on a big data processing platform to realize a large volume of big data publishing requirements. Nevertheless, it is unable to provide privacy protection for other types of big data publishing scenarios such as unstructured data and graph data, and therefore, how to achieve privacy preserving data publishing for these types of data will be the target of our futuristic work.


An integrated hybrid membrane process, composed of a diffusion dialysis (DD), a membrane distillation (MD) and a reactive precipitation unit (CSTR), is proposed as a promising solution for the valorization and onsite recycling of pickling waste streams. An economic analysis was performed aiming to demonstrate the feasibility of the developed process with a NPV of about EUR 40,000 and a DPBP of 4 years. The investment and operating costs, as well as the avoided costs and the benefits for the company operating the plant, were analyzed with an extensive cost tracking exercise and through face-to-face contact with manufacturers and sector leaders. A mathematical model was implemented using the gPROMS modelling platform. It is able to simulate steady state operations and run optimization analysis of the process performance. The impact of key operating and design parameters, such as the set-point bath concentration and the DD and MD membrane areas, respectively, was investigated and the optimal arrangement was identified. Finally, operating variables and design parameters were optimized simultaneously in a nonlinear framework as a tradeoff between profitability and environmental impact. We show how the integration of new technologies into the traditional pickling industry could provide a significant benefit for the issues of process sustainability, which are currently pressing. A fresh acid consumption range of 10–30 kg/tongalv.steel and a spent liquor production of 15–45 kg/tongalv.steel are reported [The harsh and outdated hot dip galvanizing practice is still the primary process used globally for metal surface treatment, along with the pretreatment pickling process for scale and impurities removal from steel products. Pickling waste liquors pose a crucial problem for company economics and, even more, for its consequent environmental impact. AIZ  carried out a survey within the framework of the ReWaCEM European project  . The IPPWith this increasing attention to environmental aspects alongside profitability, an integrated hybrid membrane process composed by a diffusion dialysis (DD), a membrane distillation (MD) and a reactive precipitation unit (CSTR) is presented here. The process feasibility was studied and proposed in previous authors’ works. Process design was carried out by adopting an earlier developed process simulator  and releHere, economic considerations regarding the integration of the process in the industrial environment are presented, firstly for a hot-dip galvanizing company in Italy  and then also considering medium-size throughput treatment plants. Diffusion Dialysis is counted in the BAT recommendations , as it hThe huge potential of considering an onsite treatment process in the galvanizing industry concerns (i) the reduction or total elimination of the disposal waste to treat and (ii) the avoidance its transportation towards treatment sites. The present work is devoted to exploring such potential and in particular to proving that the integration of the treatment and recycling process will be a cost-effective solution, as already found for different recovery processes in the same field . The intIntroducing further environmental aggravations should be avoided, as could happen by increasing the process water consumption with related increasing water streams flow rate to be included in the industrial water circuit . An opti+), these can easily pass though the AEM with the chlorides, thus allowing the recovery of the free acid thanks to the diffusive mass transport driven by the concentration difference between the waste acid and the recovered acid solutions. A detailed description of the process was provided in a previous authors’ paper [In s’ paper .The recovered acid solution, still at a relatively low acid concentration, due to the need of keeping a large driving force in the DD unit, is processed in a Membrane Distillation (MD) unit. In MD, an intrinsically hydrophobic microporous membrane separates the heated acid solution from a cold channel. Driven by the vapor pressure difference (induced by the temperature difference at the two sides of the microporous membrane) water vapor (generated by the liquid–vapor equilibria at the hot liquid–membrane interface) passes through the membrane pores and condenses on the other side of the membrane on a condensing surface kept cold by a an external cooling water stream. Such condensed low concentration acid solution is, eventually, used as DD drawing solution after the mixing with another freshwater stream. On the other side, the solution flowing in the MD hot channel is concentrated in hydrochloric acid and less volatile components, such as the metal salts, and is recycled back to the pickling bath. The MD heating requirements are accomplished by exploiting the available industrial waste heat through the integration of the technology with the industrial heating water network. More details of the process were provided in a previous paper by the authors .The brine rich in metals exiting from the DD process is directed to a reactive crystallizer (CSTR), where iron (II) ions are oxidized into iron (III) ions by the addition of hydrogen peroxide and precipitated by addition of ammonia, while zinc chlorides and ammonium chlorides remain in solution, thanks to their high solubility, thus generating the so-called fluxing solution, useful in a step of the hot-dip galvanizing chain. Of interest, a detailed description of the crystallization process was provided in a previous authors’ paper .A pilot scale plant of the proposed process was recently designed, built and installed at the Tecnozinco S.r.l. hot-dip galvanizing company in Carini . Detailed characteristics of the process and the pilot system are reported in the authors’ previous work .® simulation platform. All the main equations of the integrated model for the different sections, consisting in simple mass balance and transport equations, are summarized in An integrated model of the entire treatment chain was already developed and implemented using an Excel spreadsheet in order to simulate the stationary operation of the process . In the A standard profitability analysis was carried out by following a well-known procedure proposed by Turton et al.  to evalu3/h of treated acid). As Tecnozinco S.r.l. can be considered a small company in the hot-dip galvanizing sector, higher capacity plants were investigated in order to consider the case of a more significant economical return as expected for a larger portion of companies.Pilot plant capacity was designed in order to be able to treat only one of the seven pickling baths of the Tecnozinco S.r.l. plant. Here, for a consistent  economic analysis, the capacity of the integrated treatment process was scaled-up in the calculation to the full Tecnozinco capacity  to be treated in the integrated process, as it represents the capacity variable of the proposed treatment process. Costs reported in , green field development costs or grass root costs [The Fixed Capital Investment (FCI) estimation was assessed through an extensive cost tracking carried out during the realization of the pilot unit and with the help of manufacturers. Results of the analysis for the demo plant and for the up-scaled sizes are reported in ot costs ) are notspecific membrane module cost was derived from data provided by DD and MD module manufactures and values of EUR 470/m2 and EUR 310m2 for the DD and the MD modules were obtained, respectively. In particular, these costs refer to the whole technology and not only to the membrane cost. The FCI can be conceived as the product of a “lump factor” and the cost of the membrane technologies (Equation (34)). The lump factors at different scales were extrapolated from the corresponding FCI values, thus resulting in a power law of the treatment capacity (Equation (35)). This equation will be used later on in the optimization section FCIsix tenths rule” was performed . The subscript a indicates the equipment with the required “attribute”, while b indicates the equipment with the base attribute, at which normally the purchase cost is known. In the present case, the feed flow rate can be chosen as “attribute” and the law can be plotted for comparison with the values reported in For a validation of the trend obtained for the Fixed Capital Investment vs. the plant capacity (feed flow rate), a comparison with the well-known “rmed see . The “si follows :(36)Ca=C3/h–10 m3/h), while a higher discrepancy is observed in the low-size range (0.1 m3/h to 1 m3/h).The curve obtained in the present work is more conservative and predicts higher investment cost for the low- and medium-size capacity plants. The results obtained are comparable within a wide range of capacity, estimated .It is worth noting that treatment plants with a capacity greater than or equal to 10 mThe yearly operating expenditure (OPEX) estimation was based on the value of the FCI, the cost of operating labor (COL), the cost of raw material (CRM), the cost of waste treatment (CWT) and the cost of utilities (CUT) following the indications of Turton et al.   Direct Costs, (ii) Fixed Manufacturing Costs and (iii) General Manufacturing Expenses. In this OPEX definition the depreciation is not taken into account: it is taken into account in the overall expenses (see following paragraph). Inputs for the calculation of the different OPEX items were collected and reported in RM concerns HCl make-up and reactants for the reactive crystallizer. The HCl industrial price is very variable since, in the relevant sector, it is a by-product of other processes and its price depends on several factors, such as the abundance in the specific geographical area or in the particular time period, and the incidence of transport costs. The chosen and reported price is mainly based on Tecnozinco knowhow. Moreover, the HCl makeup amount to consider is the difference between the design value  and the current Tecnozinco nominal consumption, as only this difference would result in an extra cost for the company. For the NH4OH and H2O2 reactants, Tecnozinco purchasing costs were considered as well. It is worth noting that all the selected purchasing costs are the highest in the relevant range reported in CWT is considered zero for the present process.CUT plant utilities consist of process water re-filling and electricity. Thermal energy is provided in the form of waste heat. Therefore, thermal energy cost is assumed to be zero. Electricity is mainly consumed by pumps, controls and switch cabinet cooling.COL, since the pilot plant principally operates automatically, it was considered only a quarter of an engineer position for maintenance and operation. With an assumed engineering salary of EUR 44,000/year this leads to EUR 11,000/year.CProduct), a saving for the reduced waste acid production and corresponding disposal , and for the enhanced production (Enhanced Prod.) for the optimal pickling performance of the baths have to be counted. In particular, SProduct refers to solid iron hydroxide, which is the direct saleable product, and the fluxing solution, which is the recyclable product. The latter is considered a revenue contribution as it is an avoided expense for the company.For the yearly revenues (R) estimation, the following aspects have to be taken into account. Along with the direct revenues related to the selling or re-cycling of products , a saving flow rate was estimated as the flow rate corresponding at a Fe concentration disposal of 185 g/L as reported in Equation (38).In order to account for the avoided disposal cost . Pickling performances are dependent on several factors: temperature of the bath, acid and iron concentration, inhibitor choice and concentration [Another revenue contribution comes from increased production  concentration: 137 g/L; average pickling time 8.3 min, red dot in Enhanced Prod. Factor does not show any variation in terms of productivity. However, if we move to a higher HCl operating concentration, increased production will result. Moreover, a reduction factor will be obtained if the acid working concentration is below the nominal case (refer to The implementation of the proposed process will affect mainly the composition of the bath in terms of acid and iron (II) concentration. In particular, literature values from Kleingarn were extrapolated and processed in order to derive a minimum pickling time curve with the hydrochloric acid concentration, reported in refer to b.Enhanced Prod. Factor.As defined, this is related to the particular plant operation, and it has to be tuned for the specific company considered. However, a typical composition for the relevant industrial sector could be used in order to generalize the Enhanced Prod. is expressed as follows:Thus, the increased production Enhanced Prod. manufactured steel flow rate.Finally, the yearly revenues were estimated as follows (Equation (40)):Fluxing Solution saving and the Added value of the Enhanced Prod. have been carefully discussed and agreed with the industrial partner, thus making analysis more reliable. Disposal costs faced by Tecnozinco S.r.l. include transportation to a far waste treatment plant in northern Italy (nearly 800 km). The disposal cost amounts to about EUR 80/ton, which is in agreement with values reported from Stocks et al. [The selected costs for the s et al. , while tThe simple straight-line depreciation method was applied by following the procedure described by Turton : the totThe yearly plant expenses are evaluated as the sum of the annual manufacturing cost (OPEX) and the yearly depreciation. A fixed taxation rate of 33% of profits , was assumed. Clearly, profit is the difference between annual revenues (R) and expenses. Thus, the net cash flow was evaluated as the net profit after tax plus the yearly depreciation. A discount rate equal to 4% was assumed.All the inputs for the profitability analysis relevant to the Tecnozinco case study ):The resulting cumulative discounted cash flow (CDCF) diagram, calculated as reported in Equations (42) and (43), is shown in Results are presented in terms of the main profitability indexes, whose definitions are reported in Optimization analyses on the process performance in steady state operations were run on the gPROMS modelling platform. Optimization is required to create greater insight into the proposed process showing when integration into the traditional pickling industry could provide a significant benefit. The impact of key operating and design variables was investigated, and optimized configurations are presented in following section. As the Tecnozinco plant  as the ratio between the amount of acid recovered and the MD inlet acid amount, presents a non-monotonic trend. At low ConversionRatio decreases despite the increasing of acid concentration in the recovered pickling solution, as the recovered amount is lower compared to the inlet acid (in the recovered acid solution). Then, at the higher waste acid concentrations, which means higher acid concentrations in the recovered acid solution, the water vapor pressure decreases, thus reducing the water extraction through the MD unit, causing an increase of the conversion ratio (AcidCosum (Equation (6)), i.e., the make-up acid for processed steel, and the WaterRatio (Equation (33)), which is significant of the plant water demand; both of these decline rapidly as the HCl pickling bath concentration increases  as the ratio between the fresh process water demand and the drawing DD feed solution. To this purpose, a Pareto Frontier was found by using the ε-constraints method [WaterRatio value, thus converting a multi-objective analysis to a parametrized single-objective one.The trade-off value was evaluated using a multi-objective optimization approach, in order to find out the preferred solutions that maximize the NPV objective function while minimizing the s method , construWaterRatio up the value of 90%. A strong sensitivity is observed, as a slight reduction of the water utilization leads to a significant increase of the MD membrane area and thus of the costs. As a consequence, the NPV of the plant is firmly reduced, making this configuration unprofitable for a WaterRatio of about 80% as shown in In In the same figure, a second scenario is also shown. In order to reduce the water demand, a second MD Brine unit can be included in the process in order to concentrate the fluxing solution and recover a part of the process water to be used as drawing media in the diffusion dialysis  to simulate steady state operations of the process and (ii) to perform optimization analyses. The optimization problem formulation was aimed to maximize the net present value of the process using, firstly, operating parameter (acid concentration) and then, also the design variables (membrane modules areas) as control variables. The process was found profitable for the low size capacity of the Tecnozinco plant and, more importantly, the profitability is much more evident at larger scales. Indeed, for the real industrial scenario  plant, results show a DPBP of 4 years and, assuming a project duration of 5 years, a NPV value of about EUR 40,000 was obtained. Furthermore, the reduction of ecological burden gives a significant valuable benefit in all the scenarios investigated.The above findings indicate that the technology is profitable and can be easily integrated into industrial plants. The multi-objective optimization highlighted the possibility of increasing the NPV up to about EUR 75,000, with a water ratio of 85%, by including in the integrated process a second MD unit, which allows the reduction of water use. The proposed technology shows a huge potential for the relevant industry sector as it provides a solution for the simultaneous reduction of costs and environmental impact, thus moving the pickling process towards a higher level of sustainability. Future research directions are oriented towards the further testing of the system at the pilot scale, followed by the system scale-up and application to real scenarios.


As an extension of the support vector machine, support vector regression (SVR) plays a significant role in image denoising. However, due to ignoring the spatial distribution information of noisy pixels, the conventional SVR denoising model faces the bottleneck of overfitting in the case of serious noise interference, which leads to a degradation of the denoising effect. For this problem, this paper proposes a significance measurement framework for evaluating the sample significance with sample spatial density information. Based on the analysis of the penalty factor in SVR, significance SVR (SSVR) is presented by assigning the sample significance factor to each sample. The refined penalty factor enables SSVR to be less susceptible to outliers in the solution process. This overcomes the drawback that the SVR imposes the same penalty factor for all samples, which leads to the objective function paying too much attention to outliers, resulting in poorer regression results. As an example of the proposed framework applied in image denoising, a cutoff distance-based significance factor is instantiated to estimate the samples’ importance in SSVR. Experiments conducted on three image datasets showed that SSVR demonstrates excellent performance compared to the best-in-class image denoising techniques in terms of a commonly used denoising evaluation index and observed visual. A broad variety of digital images are widely used in applications such as medicine, remote sensing, military, and robotics . Owing tAs a generalization of SVM, SVR attempts to find the flattest region containing most of the training instances in the feature space. By constructing an optimal objective with the loss function and the geometrical properties of the region, SVR predicts actual values according to the structural risk minimization principle . During To increase noise robustness, some sample prior information is used to improve the performance of SVR. Qiang et al. introduced a robust weighted linear loss SVR for large-scale classification by punishing the rest class samples . Luo et It is noteworthy that the noise information of pixels is critical in image denoising. Considering the high relevancy of neighborhood pixels and the disturbance of noise, obtaining the samples significance estimation remains challenging. In this paper, the SSVR method is proposed with optimized noise robustness and applied in image denoising. The significance factor based on spatial density information is calculated for each sample pixel to measure the noise information of images. Then, the significance factor is adopted as a weight coefficient of the penalty parameter in SVR for reducing noise interference in the model training process. The contributions of this paper are summarized as follows.(1)A significance estimation framework based on spatial density information is introduced to evaluate sample importance.(2)SSVR is proposed as an improved SVR with sample significance. In SSVR the significance factors are attached to samples as the weight coefficients during model training.(3)As an implementation of SSVR with sample spatial density, cutoff distance-based SSVR with enhanced noise robustness is successfully applied in image denoising with remarkable results.ix, iy} , where ix is a sample data vector, and iy is its corresponding value. Then, the goal of SVR is to minimize the sum of the squared errors  is the structure risk, and empR[f] represents the empirical risk. Substituting Equation (13) into Equation (14), we obtainFor significance weight In this study, the empirical risk is minimized using SSVR. If the number of samples is sufficiently large, it is reasonable to assume that As an example, this paper implements a cutoff distance-based SSVR and applies it to image denoising. In application, the more accurate the priori information is, the more effective the improvement of the noise robustness of the SSVR model. From the intuitive two-dimensional data example, the sample significance introduction can better improve the noise denoising effect than traditional SVR.ix and jx is stored in vector sortD in order from small to large. The cutoff distance ε is defined following  in all experiments. To compare performance on image denoising, the peak signal-to-noise ratio (PSNR) and signal-to-noise ratio (SNR) are used as follows.The images CAMERAMAN, LENA, and PEPPER from USC-SIPI are used in the experiments because they are commonly used as image processing benchmarks. CAMERAMAN is used to train the models for all methods to denoise LENA and PEPPER. The value of each pixel of the images is adopted as a feature, and the size of the sample windows is σ and trade-off parameter C in range  are distributed not only in its own channel but also in the vicinity of  in the other two channels, so the training sample is a vectorization of the pixel cube. The SSVR regression model for each channel uses the same weight distribution.This section extends the application of the algorithm to color images of natural objects in daylight. In color image denoising experiments, different models are trained for each channel and then the individual denoised channel maps are combined into a single image for evaluation. The aim of SSVR is to learn a more precise regression hyperplane by using sample distribution information to capture the relationship between pixels and their neighbors. In the color image, the neighboring pixels of a single channel pixel (In the experiment, the pixel values of the RGB channels of each image are used as denoising image features. The size of the window is In the first experiment, each channel of image C1 is contaminated with Gaussian noise with a variance of 0.04 as training data. Each channel of the testing images is contaminated with Gaussian noise variance varying from 0.02 to 0.06. The results are shown in Because non-uniform noise is widespread in multichannel images, we also apply SSVR to non-uniform noise. In the testing images, different channels are added with different degrees of noise. The two results in Traditional SVR rarely considers the effect of sample distribution information during the model training process. To overcome this drawback, SSVR improves noise by introducing significance measurement information based on spatial density information as the refining coefficient into SVR. The refining coefficient for the penalty parameter in SVR could improve the importance of normal data and reduce the influence of noisy data in the solution process. As an implementation example of SSVR, a significance factor which utilizes the cutoff distance as a spatial density estimation method was proposed and applied on image denoising. Experimental results demonstrated that the proposed SSVR improved with samples significance information could achieve effective performance on image denoising. Further research will focus on applying the proposed method in wide fields such as bearing vibration signals and acoustic emission signal denoising.


To develop a goal-oriented indicator system based on the balanced scorecard (BSC) concept, which takes into account the perspectives of the referring physician and patient and emphasizes the focus on the internal processes of the radiology department.Development of a BSC occurred in six steps: (Step 1) strengths/weaknesses and opportunities/risks (SWOT-) analysis of the radiology department, (Step 2) setting-specific objectives  followed by the development of 4 perspectives, (Step 3) and definition of strategic issues oriented to the value-added chain of the processes of the radiology department. (Step 4) Creation of a “Strategy Map” with regard to the perspective and their cause–effect relationships. (Step 5) Development of an automated key performance indicator (KPI) cockpit for the monitoring, reporting, and management scorecard.A total of 10 success factors were identified using SWOT analysis. The core values include high quality in clinical, teaching, and research areas. The radiological value-added chain is composed of three processing steps. 1. registration, 2. examination, and 3. reading/X-ray demonstration. Three action programs were derived: 1. increase competency , 2. improve referring physician/patient satisfaction, 3. increase productivity. Daily process monitoring was added to the management cockpit as a monitoring scorecard. The scorecard comprises 18 KPIs and is automatically updated every month. The annual management scorecard comprises 10 KPIs.The BSC makes it possible to implement a strategy for radiology that is strongly oriented toward the requirements of the referring physicians and the demands of patients. The implantation of a Balanced Scorecard concept in radiology represents a strategic management tool, which takes into account the perspectives of the referring physician and patient and emphasizes on the internal processes of the radiology department.Focusing on the value-added chain of the radiology department (process orientation) represents the fundamental idea of Balanced Scorecard for radiology.Balanced Scorecard based monitoring can help to overcome the weaknesses of a traditionally budget-oriented reporting system.Radiology departments in a hospital function as a central internal cost center for other clinical departments and clinics (profit centers), such as the internal medicine, surgery, or emergency departments. As a rule, a radiology department usually is defined as a clinical institution with its own budget and cost responsibilities without its own wards  or outpatient clinics , 2. RadiWhile the revenue side is closely related to the type and number of examinations performed, the cost structure in a radiology department is characterized by high personnel costs  on the one side and high technology-related, fixed costs on the other. Smooth organization of the processes with respect to personnel deployment, patient waiting times, and diagnosis ensures effective cooperation with requesting and referring clinics as well as the efficiency of the department. Therefore, the focus of management in the radiology department and the greatest optimization potential is in the design and organization of work processes. Traditional reporting in the radiology department does not consist of special measured values and key performance indicators (KPIs) that document and evaluate the work processes. Therefore, it is necessary to expand the current reporting system to focus on factors such as the work processes and use of resources and to be able to achieve goal-directed planning and management of added value for both patients and referrers as primary clients.FinancesMarket and clientsInternal business processes/work processesLearning and growth (innovation)/potentialKaplan/Norton developed a multidimensional system of indicators at the beginning of the 1990s known as the balanced scorecard (BSC) to serve as a management concept for implementing strategies. BSC is a concept for measuring, documenting, and managing the activities of a business or an organization with regard to the implementation of its vision and strategy with the intention of balancing the four perspectives of the services . BSC is Therefore, the perspectives should not be considered independently but in a hierarchical structure according to Kaplan and Norton . Among oIn Health Care Organizations, it becomes more important that department heads serve as strategic managers with the ability to evaluate a changing industry, analyze data, question assumptions, and develop new ideas . Kaplan A Daily Monitoring Scorecard depicts the current performance with the aid of process key values.A Monthly Reporting Scorecard replaces the monthly reporting system, summarizes the most important financial KPIs, adds to the internal processes (work processes) through use of KPIs, and links strategic management with operative controlling.An annual balanced scorecard serves as the basis for an annual reporting, summarizing all perspectives and used for strategic management.To address this lack of strategy-based management, Friedag and Schmidt have developed a highly practical approach to implementing BSC and have extended the concept to three consecutive scorecards Fig.  7]:A Da:A Da7]:AFor the radiology department as the internal cost center of the hospital, there is a large potential for improvement both in the sense of quality and the cost efficiency of internal processes. Therefore, internal monitoring of work processes  offers itself as an addition to the report and annual balanced scorecards. Daily monitoring is conducive to operative management of the department. The results of process monitoring are included in the report and annual balanced scorecards in an aggregate form.The development of the BSC for the radiology department at our university hospital and its service portfolio occurred over six steps Fig. : . The derivation of the key objective was performed through analyses of uniqueness, target clients, and the needs of their clients. The third implementation step was the development of four perspectives of the BSC design as well as the strategic areas of activity in the perspectives. The classical hierarchical arrangement of the perspectives according to Kaplan/Norton was modified in terms of the environment and for use in the radiology department. Instead of the financial perspective, the client perspective in terms of a cost center and the medical result dimensions was chosen as the highest hierarchical level. Unlike other clinical institutions in the hospital, the radiology department serves two categories of clients: patients and clinical referrers . We grouped both client categories as part of the client perspective, creating the patient and referrer perspective. This was then followed by the financial perspective, the internal process perspective and, finally, the employee perspective.Strategic themes for each level based on this BSC structure were defined and oriented on the value-added chain of the processes in the radiology department. The radiological value-added chain consists of three basic process steps: 1. patient registration and the requested examination, 2. realization of the examination, and 3. findings/X-ray demonstration. Important time-dependent measured values in terms of process efficiency linked to these core processes were identified in a subsequent implementation step; these are closely linked to quality and profitability requirements on the client and financial levels.The focus of the fourth implementation steps was the creation of a strategy map Fig. , which pNext, potential KPIs based on the thematic focus as well as the area of activity regarding the other perspectives and their cause/effect relationships were identified, which could be consulted with objective-oriented assessment of each core aspect. Possible process KPIs could be derived, e.g., from automatically measurement timestamps from the radiological information system (RIS). In total, nine different timestamps were collected. Additional KPIs were collected through literature research. For the referrer and employee perspective, KPIs from the radiology department annual electronic referral and employee survey were available.For the subsequent selection of KPIs for the BSC, a unique evaluation concept with the acronym CSTC was developed. Based on four quality criteria, all previously compiled KPIs were evaluated and prioritized using a numerical ranking scale based on Concision, Survey effort, Topicality and Clarity. The fifth implementation step consisted of a final selection and definition of KPIs using a Horváth and Partner filter as well as the derivation of action programs . In the SWOT analysis resulted in a complete picture of the current situation as well as the important challenges for the hospital and the radiology department as a basis for strategic planning Table . From thMainline for-profit companies accepted BSCs in the meantime, but non-for-profit healthcare organizations were slow to adopt them for use. A number of problems face the healthcare industry, including cost structure, payor limitations and constraints, and performance and quality issues that require changes in how healthcare organizations, both profit and nonprofit, manage operations . AlreadyIn peer-reviewed journals in the medical field, there have only been two studies published on the application of BSC in a radiology department. An article from Mauer et al. presents a conceptual BSC for an academic university radiology department with focusing on teaching and research . In thisDonnelly et al.  present The BSC concept allows for the development and implementation of a strategy for the radiology department, which is closely aligned with the requirements of the referring in- and outpatient clinics on the department as a cost center and considers patients to be the true clients. The BSC makes available a new management instrument for the radiology department of the Jena University Hospital with a measurable target not only to use existing potential efficiently but also to develop new potentials. The methodology of our BSC used and adjusted for the radiology department is not a cookbook recipe that can be used immediately for other clinical institutions and should always be adjusted methodically to align with the mission and the vision of each institution or hospital. Focusing on the value-added chain of the radiology department (process orientation) represents the fundamental idea of BSC for radiology. This approach can serve as a guide for other radiology departments at other hospitals when establishing a BSC. Individual KPIs can also certainly be used without modification. In these cases, it should be tested whether these align with the strategy and success factors of each institution. It is not recommended to simply adopt KPIs even within the same specialization.After the implementation of our BSC, the different KPIs were used and accepted in different ways. The process KPIs showed their effect in daily monitoring. In addition, these process KPIs are made available to the internal referring clinical departments in monthly reports for the use of radiological services. KPIs such as the waiting time for examination appointments or radiological reports are the focus of interest. The annual BSC is used exclusively for the business planning and serves as an overview for the board of directors of our hospital for the operational results of the radiology department. For the personnel requirement calculation, the total performance figures, the utilization of the devices, and the minute-by-minute staff retention time of radiologists and technicians are used.A strategic situation analysis including defining long-term-oriented objectives should always be the first step before defining KPIs for a BSC. The selection of KPIs should be conducted in reference to set-thematic focuses and a quality evaluation that also takes into account the survey effort and consequent practical feasibility for KPIs. An important stipulation when establishing a BSC for the Jena University Hospital radiology department was that a paper-based survey of KPIs would not be used. The KPIs survey should be completely automated. The results of the BSC would be communicated through a web portal in an electronic management cockpit. That ensured that the survey for the BSC in the radiology department would not and will not require additional personnel from the management division. At Jena University Hospital, the process and financial KPIs are all generated fully automatically via database queries. The Human Resource department data, on the other hand, must still be entered manually. In our experience, these KPIs are unfortunately not regularly updated, such as the level of illness. As a result, the personal KPIs can only be meaningfully evaluated within the framework of the annual BSC. This clearly shows how important automatic digital data transmission is for a BSC in order to use it effectively as a management tool.Overall, this example of the radiology department shows that BSC-based monitoring can also help hospitals to overcome the weaknesses of a traditionally budget-oriented reporting system and to be able to use different scorecards to meet the different requirements of a goal-oriented strategic and operational management.


GPGPUs has gradually become a mainstream acceleration component in high-performance computing. The long latency of memory operations is the bottleneck of GPU performance. In the GPU, multiple threads are divided into one warp for scheduling and execution. The L1 data caches have little capacity, while multiple warps share one small cache. That makes the cache suffer a large amount of cache contention and pipeline stall. We propose Locality-Based Cache Management (LCM), combined with the Locality-Based Warp Scheduling (LWS), to reduce cache contention and improve GPU performance. Each load instruction can be divided into three types according to locality: only used once as streaming data locality, accessed multiple times in the same warp as intra-warp locality, and accessed in different warps as inter-warp data locality. According to the locality of the load instruction, LWS applies cache bypass to the streaming locality request to improve the cache utilization rate, extend inter-warp memory request coalescing to make full use of the inter-warp locality, and combine with the LWS to alleviate cache contention. LCM and LWS can effectively improve cache performance, thereby improving overall GPU performance. Through experimental evaluation, our LCM and LWS can obtain an average performance improvement of 26% over baseline GPU. The general-purpose graphics processing unit (GPGPU) is one of the most mainstream acceleration components in the field of throughput-oriented high-performance computing ,2. The hThe special multi-thread execution mode of GPUs determines that the memory access efficiency plays a crucial role in the performance of the application. Since a large number of threads may simultaneously issue memory access requests at the same time, if the design of the storage hierarchy cannot effectively respond to this access pattern, causing these memory access requests to flow to the off-chip DRAM, it will cause a large number of threads to wait for data, thereby significantly reducing GPU computing effectiveness. If the application’s memory access behavior cannot reasonably match the design of the on-chip GPU storage hierarchy, and the potential data locality is not well used, GPU performance will be limited. Therefore, GPU memory analysis and optimization have become issues of high concern in GPU performance optimization.The main purpose of this paper is to improve GPU performance by reducing cache contention. Cache contention refers to the case when two processing units try to update the same cache lines. In the GPU data cache, the L1 data cache is especially significant for reducing memory access latency and meet high bandwidth requirements. Due to the large number of multi-threaded tasks in the GPU, the cache space required by the application often exceeds the L1D capacity. For example, each streaming multiprocessor (SM) in the Fermi and Kepler architecture supports 1536 threads and 2048 threads, respectively. However, the capacity of the L1 data cache shared by these threads is only 16 KB or 48 KB .In order to reduce memory access, the GPU integrates the memory coalescing unit into the load/store unit (LDST unit), so that once instructions needing to access memory are executed, many memory requests generated by a single warp would be coalesced into a few memory transactions, improving the efficiency of memory access. However, it is only when the memory requests from a single warp are regular, coalesced into several cache blocks, that the memory coalescing effectively improves performance. Current GPGPU applications always have irregular memory requests. The requests from the same warp cannot always be coalesced. This non-coalesced memory access often leads to memory divergence ,9. That This paper introduces the challenges encountered by massive memory accesses under the massive multi-threaded architecture of GPGPU, and data cache plays a vital role in it. We analyzed the cache contention in GPGPU applications, and we can take advantage of the different locality characteristics.We propose Locality-Based Cache Management (LCM), combined with the Locality-Based Warp Scheduling (LWS), to improve GPU performance. First, according to the locality characteristics of the load instruction, improve the cache utilization by bypassing the streaming data, and extended inter-warp memory request coalescing to make full use of inter-warp data locality. LWS alleviates cache contention by applying different warp scheduling strategies according to cache performance. By adopting different optimization strategies for different locality characteristics, LCM and LWS can effectively improve cache utilization.In the GPGPU system built by the GPGPU-Sim simulator, we evaluate the LCM and LWS. The experimental results show that the LCM and LWS can improve system performance.How to improve cache utilization and improve cache performance is the key to improving the GPU processing power, improving the resource utilization rate, and optimizing the system operating efficiency. This paper proposes a GPU cache management strategy based on data locality features, combined with the warp scheduling strategy to improve GPU performance. Each load instruction can be divided into three categories. According to the locality characteristics of the load instruction, apply cache bypass to the streaming locality request to improve the cache utilization rate; at the same time, simultaneously make full use of the data locality between the warps through the inter-warp memory request coalescing; combined with the locality-based warp scheduling to further alleviate cache contention. By adopting different optimization strategies for different local characteristics, the GPU cache management strategy based on data locality types can effectively improve cache performance and alleviate pipeline congestion, thereby improving overall GPU performance. The main contributions of our work are the following:The rest of this paper is organized as follows: Cache management and replacement schemes designed for CPUs are often not suitable for GPUs ,13 becauMemory request coalescing technology can provide more efficient memory access ,25 and iA lot of research on warp scheduler has been proposed. Rogers et al.  proposedThe efficiency of the storage hierarchy is a key performance factor. Existing research uses multiple methods to manage GPU cache to solve the problem of the GPU memory subsystem and cache management efficiency. In addition to the above methods, there are warp throttling  and memoThe baseline GPU architecture is composed of multiple SMs. The parallelism of GPUs is achieved by multiple SMs. GPUs execute threads with the SIMT execution model. Every 32 threads are gathered into a warp. In general, each warp is a thread cluster composed of 32 threads and is the smallest scheduling unit of GPGPU. In the absence of branches, threads in a warp access different data and execute the same instructions. Performance can be significantly improved by making threads from the same warp execute the same code path and access neighboring addresses. The LDST unit is a functional unit responsible for load, store, and memory barrier instructions. It simultaneously processes 32 threads of warp as a functional unit.The GPU memory hierarchy comprises register memory, L1 cache, shared L2 cache, and off-chip dynamic random-access memory (DRAM). There is an L1 cache in each SM. The L1 cache is private and accessed by the warps within the core. In contrast, the L2 cache can be shared by all cores. Warp running on any SM can access the L2 cache. The core interacts through the interconnection network. When the core sends a memory request to L2 cache, the intermediate network sends it to different memory ports on the L2 cache according to the address of the memory request and then accesses different cache blocks’ process data access. A GPU application will initiate one or more kernel functions to the GPU, and each kernel function will allocate one or more thread blocks co-operative thread arrays (CTA) to each SM for execution.Global memory resides in the device memory and can be accessed through 32 B, 64 B, or 128 B memory transactions. The memory transactions can only read or write 32 B, 64 B, or 128 B device memory segments aligned to their size. Examples of aligned and unaligned memory transactions are shown in When a warp accesses global memory, it coalesces the memory request of threads in the warp into fewer memory transactions. The coalescing is based on the size of the word, and the allocation of memory addresses in memory. For instance, the threads in a single warp access a continuous 4-byte data, a 128-byte data request will be sent to DRAM instead of 32 4-byte requests, which reduces the number of transactions between SIMT core and DRAM, and reduces the workload of the interconnection network, memory partitions, and DRAM.Miss Information/Status Holding Registers (MSHR) store the address, size, type, and other information about requests. Once the memory controller returns the data needed by miss access, the information is used for re-execution. MSHR is also used to coalesce multiple requests for the same line to prevent the same request from being sent multiple times.In the current GPU architecture, the cache capacity of the L1 data cache is not high. Nevertheless, the SIMD architecture will generate a lot of memory requests in a short period of time. Required memory capacity will even be 1 to 2 orders of magnitude larger than the L1 data cache. This leads to severe cache contention. One of the reasons for cache contention is the failure to make effective use of data locality. If you cannot distinguish the reuse of the data, the data may be ejected from the cache before hit in the cache when the application reuse distance is long. Data that will not be reused may occupy the cache, causing cache pollution. The second reason is conflict in the cache. Thirty-two threads executing concurrently in a warp may access the same cache set. The third reason is that stall will be caused by the lack of other resources, such as MSHR resources.In order to reduce cache contention, we experimentally observed the impact of increasing the capacity and associativity of L1 data cache on performance. The experimental results are shown in The overall architecture diagram of Locality-Based Cache Management is shown in Classified by each global load instruction, there are three types of locality that can be exhibited by the data of load instruction: streaming locality, inter-warp locality, and intra-warp locality. The data may show both intra-warp locality and inter-warp locality at the same time.Our classification method is like . StreamiBefore applying specific strategies to different types of data locality, first, decide the load instructions’ locality types. The method to detect the locality type is based on the access count of the same warp and the access count of requests from other warps. The criteria of locality types are shown in Every load instruction in a GPU application will tend to have a similar cache access mode because threads of a single core execute the same code . TherefoThe table records the instruction ID as each global load instruction begins to execute. The instruction PC will be hashed to save the storage. When executing the instruction, the total access count of the data and the access count of the detected warp are recorded and updated synchronously. If the access request comes from the detected warp, the total access count and the access count of the same warp are both set as one. If the request comes from another warp, only the total access count is set as 1. When the same load instruction generates an address conflict or the total number of accesses exceeds the threshold, the current locality type is calculated, and the field of the invalid bit is set as 1. At that time, the locality-specific strategy can be adopted. Then release the entry and wait for the update. The locality type field consists of 2 bits, the first is set as 1 for the intra-warp locality, and the other is set as 1 for the inter-warp locality. If it is detected as streaming data, the locality type field is set as 00. In the L1 data cache, a field is needed to record the total access count to initialize the total access count of the information table entry when the detected warp accesses the data reaccessed by another warp. To save hardware overhead, we use the hash value of the PC as the instruction ID. Entries of the locality type detection formation table are shown in Inter-warp memory request coalescing has been considered effective in previous work . Our proThe baseline coalescing can only coalesce requests between threads of a single warp. To find more coalesce opportunities and improve data utilization, the inter-warp memory request coalesce is added, aiming to lookup requests issued by different warps and increase the coalesce window of the requests of threads in different warps.The inter-warp coalesce queue constitutes an inter-warp coalescing window, coalescing requests from different warps. Requests from the intra-warp coalesce queue are mapped to one of the multiple queues based on a subset of their addresses. When the inter-warp coalescing queue receives a request, its cache line will match the cache line already in the queue, coalescing requests from the same cache line.Several tags in the inter-warp coalesce queue are used to record the request information of a cache line. The warp ID, load instruction, and the mapping information in the cache line were saved in each tag. Once the queue received a request, a lookup is performed on the tag. Then the request will be inserted under the tag if matching. If there is no matching entry, a new entry is assigned when idle. In this work, only two tags per queue are reserved to reduce the number of tag lookups required and the times the memory request coalescer attempts to insert into the same queue, reducing overhead.The inter-warp coalesce queue takes temporal information as the priority and evicts the requests that enter the queue first to ensure that requests will not stay in the queue for too long and will not destroy the temporal locality of the warp.Many resources are occupied when fetching streaming data into the cache, such as cache lines and MSHR entries. However, streaming data will not be reused, so that the above resources will be wasted. Furthermore, some other data with a good locality in the application may be ejected from the cache. Cache bypass policy is a standard method to deal with such problems. Therefore, the method of processing the detected streaming data is the cache bypass policy. The locality-based bypassing directly provides data to the computing core from the L2 cache and its related interconnection network to improve resource utilization and reduce cache contention.When the cache bypass component receives the request and reads the locality type as streaming, it directly sends the request to the L2 cache. Otherwise, the request will be sent to the L1 cache.The GTO scheduling strategy is now a commonly used warp scheduling strategy. It prioritizes all warps on the GPU according to time. The oldest warp always has the highest priority. This method can effectively retain data locality . HoweverAs mentioned in When the warp scheduler needs to decide the warp to be issued, first check its locality type. If it is streaming data, it means it will be bypassed the cache. Then check if the cache is busy or idle. In order to quantify the performance of the cache, we propose a Cache Utilization Score (CUS) as a basis for judgment. As shown in Equation (1), the numerator of CUS is the product of the number of cache hits and the L2 cache latency and represents the speedup brought by cache hits. The denominator of CUS is the product of the number of cache misses and the pipeline stall and represents the degree of cache contention.To keep a certain balance between cache contention and full use of the locality, when the CUS is too high or too low, warp scheduling strategies need to be changed. When the CUS value is greater than the threshold H, we consider that the cache is idle and has good performance, and the cached warp can be scheduled faster to make full use of the cache. When the CUS value is less than the threshold L, we think that the cache is contention and busy; scheduling cached warp will increase the contention. Therefore, the bypassed warp should be scheduled first to ease the pressure of the cache. If the CUS value is between the threshold H and the threshold L, we believe that priority scheduling of cached warp or bypassed warp will not make a big difference. Therefore, the default GTO warp scheduling strategy is adopted.The warp scheduler first checks whether the load instruction executed by the warp is marked as streaming. Divide streaming warp into a group, and other warp into a group. Then compare CUS with both thresholds. Similarly, if the CUS value is less than the threshold H, the streaming warp group is set to a high priority group. If the CUS value is greater than the threshold L, the other warp groups are set as high priority groups. Use GTO scheduling strategies within the group. Otherwise, the GTO scheduling strategy is used within the scope of the two groups.We determined the values of threshold H and threshold L for better performance through experiments. In the experimental setup of this paper, we set the threshold H and threshold L to 1 and 0.2. Compared with the default GTO scheduling strategy, our LWS can make full use of local type information at runtime, alleviate cache contention, and make full use of the cache.We implemented our strategy in GPGPU-Sim version 3.2.3  and evalThe details of the benchmark we used are shown in By bypassing the streaming data, the cache bypass policy alleviates cache contention while protecting other cache lines, providing more opportunities for data with a better locality. Because streaming data is almost no longer reused or has a longer reuse distance, the cache cannot be used effectively. The cache bypass policy has the best results for applications with high cache sensitivity, such as GRA and PTF, which provide up to 38% performance improvement. Inter-warp memory request coalescer provides additional memory consolidation opportunities for data with inter-warp locality over baseline coalescer. As a result, performance has improved by an average of 9%. The LCM, which combines the cache bypass strategy and inter-warp memory access merger, has shown better performance than both separate policies.Baseline GPU uses the default GTO warp scheduling strategy. LWS considers streaming data that bypassed cache, which can further alleviate cache contention over the cache bypass strategy. LCM combined with LWS, has achieved a total performance improvement of 26%, and almost all applications can benefit from LWS. The experimental results prove that our strategy is effective and can bring significant performance improvements.From GPGPU has become the mainstream trend currently, and the memory system occupies a very important position in it. The optimization of the cache management strategy also plays an important role in the optimization of the overall performance. How to improve the cache utilization and improve the cache performance is the key to improving the GPU processing power, improving the resource utilization, and optimizing the system operating efficiency. In order to reduce cache contention, we proposed Locality-Based Cache Management and Locality-Based Warp Scheduling. LCM and LWS consider the use of different types of locality and further optimizes the warp scheduling strategy. Each load instruction classifies data locality by reading the access count in the cache, adding several inter-warp coalesce queues, and making full use of the inter-warp locality. The cache bypass policy makes data that are no longer reused bypass the cache to save cache resources. LWS selectively schedules warps accessing streaming data or other warp according to the cache and the busyness of the Internet. The experiment was implemented in an extended GPGPU-Sim simulator. Experimental results show that the LCM and LWS we proposed can effectively improve system performance, compared with the benchmark strategy, reduce the L1 cache miss rate, and obtain an average performance improvement of 26%.In the GPU, fast thread switching increases the difficulty of prefetching data. If the data cannot be prefetched, it is likely to hurt performance. The next step can be to use the most suitable prefetch strategy for different memory access characteristics, and improve the accuracy and coverage of prefetching.


This paper addresses the problem of unsupervised video summarization. Video summarization helps people browse large-scale videos easily with a summary from the selected frames of the video. In this paper, we propose an unsupervised video summarization method with piecewise linear interpolation (Interp-SUM). Our method aims to improve summarization performance and generate a natural sequence of keyframes with predicting importance scores of each frame utilizing the interpolation method. To train the video summarization network, we exploit a reinforcement learning-based framework with an explicit reward function. We employ the objective function of the exploring under-appreciated reward method for training efficiently. In addition, we present a modified reconstruction loss to promote the representativeness of the summary. We evaluate the proposed method on two datasets, SumMe and TVSum. The experimental result showed that Interp-SUM generates the most natural sequence of summary frames than any other the state-of-the-art methods. In addition, Interp-SUM still showed comparable performance with the state-of-art research on unsupervised video summarization methods, which is shown and analyzed in the experiments of this paper. With the exponential increase in online video, video summarization research has become attractive because it facilitates large-scale video browsing and efficient video analysis . Many reRecently, the reinforcement learning (RL) based unsupervised video summarization method outperformed in results with an explicit reward function to select keyframes . HoweverIn this paper, we propose an unsupervised video summarization method with piecewise linear interpolation (Interp-SUM). Interp-SUM is a method to predict importance scores by interpolating the output of a deep neural network. The piecewise linear interpolation method is utilized to mitigate the high variance problem and to generate a natural sequence of summary frames. We extract visual features from the frame images of the video using GoogleNet . GoogleNEspecially in the case of the high dimensional action space, the reward is changing while training because there are many cases where different actions are selected. Therefore, the variance of the gradient estimate is calculated with the log probability of the action, and the reward is increased. When the frames are selected, using the interpolated importance score, nearby frames with similar scores are selected together in high probability. In other words, the number of variants of the action is decreased, and it makes an effect of reducing the action space. A natural sequence of summary frames is generated by selecting adjacent frames with high importance scores together, as shown in experiments. The high variance problem is mitigated by the reduced action space.After interpolation, we convert the importance score to 0 or 1 frame-selection action using Bernoulli distribution. Using the action, we select keyframes as a summary, then evaluate how diverse and representative the summary is by using reward functions. Then, we compute the objective function proposed in exploring the under-appreciated reward (UREX) method with the reward and log probabilities of the action . UREX obWe propose an unsupervised video summarization method, with piecewise linear interpolation, to mitigate the high variance problem and to generate a natural sequence of summary frames. It reduces the size of the output layers of the network. It also makes the network learn faster because the network needs to predict only the short importance score in this method.We present Transformer network and CNN based video summarization network (TCVSN) to generate importance score well.We develop a novelty reward that measures the novelty of the selected keyframes.We develop the modified reconstruction loss with random masking to promote the representativeness of the summary.The main contributions of this paper are as follows:Video summarization methods are divided into the supervised learning-based method and the unsupervised learning-based method ,10,11,12In , they prHowever, the problem with the supervised learning-based method is that it is very difficult to create a large-scale video summarization dataset, including videos of various domains and situations, with human annotations . On the In , VAE andThe policy gradient method is a popular and powerful policy-based reinforcement learning method ,18,19,20We formulate video summarization as a frame selection problem with importance scores trained by the video summarization network. In particular, we develop a video summarization network, which is a parameterized policy to predict the importance score candidates to be interpolated to the importance scores. In other words, the importance score is frame-selection probability. The scores converted as frame-selection actions to select keyframes by using Bernoulli distribution, as shown in We first extract visual features oogleNet , which ioogleNet . To traiWe propose Transformer network and CNN based video summarization network (TCVSN) to increase video summarization performance as shown in Interpolation is a type of estimation method that guesses the new data points within the range of the known discrete data point. We first align the importance score candidate to fit the input size N of the frame sequence at equal intervals. We interpolate the candidate  method . If the To compute the objective function (5), we first calculate the log probability me video . Expectaposed in . At the We use the baseline, which is an important method for policy gradient, to reduce variance and to improve computational efficiency. The baseline is calculated as the moving average of rewards experienced so far and updated at the end of the episode. Our baseline Based on the policy gradient, we maximize We use the regularization term posed in  to contrWe introduce the modified reconstruction loss with random masking to promote the representativeness of the summary for training our video summarization network. Using the importance score S, we multiply the score After we compute all of the loss function, we finally calculate the loss Algorithm 1. Training Video Summarization Network.1: Input: Frame-level features of the video2: Output: TCVSN parameters  method. This variant uses the method proposed in .Interp-SUM w/o Recon. Loss which does not use the modified reconstruction loss that we presented.Interp-SUM w/o Interp. which does not use the piecewise linear interpolation method. Our network is trained to predict the importance score directly.We analyze the effects of different components of our method with the following variants:We visualize the qualitative results of different variants of our method with example images. We proposed an unsupervised video summarization method with a piecewise linear interpolation method (Interp-SUM). We present the Transformer network and CNN-based video summarization network (TCVSN). We use the policy gradient method with UREX objective function in reinforcement learning for training the network. We introduced the interpolation method to mitigate the high variance problem and to generate a natural sequence of summary frames. In the experimental results, the interpolation method helped to generate a more natural sequence of summary frames and improves performance by mitigating high variance problems. We showed the best performance on medium-length videos when we choose interpolation size to 35. Specifically, the F-measure on SumMe is 47.68, and the F-measure on TVSum is 59.14. This result indicates that our proposed method showed comparable performance on both datasets with the compared methods. In the future, we plan to make a network to find the best interpolation size automatically. To mitigate the high variance problem more, we will use new interpolation methods and employ the state-of-the-art techniques of reinforcement learning.