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Research article

Deconvolving breath alcohol concentration from biosensor measured transdermal alcohol level under uncertainty: a Bayesian approach


  • Received: 22 May 2021 Accepted: 03 August 2021 Published: 10 August 2021
  • The posterior distribution (PD) of random parameters in a distributed parameter-based population model for biosensor measured transdermal alcohol is estimated. The output of the model is transdermal alcohol concentration (TAC), which, via linear semigroup theory can be expressed as the convolution of blood or breath alcohol concentration (BAC or BrAC) with a filter that depends on the individual participant or subject, the biosensor hardware itself, and environmental conditions, all of which can be considered to be random under the presented framework. The distribution of the input to the model, the BAC or BrAC, is also sequentially estimated. A Bayesian approach is used to estimate the PD of the parameters conditioned on the population sample's measured BrAC and TAC. We then use the PD for the parameters together with a weak form of the forward random diffusion model to deconvolve an individual subject's BrAC conditioned on their measured TAC. Priors for the model are obtained from simultaneous temporal population observations of BrAC and TAC via deterministic or statistical methods. The requisite computations require finite dimensional approximation of the underlying state equation, which is achieved through standard finite element (i.e., Galerkin) techniques. The posteriors yield credible regions, which remove the need to calibrate the model to every individual, every sensor, and various environmental conditions. Consistency of the Bayesian estimators and convergence in distribution of the PDs computed based on the finite element model to those based on the underlying infinite dimensional model are established. Results of human subject data-based numerical studies demonstrating the efficacy of the approach are presented and discussed.

    Citation: Keenan Hawekotte, Susan E. Luczak, I. G. Rosen. Deconvolving breath alcohol concentration from biosensor measured transdermal alcohol level under uncertainty: a Bayesian approach[J]. Mathematical Biosciences and Engineering, 2021, 18(5): 6739-6770. doi: 10.3934/mbe.2021335

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  • The posterior distribution (PD) of random parameters in a distributed parameter-based population model for biosensor measured transdermal alcohol is estimated. The output of the model is transdermal alcohol concentration (TAC), which, via linear semigroup theory can be expressed as the convolution of blood or breath alcohol concentration (BAC or BrAC) with a filter that depends on the individual participant or subject, the biosensor hardware itself, and environmental conditions, all of which can be considered to be random under the presented framework. The distribution of the input to the model, the BAC or BrAC, is also sequentially estimated. A Bayesian approach is used to estimate the PD of the parameters conditioned on the population sample's measured BrAC and TAC. We then use the PD for the parameters together with a weak form of the forward random diffusion model to deconvolve an individual subject's BrAC conditioned on their measured TAC. Priors for the model are obtained from simultaneous temporal population observations of BrAC and TAC via deterministic or statistical methods. The requisite computations require finite dimensional approximation of the underlying state equation, which is achieved through standard finite element (i.e., Galerkin) techniques. The posteriors yield credible regions, which remove the need to calibrate the model to every individual, every sensor, and various environmental conditions. Consistency of the Bayesian estimators and convergence in distribution of the PDs computed based on the finite element model to those based on the underlying infinite dimensional model are established. Results of human subject data-based numerical studies demonstrating the efficacy of the approach are presented and discussed.



    In information sciences, in order to extract useful information hidden in voluminous data, many methods were proposed, including classical logics and classical mathematics. Rough set theory, introduced by Z. Pawlak in [1], plays an important role in applications of these methods (see [2,3,4,5,6,7,8,9], for example). In the classical rough set theory, Pawlak approximation spaces are based on partitions of the universe of discourse, but this requirement is not satisfied in some situations [10]. In the past years, Pawlak approximation spaces were extended to covering approximation spaces, and used in information sciences (see [10,11,12,13,14,15,16,17,18,19,20,21,22], for example).

    It is easy to see that topological spaces on finite universes are special covering approximation spaces. Thus, some topological properties are considered in covering approximation spaces. In addition, Separations play important roles in topological spaces. Recently, Si-separations (i=0,1,2,3,d,r) in covering approximation spaces draw our attention. And some interesting results were obtained ([12,13]).

    However, just as separations in topological spaces [23,24,25], the theoretical research framework about separations in covering approximation spaces can be constructed by relations, characterizations and invariance. More precisely, this framework can be described as follows.

    Framework 1. The theoretical research framework about separations in covering approximation spaces consists of the following three parts.

    Part 1: Establish some relations among separations in covering approximation spaces. These relations mainly include implications, not implications, and equivalences.

    Part 2: Give some equivalent characterizations of separations in covering approximation spaces. These equivalent characterizations are mainly shown by elements of covering, the covering upper approximation of subsets and the covering lower approximation of subsets, respectively.

    Part 3: Study invariance of separation in covering approximation spaces. The invariance of separation mainly includes invariance of separation about reducts of covering, subspaces and transformations.

    Part 1 and Part 2 of Framework 1 were researched in [12,13]. However, there are not results on Part 3 of Framework 1. Obviously, this research can be performed by means of investigations of the following questions.

    Question 1. Let i=0,1,2,3,d,r.

    (1) Is Si-separation in covering approximation spaces invariant to reducts of coverings?

    (2) Is Si-separation in covering approximation spaces invariant to covering approximation subspaces?

    (3) Is Si-separation in covering approximation spaces invariant under some transformations of covering approximation spaces?

    This paper investigates covering upper approximations and covering lower approximations of subsets in covering approximation spaces and their reduction spaces (resp. their subspaces, their transformations), and give some relations among covering approximations of these subsets. Based on these results, we answer Question 1 positively.

    The remainder of this paper is organized as follows. Section 2 reviews some definitions and remarks. The invariance of separations in covering approximation spaces are considered in Sections 3–5. In section 6, some concluding remarks are provided.

    To begin with, the definition of covering approximation space is reviewed.

    Definition 1. (cf. [1,22]) Let U, the universe of discourse, be a set and C be a family of subsets of U.

    (1) C is called a covering of U if {K:KC}=U. Furthermore, C is called a partition on U if also KK= for all K,KC, where KK.

    (2) The pair (U;C) is called a covering approximation space (resp. a Pawlak approximation space) if C is a covering (resp. a partition) of U.

    All results this paper proposed are based on the assumption that the universe U is a finite set. Next we review the following upper approximation operator ¯C and lower approximation operator C_ on (U;C) [17].

    Definition 2. (cf. [17]) Let (U;C) be a covering approximation space. For each XU, put

    C_(X)={K:KC and KX},

    ¯C(X)=UC_(UX).

    (1) ¯C:2U2U is called a covering upper approximation operator, simply denoted by X.

    (2) C_:2U2U is called a covering lower approximation operator, simply denoted by X.

    Before introducing concepts of separations, we give some notations as follows.

    Notation 1. Let (U;C) be a covering approximation space. Throughout this paper, we use the following notations, where F is a family of subsets of U and xU.

    (1) F=FFF.

    (2) F=FFF.

    (3) Cx={K:xKC}.

    (4) N(x)=Cx.

    (5) ¯N(x)={K:KCx}.

    (6) D(x)=U(CCx).

    Now we define separations in covering approximation spaces.

    Definition 3. (cf. [12]) Let (U;C) be a covering approximation space. Some separations are defined as follows.

    (1) S0-separation: if x,yU and xy, then there exists KC such that K{x,y}={x} or K{x,y}={y}.

    (2) S1-separation: if x,yU and xy, then there exist Kx,KyC such that Kx{x,y}={x} and Ky{x,y}={y}.

    (3) S2-separation: if x,yU and xy, then there exist Kx,KyC such that xKx, yKy and KxKy=.

    (4) S3-separation: if xU and xXU, then there exists KC such that xK and KX=.

    (5) Sd-separation: if xU, then there exists KC such that {x}=KD(x).

    (6) Sr-separation: if xKC, then D(x)K.

    Some explanations of separations are provided in the following.

    Remark 1. Let (U;C) be a covering approximation space.

    (1) If (U;C) satisfies S0-separation, then for every pair of distinct x,yU there is KC containing exactly one of x and y.

    (2) If (U;C) satisfies S1-separation, then for every pair of distinct x,yU there is KC containing x and KC containing y such that K does not contain y and K does not contain x.

    (3) If (U;C) satisfies S2-separation, then every pair of distinct x,yU can be separated from elements of C.

    (4) If (U;C) satisfies S3-separation, then for each xU and each subset X of U not containing x there is KC containing x such that K and X are separated.

    (5) If (U;C) satisfies Sd-separation, then for each xU there is KC containing x such that whenever yU and yx, either K does not contain y or K contains y for KC satisfying xK.

    (6) (U;C) satisfies Sr-separation, i.e., for each xU and each KC containing x, if yU such that KC does not contain x implying K does not contain y, then K contains y.

    Let i=0,1,2,3,d,r. Throughout this paper, we call a covering approximation space (U;C) with Si-separation an Si-covering approximation space. For short, Si-covering approximation spaces are called Si-spaces (Note: Si-separations and Si-spaces in [13] are called Gi-separations and Gi-spaces, respectively).

    The following example illustrates the concepts of separation.

    Example 1. (1) Let U={a,b,c} and C={{a,b},{c}}. Then (U,C) is a Pawlak approximation space. Each Pawlak approximation space is an Sr-space ([12,Remark 3.4]). Hence, (U,C) is an Sr-space. On the other hand, N(a)={a,b}{a}, so (U,C) is not an S1-space ([12,Theorem 3.6]).

    (2) Let U={a,b,c} and C={{a,c},{b,c}}. It is easy to check that (U,C) is an S0-space. Since D(c)=U and KD(x)=K{c} for each KC, (U;C) is not an Sd-space.

    (3) Let U={a,b} and C={{a,b},{a}}. Since D(a)={a,b} and D(b)={b}, we have {a}={a}D(a) and {b}={a,b}D(b), Note that {a}, {a,b}C. So (U,C) is an Sd-space. On the other hand, N(b)={a,b}{b}, so (U,C) is not an S1-space ([12,Theorem 3.6]).

    (4) Let U={a,b,c} and C={{a,b},{a,c},{b,c}}. Since N(a)={a}, N(b)={b} and N(c)={c}, (U,C) is an S1-space. Whenever K,KC, KK, So (U,C) is not an S2-space.

    (5) Let U={a,b,c,d} and C={{a,b},{a,c},{c,d},{b,d}}. It is not difficult to check that (U,C) is an S2-space. Since {a}C, (U,C) is not an S3-space ([12,Theorem 3.13]).

    The following lemma shows the relation among all separations in covering approximation spaces.

    Lemma 1. [13] The following implications are known and none of these implications can be reversed by Example 1.

    (1) S3-space S2-space S1-space Sd-space S0-space.

    (2) S1-space Sr-space.

    However, we have the following relations among separations in Pawlak approximation spaces, which further illustrates the concepts of separation.

    Corollary 1. Let (U;C) be a Pawlak approximation space. Then the following are equivalent:

    (1) (U;C) is an S3-space,

    (2) (U;C) is an S2-space,

    (3) (U;C) is an S1-space,

    (4) (U;C) is an Sd-space,

    (5) (U;C) is an S0-space.

    Proof. By Lemma 1, we have (1) (2) (3) (4) (5). So we only need to prove that (5) (1).

    Suppose (U;C) is an S0-space. Since (U;C) is a Pawlak approximation space, C is a partition of U. Let xU and xXU. Take KC satisfying xK. It suffices to prove that KX=. In fact, if KX, then there is yKX. So we obtain yx. Since (U;C) is an S0-space, there is KC such that K{x,y}={x} or K{x,y}={y}. Without loss of generality, we assume K{x,y}={x}. Then yK. It is not hard to see that KK and xKK. This contradicts that C is a partition of U. So KX=.

    Remark 2. Separations in covering approximation spaces play an important role in not only applications but also theoretical research of rough set theory. For example, in the fields of rough set data analysis, it is an interesting question how to characterize the conditions under which {N(x):xU} forms a partition of U for a covering approximation space (U;C) ([17,26]). Reference [12] proves that a covering approximation space (U;C) is an Sr-space if and only if {N(x):xU} forms a partition of U. Thus, we can investigate the above question by discussing Sr-spaces. Furthermore, one concludes that {N(x):xU} forms a partition of U if C is a partition of U.

    The following results are known.

    Lemma 2. (cf. [13,17,22]) Let (U;C) be a covering approximation space. Then the following results hold:

    (1) U=U=U and ==,

    (2) if XU, then XXX,

    (3) if XYU, then XY and XY,

    (4) if XU, then (X)=X and (X)=X,

    (5) if KC, then K=K,

    (6) if XU, then (UX)=UX and (UX)=UX,

    (7) if xU, then D(x)={x}.

    The characteristics about all separations in covering approximation spaces are provided.

    Theorem 1. (cf. [13]) Let (U;C) be a covering approximation space. Then the following results hold:

    (1) (U;C) is an S0-space if and only if {x}{y} for each pair x,yU with xy,

    (2) (U;C) is an S1-space if and only if {x}=N(x) for each xU,

    (3) (U;C) is an S2-space if and only if {x}=¯N(x) for each xU,

    (4) (U;C) is an S3-space if and only if {x}C for each xU,

    (5) (U;C) is an Sd-space if and only if whenever xU, {x}=XY for some X,YU,

    (6) (U;C) is an Sr-space if and only if x{y}y{x} for each pair x,yU.

    To consider the invariance of separation in covering approximation spaces, the reduct of covering is introduced firstly.

    Definition 4. (cf. [20,21]) Let U be the universe of discourse and C be a covering of U.

    (1) KC is called reducible in C if K=F for FC{K}. Otherwise, K is called irreducible in C.

    (2) C is called irreducible if K is irreducible in C for each KC; otherwise C is called reducible.

    (3) C is called the reduct of C if C is obtained by deleting all reducible elements in C. It is clear that C is an irreducible subcovering of C.

    Proposition 1. Let C be a covering of the set U. Each set in C is a union of some irreducible elements of C.

    However, Proposition 1 does not hold for infinite set U. A counter example is provided to show it.

    Example 2. Let U be the set of all positive integers. Assume C={{1,2},{3},{4},{5},{6},}{{2}{3,4,5,6,},{2}{4,5,6,},{2}{5,6,},} is a covering of U. It is easy to see that {1,2},{3},{4},{5},{6}, are irreducible elements of C. But {2}{n,n+1,n+2,} is a reducible element of C because {2}{n,n+1,n+2,}={2}{n+1,n+2,}{n} holds, where nU and n>2. Thus, {2}{n,n+1,n+2,} is not a union of some irreducible elements of C.

    Let (U;C) be a covering approximation space, C be the reduct of C and XU. According to Definition 4, C is the unique reduct of C. In this section, the covering lower approximation of X and the covering upper approximation of X in covering approximation space (U;C) are denoted by X# and X#, respectively. For each xU, D(x)=U(CCx), N(x)={K:KCx}=Cx and ¯N(x)={K#:KCx}. The following propositions can be provided.

    Proposition 2. Let (U;C) be a covering approximation space and C be the reduct of C. For each xU, if KCx, then KK for some KCx.

    Proof. (1) Let xU and KCx. If KCx, then KK for K=KCx. If KCx, then K is a reducible elements in C. It is not difficult to see that there exists FC such that K=F. Since xK, we choose KF satisfying xK. Thus, one derives KCx and KK.

    Proposition 3. [21] Let (U;C) be a covering approximation space and C be the reduct of C. For XU, X=X# and X=X#.

    Proposition 4. Let (U;C) be a covering approximation space, C be the reduct of C and xU. Then the following conclusions hold:

    (1) D(x)=D(x),

    (2) N(x)=N(x),

    (3) ¯N(x)=¯N(x).

    Proof. (1) It suffices to prove that (CCx)=(CCx). Assume y(CCx). Then yK for some KCCx. By Proposition 2 (1), there is KC such that yKC and KK. KCCx because xK and xK, i.e., KCx. It follows that y(CCx). Conversely, we assume y(CCx). Then yK for some KCCx. So KCC and xK. Thus KCCx. It follows that y(CCx). It proves that (CCx)=(CCx).

    (2) Since CC, CxCx, one sees N(x)=CxCx=N(x). On the other hand, we assume yN(x). Whenever KCx, by Proposition 2 (1), there is KCx such that KK, hence yN(x)=CxKK. It proves that yN(x). So N(x)N(x). Consequently, we have N(x)=N(x).

    (3) Since CC, CxCx, we have ¯N(x)={K:KCx}{K#:KCx}=¯N(x). On the other hand, if y¯N(x), then xK# for each KCx. Whenever KCx, by Proposition 2 (1), there is KCx such that KK. Based on Lemma 2 (3) and Proposition 2 (2), it is not hard to see that y¯N(x)K#=KK. This proves that y¯N(x). So ¯N(x)¯N(x). Consequently, one gets ¯N(x)=¯N(x).

    Now we give the main theorem of this section.

    Theorem 2. Let (U;C) be a covering approximation space and C be the reduct of C. Then the following conclusions hold:

    (1) (U;C) is an S0-space if and only if (U;C) is an S0-space,

    (2) (U;C) is an S1-space if and only if (U;C) is an S1-space,

    (3) (U;C) is an S2-space if and only if (U;C) is an S2-space,

    (4) (U;C) is an S3-space if and only if (U;C) is an S3-space,

    (5) (U;C) is an Sd-space if and only if (U;C) is an Sd-space,

    (6) (U;C) is an Sr-space if and only if (U;C) is an Sr-space.

    Proof. (1) By Proposition 2 (2), {x}{y} if and only if {x}#{y}# for each pair x,yU with xy. By Theorem 1 (1), (U;C) is an S0-space if and only if (U;C) is an S0-space.

    (2) By Proposition 4 (2), N(x)=N(x) for each xU. It follows that {x}=N(x) if and only if {x}=N(x). By Theorem 1 (2), (U;C) is an S1-space if and only if (U;C) is an S1-space.

    (3) By Proposition 4 (3), ¯N(x)=¯N(x) for each xU. It follows that {x}=¯N(x) if and only if {x}=¯N(x). By Theorem 1 (3), (U;C) is an S2-space if and only if (U;C) is an S2-space.

    (4) By Definition 4, {x}C if and only if {x}C for each xU. By Theorem 1 (4), (U;C) is an S3-space if and only if (U;C) is an S3-space.

    (5) By Proposition 2 (2), whenever xU, {x}=XY if and only if {x}=X#Y# for some X,YU. By Theorem 1 (5), (U;C) is an Sd-space if and only if (U;C) is an Sd-space.

    (6) By Proposition 2 (2), for each pair x,yU, x{y}y{x} if and only if x{y}#y{x}#. By Theorem 1 (6), (U;C) is an Sr-space if and only if (U;C) is an Sr-space.

    Remark 3. For each i=0,1,2,3,d,r, Theorem 2 shows that Si-separation in covering approximation spaces is invariant with respect to the reduct of coverings. It indicates that we can reduce some elements of C without influencing separations. In network applications, some existing approaches deal with the covering directly. In contrast, we deal with C', which is the reduct of C. Therefore, both time and space requirements are reduced, and network securities are preserved.

    The following definition presents covering approximation subspaces.

    Definition 5. (cf. [11]) Let (U;C) be a covering approximation space and UU. Take C={KU:KC}, then (U;C) is a covering approximation space. (U;C) is called a subspace of (U;C).

    Let (U;C) be a covering approximation space, (U;C) be a subspace of (U;C) and XU. In this section, the covering lower approximation of X and the covering upper approximation of X in covering approximation space (U;C) are denoted by X# and X#, respectively. For each xU, D(x)=U(CCx), N(x)={K:KCx}=Cx and ¯N(x)={K#:KCx}. The following two results are proved.

    Proposition 5. Let (U;C) be a covering approximation space and (U;C) be a subspace of (U;C). Then the following conclusions hold:

    (1) If XU, then (XU)#XU.

    (2) If XU, then XU(XU)#.

    Proof. (1) Because (XU)#=U(UXU)#=U(UX)# and XU=(U(UX))U=UU(UX), it suffices to prove that U(UX)(UX)#. Assume xU(UX). Then xU and there is LC such that xLUX. It follows that xLUUX. Note that LUC. So x(UX)#. It proves that U(UX)(UX)#.

    (2) Assume xXU. Then xU and there is KC such that xKX. Thus xKUXU. Note that KUC. So x(XU)#. It proves that XU(XU)#.

    Remark 4. In Proposition 5 (1) and (2), "" can not be replaced by"=" (see Examples 2 and 4). However, we have the following result.

    Proposition 6. Let (U;C) be a covering approximation space and (U;C) be a subspace of (U;C). If XU, then X#=XU. Specially, {x}#={x}U if xU.

    Proof. Assume XU. By Proposition 5 (1), X#XU, we only need to prove XUX#. Note that XU=(U(UX))U=U(UX)U and X#=U(UX)#. It suffices to prove that (UX)#(UX)U. Assume y(UX)#. Then there is KC such that yKUX, i.e., there is KC such that yKUUX. Thus (KU)X=, i.e., KX=. Hence, we derive yKUX. It follows that y(UX)U. It proves that (UX)#(UX)U.

    The following examples show that "" is not replaced by "=" in Proposition 5 (1) and (2).

    Example 3. Let U={a,b,c}, C={{a,b},{c}}, U={b,c}, C={{b},{c}} and X={a,c}. Then XU(XU)#. In fact, (XU)#={c}#=U(U{c})#=U{b}#=U{b}={c}. On the other hand, X=U(UX)=U{b}=U=U. It follows that XU=UU=U={b,c}. So XU(XU)#.

    Example 4. Let U={a,b}, C={{a,b}}, U={a}, C={{a}} and X={a}. Then (XU)#XU. In fact, XU= and (XU)#={a}.

    To show the invariance of separation, the following results are proved.

    Proposition 7. Let (U;C) be a covering approximation space, (U;C) be a subspace of (U;C) and xU. Then the following conclusions hold:

    (1) D(x)U=D(x),

    (2) N(x)U=N(x),

    (3) ¯N(x)U¯N(x).

    Proof. By computing directly, we have the following results.

    (1) D(x)U=(U(CCx))U=UU((CCx))=UU({K:xKC})=U{KU:xKC}=U{K:xKUC}=U(CCx)=D(x).

    (2) N(x)U=({K:xKC})U={KU:xKC}={K:xKC}=N(x).

    (3) ¯N(x)U=({K:xKC})U={KU:xKC}{(KU)#:xKC}={K#:xKC}=¯N(x).

    Now we give the main theorem of this section.

    Theorem 3. Let (U;C) be a covering approximation space and (U;C) be a subspace of (U;C). Then the following conclusions hold:

    (1) if (U;C) is an S0-space, then (U;C) is an S0-space,

    (2) if (U;C) is an S1-space, then (U;C) is an S1-space,

    (3) if (U;C) is an S2-space, then (U;C) is an S2-space,

    (4) if (U;C) is an S3-space, then (U;C) is an S3-space,

    (5) if (U;C) is an Sd-space, then (U;C) is an Sd-space,

    (6) if (U;C) is an Sr-space, then (U;C) is an Sr-space.

    Proof. (1) Assume (U;C) is an S0-space. By Theorem 1 (1), {x}{y} for any x,yU satisfying xy. We claim that {x}#{y}#. In fact, if {x}#={y}#, then {x}U={y}U from Proposition 6. Thus x{x}U={y}U{y}. That is {x}{y}. Similarly, we obtain {y}{x}. It follows that {x}={y}. This is a contradiction.

    (2) Assume (U;C) is an S1-space. By Theorem 1 (2), N(x)={x} for each xU. Thus, we have N(x)U={x}. From Proposition 7 (2), one sees N(x)=N(x)U={x}. According to Theorem 1 (2), (U;C) is an S1-space.

    (3) Assume (U;C) is an S2-space. By Theorem 1 (3), ¯N(x)={x} for each xU. Thus, we have ¯N(x)U={x}. By Proposition 7 (3), {x}¯N(x)¯N(x)U={x}, i.e., ¯N(x)={x}. By Theorem 1 (3), (U;C) is an S2-space.

    (4) Assume (U;C) is an S3-space. By Theorem 1 (4), {x}C for each xU. Thus, we have {x}={x}UC. By Theorem 1 (4), (U;C) is an S3-space.

    (5) Assume (U;C) is an Sd-space. Take xU. Then there is KC such that {x}=KD(x). Thus, we obtain {x}=(KU)(D(x)U). By Proposition 7 (1), D(x)U=D(x). It follows that {x}=(KU)D(x). Note that KUC. So (U;C) is an Sd-space.

    (6) Assume (U;C) is an Sr-space. Let x,yU and x{y}#. By Theorem 1 (6), we only need to prove that y{x}#. Since {y}#={y}U from Proposition 6, x{y}. By Theorem 1 (6), y{x} because (U;C) is an Sr-space. It follows that y{x}U={x}#.

    Remark 5. For each i=0,1,2,3,d,r, Theorem 3 shows that Si-separation in covering approximation spaces is invariant to covering approximation subspaces. It indicates that some dynamic methods [27] are applicable to covering based rough sets theory. We can employ different subsets of U to produce different coverings, and obtain more stable rules.

    In this section, we use the following notations for covering approximation space (U;C). For XU, the covering lower approximation of X and the covering upper approximation of X in covering approximation space (U;C) are denoted by X# and X#, respectively. For xU, D(x)=U(CCx), N(x)={K:KCx}=Cx and ¯N(x)={K#:KCx}. The definition of transformation is proposed.

    Definition 6. Let (U;C) and (U;C) be two covering approximation spaces. If f:UU is a bijective mapping, then f is called a transformation from (U;C) to (U;C).

    Remark 6. It is clear that if f is a transformation from (U;C) to (U;C), then f1 is a transformation from (U;C) to (U;C).

    Remark 7. Let (U;C) and (U;C) be two covering approximation spaces, f be a transformation from (U;C) to (U;C). Whenever A,BU and X,YU. Then the following conclusions hold:

    (1) f(f1(X))=X,

    (2) f1(f(A))=A,

    (3) f(AB)=f(A)f(B),

    (4) f1(XY)=f1(X)f1(Y),

    (5) f(AB)=f(A)f(B),

    (6) f1(XY)=f1(X)f1(Y),

    (7) f(AB)=f(A)f(B),

    (8) f1(XY)=f1(X)f1(Y),

    (9) ABf(A)f(B),

    (10) XYf1(X)f1(Y).

    In general, Si-separation in covering approximation spaces is not invariant under transformations of covering approximation spaces for i=0,1,2,3,d,r.

    Example 5. Let (U;C) and (U;C) be two covering approximation spaces, where U={a,b,c,d}, U={x,y,z,w}, C={{a},{b},{c},{d},{a,b},{b,c},{c,d}} and C={{x,y},{x,y,z},{z,w}}. Take f:UU, where f(a)=x, f(b)=y, f(c)=z and f(d)=w. Then f is a transformation from (U;C) to (U;C).

    (1) Since {u}C for each uU, (U;C) is an S3-space from Theorem 1 (4). By Remark 1, (U;C) is an Si-space for each i=0,1,2,3,d,r.

    (2) Note that {x}#=U(U{x})#=U{y,z,w}#=U{z,w}={x,y} and {y}#=U(U{y})#=U{x,z,w}#=U{z,w}={x,y}. So we derive {x}#={y}#. Hence, (U;C) is not an S0-space from Theorem 1 (1). By Remark 1 (1), (U;C) is not an Si-space for each i=0,1,2,3,d.

    (3) It is clear that N(z)={z} and N(w)={z,w}. So zN(w) and wN(z). By Theorem 1 (6), (U;C) is not an Sr-space.

    However, which classes of transformations preserve separations in covering approximation spaces? It is one of important topics in research framework about separations in covering approximation spaces. In order to answer this question, we introduce the following definitions.

    Definition 7. Let (U;C) and (U;C) be two covering approximation spaces, and f be a transformation from (U;C) to (U;C).

    (1) f is called a covering lower approximation-preserving transformation (abbr. CLAP-transformation) if f(A)=(f(A))# for all AU.

    (2) f is called a covering upper approximation-preserving transformation (abbr. CUAP-transformation) if f(A)=(f(A))# for all AU.

    (3) f is called a covering approximation-preserving transformation (abbr. CAP-transformation) if f is a covering both lower and upper approximation-preserving transformation.

    The following proposition is obvious.

    Proposition 8. Let (U;C) and (U;C) be two covering approximation spaces, and f be a transformation from (U;C) to (U;C). Then the following conditions are equivalent:

    (1) f is a CLAP-transformation,

    (2) f is a CUAP-transformation,

    (3) f is a CAP-transformation.

    According to Proposition 8, one obtains Lemma 3.

    Lemma 3. Let (U;C) and (U;C) be two covering approximation spaces. Then the following conditions are equivalent:

    (1) f is a CAP-transformation from (U;C) to (U;C),

    (2) f1 is a CAP-transformation from (U;C) to (U;C).

    Proof. We only need to prove (1) (2) because f=(f1)1.

    Assume f be a CAP-transformation from (U;C) to (U;C). Then f1 is a transformation from (U;C) to (U;C) by Remark 6. Whenever XU, we get (f(f1(X)))#=f((f1(X))) because f is a CLAP-transformation. Combined with Lemma 7 (1) and (2), f1(X#)=f1((f(f1(X)))#)=f1(f((f1(X))))=(f1(X)). It shows that f1 is a CLAP-transformation. By Proposition 8, f1 is a CAP-transformation.

    Lemma 4. Let (U;C) and (U;C) be two covering approximation spaces, and f be a CAP-transformation from (U;C) to (U;C). If aU and x=f(a)U, then the following conclusions hold:

    (1) f(N(a))=N(x),

    (2) f(¯N(a))=¯N(x).

    Proof. For each KCa, K=K from Lemma 2 (5). Then one sees f is a CAP-transformation. So we get x=f(a)f(K)=f(K)=(f(K))#. Hence, there is HKCx such that HKf(K).

    (1) f(N(a))=f({K:KCa})={f(K):KCa}{HK:KCa}N(x). Similarly, f1(N(x))N(a) because f1 is a CAP-transformation from Proposition 3. By Lemma 7 (1), N(x)=ff1(N(x))f(N(a)). Consequently, we obtain f(N(a))=N(x).

    (2) f(¯N(a))=f({K:KCa})={f(K):KCa}{H#K:KCa}¯N(x). By the same method, f1(¯N(x))¯N(a) because f1 is a CAP-transformation from Proposition 4.7. By Lemma 4.2(1), ¯N(x)=ff1(¯N(x))f(¯N(a)). Consequently, f(¯N(a))=¯N(x).

    Based on the analysis above, the following results are proved.

    Theorem 4. Let (U;C) and (U;C) be two covering approximation spaces, and f be a CAP-transformation from (U;C) to (U;C). Then the following conclusions hold:

    (1) (U;C) is an S0-space if and only if (U;C) is an S0-space,

    (2) (U;C) is an S1-space if and only if (U;C) is an S1-space,

    (3) (U;C) is an S2-space if and only if (U;C) is an S2-space,

    (4) (U;C) is an S3-space if and only if (U;C) is an S3-space,

    (5) (U;C) is an Sd-space if and only if (U;C) is an Sd-space,

    (6) (U;C) is an Sr-space if and only if (U;C) is an Sr-space.

    Proof. By Proposition 3, we only need to prove "only if" parts.

    (1) Assume (U;C) is an S0-space. For any x,yU satisfying xy, there are a,bU with ab such that x=f(a) and y=f(b). Because (U;C) is an S0-space, {a}{b} from Theorem 1 (1). It follows that f({a})f({b}) from Lemma 3 (9). Since f is a CAP-transformation, one sees {f(a)}#=f({a}) and {f(b)}#=f({b}). Consequently, {x}#={f(a)}#=f({a})f({b})={f(b)}#={y}#. By Theorem 1 (1), (U;C) is an S0-space.

    (2) Assume (U;C) is an S1-space. For each xU, there is aU such that x=f(a). Because (U;C) is an S1-space, we have {a}=N(a) from Theorem 1 (2). By Lemma 4 (1), one gets {x}={f(a)}=f({a})=f(N(a))=N(x). It follows that (U;C) is an S1-space from Theorem 1 (2).

    (3) Assume (U;C) is an S2-space. For each xU, there is aU such that x=f(a). Because (U;C) is an S2-space, {a}=¯N(a) from Theorem 1 (3). By Lemma 4 (2), we have {x}={f(a)}=f({a})=f(¯N(a))=¯N(x). It follows that (U;C) is an S2-space from Theorem 1 (3).

    (4) Assume (U;C) is an S3-space. For each xU, there is aU such that x=f(a). Because (U;C) is an S3-space, we obtain {a}C from Theorem 1 (4). Hence, one gets {a}={a}. Since f is a CAP-transformation, it is easy to see that f({a})=(f({a}))#. Consequently, {x}={f(a)}=f({a})=f({a})=(f({a}))#={f(a)}#={x}#. It follows that {x}C. By Theorem 1 (4), (U;C) is an S3-space.

    (5) Assume (U;C) is an Sd-space. For each xU, there is aU such that x=f(a). Because (U;C) is an Sd-space, there are A,BU such that {a}=AB from Theorem 1 (5). Since f is a CAP-transformation, one sees f(A)=(f(A))# and f(B)=(f(B))#. Combined with Lemma 7 (5), we have {x}={f(a)}=f(AB)=f(A)f(B)=(f(A))#(f(B))#. Note that f(A),f(B)U. So (U;C) is an Sd-space from Theorem 1 (5).

    (6) Assume (U;C) is an Sr-space. For any x,yU satisfying x{y}#, there are a,bU such that x=f(a) and y=f(b). Thus, we find a=f1(x) and b=f1(y). Since f is a CAP-transformation from (U;C) to (U;C), f1 is a CAP-transformation from (U;C) to (U;C) by Proposition 3. So f1({y}#)=(f1({y})). Consequently, a=f1(x)f1({y}#)=(f1({y}))={b}. Because (U;C) is an Sr-space, we obtain b{a} from Theorem 1 (6). It follows that y=f(b)f({a})=(f({a}))#={x}# because f is a CAP-transformation. Hence, (U;C) is an Sr-space from Theorem 1 (6).

    Remark 8. In Theorem 4, "CAP" can be replaced by "CLAP" or "CUAP" from Proposition 8 and "f be a CAP-transformation from (U;C) to (U;C)" can be replaced by "f1 be a CAP-transformation from (U;C) to (U;C)" from Proposition 3.

    Remark 9. For each i=0,1,2,3,d,r, Theorem 4 shows that Si-separation in covering approximation spaces is invariant under CAP-transformations of covering approximation spaces. It indicates that some transformations methods are applicable to covering-based rough sets theory. We can employ different the universe of discourse with different coverings to produce the same separations.

    This paper studies some invariance of separations in covering approximation spaces. Three main theorems, Theorems 2–4, are obtained. These are important results in the theoretical research framework about separations in covering approximation spaces. These results give answers to questions posed in the background section.

    It is a strong assumption that the universe is finite, because usually coverings are defined as a collection of nonempty subsets of an arbitrary universe. In this work we have restricted to finite universes, but we will prove whether the results this paper presented hold for covering approximation spaces with an infinite universe in the future. In addition, since there are more than 20 covering covering approximation operators, the invariance of separation can also be considered in other types of covering approximation spaces.

    The authors thank the anonymous reviewers'constructive suggestions. This work was supported by the National Natural Science Foundation of China (No. 11871259, No. 61379021), and the Educational Research Project for Young and Middle-aged teachers of Fujian Province (JAT190371).

    The authors declare that they have no conflict of interest.



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