The operation space of the vertical lift shaft is small, the components are complex, the occluding and different behavior space characteristics are similar, and the unsafe behavior is not easy to detect, which makes the operation safety of maintenance personnel in the elevator greatly threatened. This paper proposes an elevator maintenance personnel behavior detection algorithm based on the first-order deep network architecture (FOA-BDNet). First, a lightweight backbone feature extraction network is designed to meet the online real-time requirements of elevator maintenance environment monitoring video stream detection. Then, the feature fusion network structure of "far intersection and close connection" is proposed to fuse the fine-grained information with the coarse-grained information and to enhance the expression ability of deep semantic features. Finally, a first-order deep target detection algorithm adapted to the elevator scene is designed to identify and locate the behavior of maintenance personnel and to correctly detect unsafe behaviors. Experiments show that the detection accuracy rate on the self-built data set in this paper is 98.68%, which is 4.41% higher than that of the latest target detection model YOLOv8-s, and the reasoning speed reaches 69.51fps/s, which can be easily deployed in common edge devices and meet the real-time detection requirements for the unsafe behaviors of elevator scene maintenance personnel.
Citation: Zengming Feng, Tingwen Cao. FOA-BDNet: A behavior detection algorithm for elevator maintenance personnel based on first-order deep network architecture[J]. AIMS Mathematics, 2024, 9(11): 31295-31316. doi: 10.3934/math.20241509
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The operation space of the vertical lift shaft is small, the components are complex, the occluding and different behavior space characteristics are similar, and the unsafe behavior is not easy to detect, which makes the operation safety of maintenance personnel in the elevator greatly threatened. This paper proposes an elevator maintenance personnel behavior detection algorithm based on the first-order deep network architecture (FOA-BDNet). First, a lightweight backbone feature extraction network is designed to meet the online real-time requirements of elevator maintenance environment monitoring video stream detection. Then, the feature fusion network structure of "far intersection and close connection" is proposed to fuse the fine-grained information with the coarse-grained information and to enhance the expression ability of deep semantic features. Finally, a first-order deep target detection algorithm adapted to the elevator scene is designed to identify and locate the behavior of maintenance personnel and to correctly detect unsafe behaviors. Experiments show that the detection accuracy rate on the self-built data set in this paper is 98.68%, which is 4.41% higher than that of the latest target detection model YOLOv8-s, and the reasoning speed reaches 69.51fps/s, which can be easily deployed in common edge devices and meet the real-time detection requirements for the unsafe behaviors of elevator scene maintenance personnel.
Fractional differential equations are becoming a considerably more important and popular topic. In order to specify the so-called fractional differential equations, the conventional integer order derivative is generalized to arbitrary order. Fractional differential equations have been extensively employed to explain a variety of physical processes because of the effective memory function of the fractional derivative, such as seepage flow in porous media, fluid dynamic, and traffic models. Also, there are several applications of fractional differential equations in control theory, polymer rheology, aerodynamics, physics, chemistry, biology, and other exciting conceptual advancements (see [1,2,3,4] and their references).
In the real world, there are numerous processes and phenomena that are influenced by transient external factors as they evolve. When compared to the whole duration of the occurrences and processes being researched, their duration is tiny. As a result, it is reasonable to believe that these exterior impacts are "instantaneous", or take the shape of impulses. Differential equations including the impulse effect, or impulsive differential equations, seem to be a plausible explanation of the known evolution processes of various real-world issues. The impulsive differential equations have been studied as the subject of numerous excellent monographs [5,6].
Differential equations are used as representations for many processes in applied sciences research. There are a variety of classical mechanics that experience abrupt changes in their states, such as biological systems (heartbeats and blood flow), mechanical systems with impact, radiophysics, pharmacokinetics, population dynamics, mathematical economics, ecology, industrial robotics, biotechnology processes, etc [7,8]. The systems of differential equations with impulses are suitable mathematical models for such phenomena. Impulsive differential equations essentially have three parts: An impulse equation simulates an impulsive leap that is described by a jump function at the moment of impulse occurs, a continuous-time differential equation determines the state of the system between impulses, and jump criteria identifies a set of jump occurrences [9,10,11].
Furthermore, there are various models that have been developed in many fields like biology, economics, and materials science where the rate of change at time t depends not only on the system's current state but also on its history over a period of time [t−τ,t] [12,13,14,15]. These models have evolutionary equations with delay, which describe them mathematically. Equations with infinite delay are produced by the more generic if we take τ=∞.
Physics provides a compelling justification for studying the nonlocal partial differential equation. Fractional derivatives in space and time are used in abstract partial differential equations such as fractional diffusion equations. They may be used to simulate anomalous diffusion, in which a particle plume spreads differently than the traditional diffusion equation would suggest. By replacing the second-order space derivative in the classic diffusion equation by an infinitesimal generator operator of strongly continuous C0 semi-group or cosine functions, the time fractional evolution equation is derived [16,17,18].
In [19], Kumar and Pandey attempted to examine the results of the existence of a solution to a class of FDEs (the fractional calculus due to Atangana-Baleanu) of the sort
{ABCDρv(r)=Fv(r)+h(r,v(r)),r∈∪mj=0(sj,rj+1],v(r)=δj(r,v(r)),r∈∪mj=1(rj,sj],v(0)=v0−f(v), |
for all v∈D(F) (the domain of F), where ABCDρ is the ABC fractional derivative of order ρ∈(0,1), F:D(F)⊂X→X is a generator of ρ-resolvent operator {Sρ(r)}r≥0 on a Banach space (X,‖⋅‖), J=[0,d], 0=r0<r1<r2<⋯<rm<rm+1 and sj∈(rj,rj+1) for all j=1,2,…,m;m∈N. The functions h:∪mk=0(sj,rj+1]×X→X and f:X→X are given continuous functions and δj:(rj,sj]×X→X are non-instantaneous impulsive functions for each j=1,2,…,m;m∈N and vo∈X.
Recently, Kavitha et al. [20] examined the existence of solutions for a class of non-instantaneous impulses and infinite delay of fractional differential equations within the Mittag-Leffler kernel of the kind
{ABCDvp(ξ)=Bp(ξ)+F(ξ,pξ),ξ∈∪ml=0(sl,ξl+1],p(ξ)=κl(ξ,pξ),ξ∈∪ml=1(ξl,sl],p(ξ)=ϕ(ξ),ξ∈(−∞,0], |
where the fractional order v∈(0,1), B:D(B)⊂E→E is infinitesimal generator of an ρ-resolvent operator {Sρ(ξ)}ξ≥0 on a Banach space (E,‖⋅‖, K=[0,b], 0=ξ0=s0<ξ1≤s1<ξ2<⋯<ξq<ξq+1=b and sl∈(ξl,ξl+1) for all l=1,2,…,q;q∈N. The function F:∪ql=0(sl,ξl+1]×A→E satisfies Caratheodory conditions and the functions κl:(ξl,sl]×A→E are non-instantaneous impulsive functions for each l=1,2,…,q. They considered that pξ:(−∞,0]→E such that pξ(x)=p(ξ+x) for all x≤0 and ϕ∈A where A is an abstract phase space.
In light of the foregoing, in this publication, we examine the existence results for a class of fractional-order non-instantaneous impulses functional evolution equations.
Consider the fractional semilinear evolution of the form
{u(t)=ϕ(t),t∈(−∞,0],cDαtu(t)=Au(t)+h(t,ut),t∈∪mk=0(sk,tk+1],u(t)=μk(t,u(t)),t∈∪mk=1(tk,sk],u′(t)=ξk(t,u(t)),t∈∪mk=1(tk,sk],u′(0)=u0, | (1.1) |
where cDαt is the fractional derivative due to Caputo of order 1<α<2 and J=[0,a] is operational interval. Here, h:J×Ph→X is a given function satisfying some assumptions that will be determined later, where Ph is an abstract phase space and X is a Banach space. The functions μk,ξk∈C((tk,sk]×X;X) for all k=1,2,…m;m∈N reflect the impulsive circumstances and 0=t0=s0<t1≤s1≤t2<⋯<tm≤sm≤tm+1=a are pre-fixed numbers. The history function ut:(−∞,0]→X is an element of Ph and defined by ut(θ)=u(t+θ),θ∈(−∞,0].
The closed operator A is an infinitesimal generator of a uniformly bounded family of strongly continuous cosine operators {R(t)}t∈R, which is defined on a Banach space X. The Banach space of continuous and bounded functions from (−∞,a] into X provided with the topology of uniform convergence is denoted by C=Ca((−∞,a],X) with the norm
‖u‖C=supt∈(−∞,a]|u(t)|. |
As {R(t)}t∈R is a cosine family on X, then there exists ϖ≥1 [21] such that
‖R(t)‖≤ϖ. | (1.2) |
The rest of the text is structured as follows. In section 2, we give some fundamental concepts and lemmas related to our study. In section 3, we formulate the mild solution of (1.1) by considering that operator A is an infinitesimal generator of strongly continuous cosine functions {R(t)}t∈R. By using the fixed-point theorem, Section 4 presents our study outcomes. An instance is provided in Section 5 to be an application.
In this section, we present some concepts and definitions related to the components of the research paper such as fractional calculus, cosine and sine families operators, and abstract phase space. Also, some lemmas that give helpful results to prove the main results of this contribution, are provided.
The definitions of R-L integral and Caputo derivative and the important related lemma are introduced as follow.
Definition 2.1. (Caputo derivative [22]) Let ℓ−1<q≤ℓ;ℓ∈N and x:[a,b]→R (−∞<a<b<∞) be nth continuously differentiable function. Then, the left derivative of fractional order q due to Caputo is presented as
cDqax(s)=1Γ(ℓ−q)∫sa(s−t)ℓ−q−1x(ℓ)(t)dt,s∈[a,b]. |
Definition 2.2. (Riemann-Liouville fractional integral [22]) The left R-L fractional integral of the integrable function x over the interval [a,b] is derived as
Iqax(s)=1Γ(q)∫sa(s−t)q−1x(t)dt,q>0,s∈[a,b]. |
Lemma 2.1. [23] Let ℓ∈N,ℓ−1<q≤ℓ and x(s) be nth continuously differentiable function over the interval [a,b]. Then,
IqacDqax(s)=x(s)+a0+a1(s−a)+⋯+aℓ−1(s−a)ℓ−1,s∈[a,b]. |
Definition 2.3. (Atangana-Baleanu fractional derivative in Caputo sense [19]) ABC-derivative for the order α∈[0,1] and x(s)∈H1(a,b),a<b is given by
ABCDαx(s)=M(α)(1−α)∫saEα[−α(s−r)α1−α]x′(r)dr, |
where Eα(⋅) and H1 are the Mittag-Leffler function and the non-typical Banach space defined, respectively, as
Eα(z)=∞∑i=0ziΓ(αi+1),ℜ(α)>0,z∈C,H1(Ω)={η(s)|η(s),Dρη(s)∈L2(Ω),∀ρ≤1}. |
Let A be an infinitesimal generator of a uniformly bounded family of strongly continuous cosine operators {R(t)}t∈R which is defined on a Banach space X. We collect some basic properties of a cosine family and its relations with the operator A and the associated sine family.
Definition 2.4. [24] Consider {R(t)}t∈R is a one parameter family of bounded linear operators mapping the Banach space X→X. It is referred to a strongly continuous cosine family if and only if
(ⅰ) R(0)=I;
(ⅱ) R(s+t)+R(s−t)=2R(s)R(t) for all s,t∈R;
(ⅲ) The function t↦R(t)x is a continuous on R for any x∈X.
The sine family {T(t)}t∈R is correlated to the strongly continuous cosine family {R(t)}t∈R, it is characterized by
T(t)x=∫t0R(s)xds,x∈X,t∈R. |
Lemma 2.2. [24] Consider A is an infinitesimal generator of a strongly continuous cosine family {R(t)}t∈R on Banach space X such that ‖R(t)‖≤Meξ|t|,t∈R. Then, for λ>ξ and (ξ2,∞)⊂ρ(A) (the resolvent set of A), we have
λR(λ2;A)x=∫∞0e−λtR(t)xdt,R(λ2;A)x=∫∞0e−λtT(t)xdt,x∈X, |
where the operator R(λ;A)=(λI−A)−1 is the resolvent of the operator A and λ∈ρ(A).
In this case, the operator A is defined by
Ax=limt→0d2dt2R(t)x,∀x∈D(A), |
where D(A)={x∈X:R(t)x∈C2(R,X)} is the domain of the operator A. Clearly the infinitesimal generator A is densely defined operator in X and closed.
In the sequel to present our results, we need the following:
Definition 2.5. Suppose that τ>0, the Mainardi's Wright-type function is defined as
Mϱ(τ)=∞∑n=0(−τ)nn!Γ(1−ϱ(n+1)),ϱ∈(0,1),τ∈C, |
and achieves
Mϱ(τ)≥0,∫∞0θξMϱ(θ)dθ=Γ(1+ξ)Γ(1+ϱξ),ξ>−1. |
The abstract phase space Ph is demonstrated by convenient way [25,26]. Let h=C((−∞,0],[0,∞)) with ∫0−∞h(t)dt<∞. Then, for any c>0, we can define the set
P={A:[−c,0]→X,A is bounded and measurable}, |
and establish the space P with the norm
‖A‖P=sups∈[−c,0]|A(s)|,for allA∈P. |
Let us define the space
Ph={A:(−∞,0]→Xsuch that for any c>0,A|[−c,0]∈Pand∫0−∞h(t)supt≤s≤0A(s)dt<∞}. |
If Ph is furnished with the norm
‖A‖Ph=∫0−∞h(t)supt≤s≤0‖A(s)‖dt,∀A∈Ph, |
then (Ph,‖⋅‖Ph) is a Banach space. Next, we introduce the available space
¯Ph={v:(−∞,a]→Xsuch thatv|[0,a]∈C((tk,tk+1],X),v|(−∞,0]=ϕ∈Ph}, |
which has the norm
‖x‖¯Ph=sups∈[0,a]‖v(s)‖+‖ϕ‖Ph,x∈¯Ph. |
Definition 2.6. [27] If v:(−∞,a]→X,a>0, such that ϕ∈Ph. The situations listed below are accurate for all τ∈[0,a],
1) vτ∈Ph;
2) Two functions, ζ1(τ),ζ2(τ)>0, are such that ζ1(τ):[0,∞)→[0,∞) is a continuous function and ζ2(τ):[0,∞)→[0,∞) is a locally bounded function which are independent to v(⋅) whereas
‖vτ‖Ph≤ζ1(τ)sup0<s<τ‖v(s)‖+ζ2(τ)‖ϕ‖Ph; |
3) ‖v(τ)‖≤H‖vτ‖Ph, where H>0 is a constant.
Before introducing the mild solution of evolution Eq (1.1), we have to establish the following Lemmas.
Lemma 3.1. Let Iαs be the left R-L integral of order α and f(t) is integrable function defined for t≥s≥0. Then,
∫∞se−λtIαsf(t)dt=λ−α∫∞se−λtf(t)dt. |
Proof. From the Definition 2.2 and the rule of converting double integral to single integral, we get
∫∞se−λtIαsf(t)dt=∫∞se−λt∫ts(t−τ)α−1f(τ)dτdt=1Γ(α)∫∞sf(τ)dτ∫∞τe−λt(t−τ)α−1dt=1Γ(α)∫∞sf(τ)e−λτdτ∫∞0tα−1e−λtdt=λ−α∫∞se−λtf(τ)dτ. |
The proof is over.
Lemma 3.2. Let 1<α≤2 and h:J→X be an integrable function. Then, the mild solution to our problem (1.1) possess the form
u(t)={ϕ(t),t∈(−∞,0],Rq(t)ϕ(0)+∫t0Rq(s)uods+∫t0(t−s)q−1Tq(t,s)h(s)ds,t∈[0,t1],μk(t,u(t)),t∈∪mk=1(tk,sk],Rq(t−sk)μk(sk,u(sk))+∫tskRq(y−sk)ξk(sk,u(sk))dy+∫tsk(t−y)q−1Tq(t−y)h(y)dy,t∈∪mk=1(sk,tk+1], |
where 1/2<q=α2≤1,
Rq(t)=∫∞0Mq(θ)R(tqθ)dθ,Tq(t,s)=q∫∞0θMq(θ)T((t−s)qθ)dθ, |
and Mq is a probability density function defined by Definition 2.5.
Proof. Using Lemma 2.1 with operating by Iαsr on both sides to the fractional differential equation in (1.1), we arrive at
u(t)=Iαsk[Au(t)+h(t)]+c1,k(t−sk)+c0,k, | (3.1) |
where c1,k,c0,k∈R,k=0,1,…,m are constants to be determined.
● For t∈[0,t1]: By taking ρ→1 to the results given in Lemma 5 in [28], we have
u(t)=Rq(t)ϕ(0)+∫t0Rq(s)uods+∫t0(t−s)q−1Tq(t,s)h(s)ds. |
● For t∈(t1,s1]: We obtain
u(t)=μ1(t,u(t)) andu′(t)=ξ1(t,u(t)). |
● For t∈(s1,t2]: The problem (1.1) becomes
cDαs1u(t)=Au(t)+h(t),u(s1)=μ1(s1,u(s1)),u′(s1)=ξ1(s1,u(s1)). |
In this interval, Eq (3.1) becomes
u(t)=Iαs1[Au(t)+h(t)]+c1,1(t−s1)+c0,1. |
Considering the past impulsive conditions, we get
c0,1=μ1(s1,u(s1))andc1,1=ξ1(s1,u(s1)), |
which imply that
u(t)=Iαs1[Au(t)+h(t)]+ξ1(s1,u(s1))(t−s1)+μ1(s1,u(s1)). |
Multiplying both sides by e−λt followed by integrating from s1 to ∞, we achieve
U(λ)=λ−α{AU(λ)+H(λ)}+λ−1e−λs1μ1(s1,u(s1))+λ−2e−λs1ξ1(s1,u(s1)), |
where
U(λ)=∫∞s1u(t)e−λtdtandH(λ)=∫∞s1h(t)e−λtdt. |
Given that (λαI−A)−1 exists, then λα∈ρ(A). We obtain
U(λ)=(λαI−A)−1{λα−1e−λs1μ1(s1,u(s1))+λα−2e−λs1ξ1(s1,u(s1))+H(λ)}=λq−1e−λs1∫∞0e−λqtR(t)μ1(s1,u(s1))dt+λq−2e−λs1∫∞0e−λqtR(t)ξ1(s1,u(s1))dt+∫∞0e−λqtT(t)H(λ)dt. |
Let Ψq(θ)=qθq+1Mq(θ−q) be defined for θ∈(0,∞) and q∈(12,1). Then,
∫∞0e−pθΨq(θ)dθ=e−pq, |
which can be used to calculate the first term with replacing t by sq as
λq−1e−λs1∫∞0e−λqtR(t)μ1(s1,u(s1))dt=q∫∞0(λs)q−1e−(λs)qR(sq)e−λs1(μ1s1,u(s1))ds=−1λ∫∞0dds(e−(λs)q)R(sq)e−λs1(μ1s1,u(s1))ds=∫∞0∫∞0θΨq(θ)e−λsθR(sq)e−λs1(μ1s1,u(s1))dθds=∫∞0e−λ(x+s1){∫∞0Ψq(θ)R((xθ)q)μ1(s1,u(s1))dθ}dx=∫∞0e−λ(x+s1){∫∞0Mq(θ)R(xqθ)μ1(s1,u(s1))dθ}dx=∫∞0e−λ(x+s1)Rq(x)μ1(s1,u(s1))dx=∫∞s1e−λtRq(t−s1)μ1(s1,u(s1))dt. |
By using Lemma 3.1 with α=1, we get
λq−2∫∞0e−λqtRq(t)e−λs1ξ1(s1,u(s1))dt=∫∞s1e−λt{∫ts1Rq(y−s1)ξ1(s1,u(s1))dy}dt. |
Finally, we can write
∫∞0e−λqtT(t)H(λ)dt=q∫∞0e−(λs)qT(sq)sq−1H(λ)ds=q∫∞0∫∞0e−λsθΨq(θ)T(sq)sq−1H(λ)dθds=q∫∞0∫∞s1∫∞0θ−qe−λxΨq(θ)T((xθ)q)xq−1e−λyh(y)dθdydx=q∫∞0∫∞s1∫∞0e−λ(x+y)θMq(θ)T(xqθ)xq−1h(y)dθdydx=q∫∞s1∫∞y∫∞0e−λtθMq(θ)T((t−y)qθ)(t−y)q−1h(y)dθdtdy=∫∞s1∫∞ye−λt(t−y)q−1Tq(t−y)h(y)dtdy=∫∞s1e−λt{∫ts1(t−y)q−1Tq(t−y)h(y)dy}dt. |
In conclusion, we can write
∫∞s1e−λtu(t)dt=∫∞s1e−λt{Rq(t−s1)μ1(s1,u(s1))+∫ts1Rq(y−s1)ξ1(s1,u(s1))dy+∫ts1(t−y)q−1Tq(t−y)h(y)dy}dt. |
Therefore, by taking the inverse Laplace transform, we have
u(t)=Rq(t−s1)μ1(s1,u(s1))+∫ts1Rq(y−s1)ξ1(s1,u(s1))dy+∫ts1(t−y)q−1Tq(t−y)h(y)dy. |
● For t∈(sk,tk+1],k=2,3,…,m: In a similar manner, we can write
u(t)=Rq(t−sk)μk(sk,u(sk))+∫tskRq(y−sk)ξk(sk,u(sk))dy+∫tsk(t−y)q−1Tq(t−y)h(y)dy. |
Consequently, we get the solution from the earlier (1.1). Direct calculations show that the opposite results are true. The proof is completed.
Remark 3.1. [28] From linearity of R(t) and T(t) for all t≥0, it is clearly to deduce that Rq(t) and Tq(t,s) are also linear operators where 0<s<t. Therefore, the proofs of all next Lemmas are same when taking ρ approaches 1.
Lemma 3.3. [28] The following estimates for Rq(t) and Tq(t,s) are verified for any fixed t≥0 and 0<s<t
|Rq(t)x|≤ϖ|x|and|Tq(t,s)x|≤ϖaqΓ(2q)|x|. |
Lemma 3.4. [28] The operators Rq(t) and Tq(s,t) are strongly continuous for every 0<s<t and t>0.
Lemma 3.5. [28] Assume that R(t) and T(t,s) are compact for every 0<s<t. Then, the operators Rq(t) and Tq(s,t) are compact for every 0<s<t.
Define the operator N:¯Ph→¯Ph as follows
N(u)(t)={ϕ(t),t∈(−∞,0],Rq(t)ϕ(0)+∫t0Rq(y)uody+∫t0(t−y)q−1Tq(t,y)h(y,uy)dy,t∈[0,t1],μk(t,u(t)),t∈∪mk=1(tk,sk],Rq(t−sk)μk(sk,u(sk))+∫tskRq(y−sk)ξk(sk,u(sk))dy,+∫tsk(t−y)q−1Tq(t−y)h(y,uy)dy,t∈∪mk=1(sk,tk+1]. |
Let ϰ(⋅):(−∞,a]→X be the function denoted by
ϰ(t)={ϕ(t),t∈(−∞,0],0,t∈(0,a]. |
Plainly, ϰ(0)=ϕ(0). For each z∈C([0,a],X) with z(0)=0, we indicate by ϑ to the function defined as
ϑ(t)={0,t∈(−∞,0],z(t),t∈[0,a]. |
If u(⋅) satisfies that u(t)=N(u)(t) for all t∈(−∞,a], we can decompose that u(t)=ϑ(t)+ϰ(t), t∈(−∞,a], it denotes ut=ϑt+ϰt for every t∈(−∞,a] and the function z(⋅) satisfies
z(t)={Rq(t)ϕ(0)+∫t0Rq(y)uody+∫t0(t−y)q−1Tq(t,y)h(y,ϑy+ϰy)dy,t∈[0,t1],μk(t,ϑ+x),t∈∪mk=1(tk,sk],Rq(t−sk)μk(sk,ϑ+ϰ)+∫tskRq(y−sk)ξk(sk,ϑ+ϰ)dy,+∫tsk(t−y)q−1Tq(t−y)h(ϑy+ϰy)dy,t∈∪mk=1(sk,tk+1]. |
Set the space Υ={z∈C([0,a],X),z(0)=0} equipped the norm
‖z‖Υ=supt∈[0,a]‖z(t)‖. |
Therefore, (Υ,‖⋅‖Υ) is a Banach space. Assume that the operator G:Υ→Υ is formulated as follows:
G(z)(t)={Rq(t)ϕ(0)+∫t0Rq(y)uody+∫t0(t−y)q−1Tq(t,y)h(y,ϑy+ϰy)dy,t∈[0,t1],μk(t,ϑ+x),t∈∪mk=1(tk,sk],Rq(t−sk)μk(sk,ϑ+ϰ)+∫tskRq(y−sk)ξk(sk,ϑ+ϰ)dy,+∫tsk(t−y)q−1Tq(t−y)h(ϑy+ϰy)dy,t∈∪mk=1(sk,tk+1]. |
The operator N seems to have a fixed point is equivalent to G has a fixed point. Thus, we proceed to prove that G has a fixed point.
Now, we make the following assumptions:
(E1) The function h:[0,a]×Ph→X is a continuous and μk,ξk:[tk,sk]×X→X are continuous functions for all k=1,2,…,m;m∈N.
(E2) There is a constant Ω>0 satisfying
‖h(t,ut)−h(t,vt)‖≤Ω‖ut−vt‖Ph. |
(E3) There exist δk,δ∗k>0;k=1,2,…,m;m∈N such that
‖μk(t,u)‖≤δkand‖ξk(t,u)‖≤δ∗k. |
(E4) There are positive constants Dk,D∗k,k=1,2,…,m;m∈N such that
‖μk(t,u1)−μk(t,u2)‖≤Dk‖u1−u2‖, |
‖ξk(t,u1)−ξk(t,u2)‖≤D∗k‖u1−u2‖. |
(E5) There exists a continuous function g(t):[0,a]→[0,∞) such that, for any (t,ut)∈[0,a]×Ph, it satisfies
‖h(t,ut)‖≤g(t)‖ut‖Ph. |
The brief constants that will be utilized later to streamline handling, are listed as follow
E(q)=tq+11qΓ(2q),Ek(q)=a(a−sk)qqΓ(2q),B=ϖΩζ∗1,¯B=ϖgζ∗1E(q),Bk=ϖHζ∗1[Dk+D∗k(a−sk)],¯Bk=ϖgζ∗1Ek(q),O=ϖ(‖ϕ(0)‖+‖uo‖t1+E(q)Ωζ∗2‖ϕ‖Ph+E(q)c),Ok=ϖ(δk+δ∗k(a−sk)+Ek(q)Ωζ∗2‖ϕ‖Ph+Ek(q)c),Q=ϖ(‖ϕ(0)‖+‖uo‖t1+E(q)ζ∗2g‖ϕ‖Ph),Qk=ϖ(δk+δ∗k(a−sk)+Ek(q)ζ∗2g‖ϕ‖Ph) |
where k=1,2,…,m;m∈N.
Lemma 4.1. Assume that the requirement (E2) is met by c=maxt∈[0,a]|h(t,0)|. Ponder about the expressions ζ∗1=supt∈[0,a]ζ1(t) and ζ∗2=supt∈[0,a]ζ2(t) where ζ1(⋅) and ζ2(⋅) are established in Definition 2.6. Then,
‖h(t,ϑt+ϰt)‖≤Ω(ζ∗1‖z‖Υ+ζ∗2‖ϕ‖Ph)+c. |
Proof. Regarding Definition 2.6 and the presumption (E2). Then,
‖h(t,ϑt+ϰt)‖=‖h(t,ϑt+ϰt)−h(t,0)+h(t,0)‖≤‖h(t,ϑt+ϰt)−h(t,0)‖+‖h(t,0)‖≤Ω‖ϑt+ϰt‖Υ+c≤Ω(ζ1(t)supt∈[0,a]‖ϑ(t)‖+ζ2(t)‖ϕ‖Ph)+c≤Ω(ζ1(t)‖z‖Υ+ζ2(t)‖ϕ‖Ph)+c≤Ω(ζ∗1‖z‖Υ+ζ∗2‖ϕ‖Ph)+c. |
This ends the proof.
Lemma 4.2. Suppose that the statement (E5) is satisfied with g=supt∈[0,a]g(t). Let ζ∗1=supt∈[0,a]ζ1(t) and ζ∗2=supt∈[0,a]ζ2(t) where ζ1(⋅) and ζ2(⋅) are outlined in Definition 6. Then,
‖h(t,ϑt+ϰt)‖≤ℓ(t)≤ℓ, |
where
ℓ=supt∈[0,a]ℓ(t)=supt∈[0,a]{g(t)(ζ1(t)‖z‖Υ+ζ2(t)‖ϕ‖Ph)}=g(ζ∗1‖z‖Υ+ζ∗2‖ϕ‖Ph). |
Proof. By the same way in Lemma 4.1, we can easily reach the desired result.
Theorem 4.1. Consider the assertions (E1)−(E4) hold and
Λ=maxk{BE(q),DkHζ∗1,Bk+BEk(q)}. |
Then, the fractional evolution equation with non-instantaneous impulsive (1.1) has a unique mild solution on (−∞,a] if Λ<1.
Proof. To show that the operator G maps bounded subset of Υ into bounded subset in Υ, we set
Υr={z∈Υ:‖z‖Υ≤r}, |
where
r≥maxk{O1−BE(q),δk,Ok1−BEk(q)}. |
Then, for any z∈Υr and in spite of (E2) and (E3) and Lemma 4.1. Correspondingly, three situations are taken into consideration.
● Case Ⅰ. Whenever t∈[0,t1], we have
‖G(z)(t)‖Υ≤ϖ(‖ϕ(0)‖+‖uo‖t1)+ϖt1Γ(2q)∫t0(t−s)q−1‖h(y,ϑy+ϰy)‖dy≤ϖ[‖ϕ(0)‖+‖uo‖t1+tqt1qΓ(2q){Ω(ζ∗1‖z‖Υ+ζ∗2‖ϕ‖Ph)+c}]≤ϖ[‖ϕ(0)‖+‖uo‖t1+E(q){Ω(ζ∗1‖z‖Υ+ζ∗2‖ϕ‖Ph)+c}]≤O+E(q)B‖z‖Υ≤O+E(q)Br≤r. |
● Case Ⅱ. Whenever t∈(tk,sk],k=1,…,m;m∈N, we have
‖G(z)(t)‖Υ=‖μk(t,ϑ+ϰ)‖≤δk. |
● Case Ⅲ. Whenever t∈(sk,sk+1],k=1,…,m;m∈N, we have
‖G(z)(t)‖Υ≤ϖ[δk+δ∗k(t−sk)+aΓ(2q)∫tsk(t−y)q−1‖h(y,ϑy+ϰy)‖dy]≤ϖ[δk+δ∗k(a−sk)+a(a−sk)qqΓ(2q){Ω(ζ∗1‖z‖Υ+ζ∗2‖ϕ‖Ph)+c}]≤ϖ[δk+δ∗k(a−sk)+Ek(q){Ω(ζ∗1‖z‖Υ+ζ∗2‖ϕ‖Ph)+c}]≤Ok+Ek(q)Br≤r. |
For the aforementioned, we acquire ‖G(z)(t)‖Υ≤r. Thus, the operator G maps bounded subset into bounded subset in Υ.
Now, we prove that the operator G is a contraction mapping. Certainly, consider z,z∗∈Υ. Then, there still are the subsequent situations.
● Case Ⅰ. For any t∈[0,t1], we have
‖G(z)(t)−G(z∗)(t)‖≤ϖt1Γ(2q)∫t0(t−s)q−1‖h(y,ϑy+ϰy)−h(y,ϑ∗y+ϰy)‖dy≤Ωϖt1Γ(2q)∫t0(t−s)q−1‖ϑy−ϑ∗y‖Phdy≤Ωϖt1Γ(2q)ζ∗1‖z−z∗‖Υ∫t0(t−s)q−1dy≤Ωϖtq+11qΓ(2q)ζ∗1‖z−z∗‖Υ=BE(q)‖ϑ−ϑ∗‖Υ. |
● Case Ⅱ. For any t∈(tk,sk],k=1,…,m;m∈N, we have
‖G(z)(t)−G(z∗)(t)‖=‖μk(t,ϑ+ϰ)−μk(t,ϑ∗+ϰ)‖≤Dk‖ϑ−ϑ∗‖Υ≤DkH‖zt−z∗t‖Ph≤DkHζ∗1‖z−z∗‖Υ=DkHζ∗1‖ϑ−ϑ∗‖Υ. |
● Case Ⅲ. For any t∈(sk,sk+1],k=1,…,m;m∈N, we have
‖G(z)(t)−G(z∗)(t)‖≤‖Rq(t−sk)‖‖μk(sk,ϑ+ϰ)−μk(sk,ϑ∗+ϰ)‖+∫tsk‖Rq(y−sk)‖‖ξk(sk,ϑ+ϰ)−ξk(sk,ϑ∗+ϰ)‖dy+∫tsk(t−y)q−1‖Tq(t−y)‖‖h(ϑy+ϰy)−h(ϑ∗y+ϰy)‖dy≤ϖDk‖ϑ−ϑ∗‖Υ+ϖD∗k∫tsk‖ϑ(y)−ϑ∗(y)‖Υdy+ϖaΓ(2q)Ω∫tsk(t−y)q−1‖ϑy−ϑ∗y‖Phdy≤ϖDkH‖zy−z∗y‖Ph+ϖD∗kH∫tsk‖zy−z∗y‖Phdy+ϖaΓ(2q)Ω∫tsk(t−y)q−1‖ϑy−ϑ∗y‖Phdy≤ϖHζ∗1[Dk+D∗kEk(q)]‖z−z∗‖Υ+ϖaΓ(2q)Ωζ∗1∫tsk(t−y)q−1‖z−z∗‖Υdy≤ϖHζ∗1[Dk+D∗k(a−sk)+a(a−sk)qΩ qΓ(2q)H]‖z−z∗‖Υ=(Bk+BEk(q))‖z−z∗‖Υ=(Bk+BEk(q))‖ϑ−ϑ∗‖Υ. |
For the aforementioned, we may write
‖G(z)(t)−G(z∗)(t)‖Υ≤Λ‖ϑ−ϑ∗‖Υ. |
Amid the existing circumstances, Λ<1 shows that the operator G is a contraction. This suggests that the problem (1.1) has a unique solution on (−∞,a] relying on the Banach contraction mapping principle.
Remark 4.1. In viewing our problem, it is very difficult to obtain the exact solution and so it is useful to investigate some properties of the solutions, especially the uniqueness. The previous theorem shaw that the mild solution of the problem (1.1) is unique under the assumptions (E1)−(E4) and Λ<1. This enables us to apply our results to real-life problem or phenomena as in the last section.
Assume that the operator G is divided as a sum of the two operators Gi:Υ→Υ,i=1,2 as
G=G1(z)+G2(z) | (4.1) |
where,
G1(z)(t)={Rq(t)ϕ(0)+∫t0Rq(y)uody+∫t0(t−y)q−1Tq(t,y)h(y,ϑy+ϰy)dy,t∈[0,t1],0,t∈∪mk=1(tk,sk],∫tsk(t−y)q−1Tq(t−y)h(ϑy+ϰy)dy,t∈∪mk=1(sk,tk+1] |
and
G2(z)(t)={0,t∈[0,t1],μk(t,ϑ+x),t∈∪mk=1(tk,sk],Rq(t−sk)μk(sk,ϑ+ϰ)+∫tskRq(y−sk)ξk(sk,ϑ+ϰ)dy,t∈∪mk=1(sk,tk+1]. |
Theorem 4.2. Suppose the hypotheses (E1)and(E3)−(E5) are correct. Then the fractional evolution equation with non-instantaneous impulsive (1.1) has at least one mild solution on (−∞,a] if Δ<1 where Δ is given by
Δ=maxk{¯B,¯Bk}. |
Proof. Let the operators G1 and G2 be defined as (4.1). Setting g=supt∈[0,a]|g(t)|. Let us define the closed ball Υρ={z∈Υ:‖z‖Υ≤ρ} with radius
ρ≥maxk{Q1−¯B,δk,Qk1−¯Bk}. |
Then, for u,v∈Υρ, we claim that ‖G1(z)(u)+G2(z)(v)‖≤ρ which concludes that G1(u)+G1(v)∈Υρ. To verify our claiming, we show that G maps bounded sets of Υ into bounded sets in Υ, for any ρ≥0. Then for any z∈Υρ and in light of (E3),(E5) and Lemma 4.2, we have three cases
● Case Ⅰ. For any t∈[0,t1], we have
‖G(z)(t)‖Υ≤ϖ(‖ϕ(0)‖+‖uo‖t1)+ϖt1Γ(2q)∫t0(t−s)q−1‖h(y,ϑy+ϰy)‖dy≤ϖ[‖ϕ(0)‖+‖uo‖t1+t1Γ(2q)∫t0(t−s)q−1{g(y)(ζ1(y)‖z‖Υ+ζ2(y)‖ϕ‖Ph)}dy]≤ϖ[‖ϕ(0)‖+‖uo‖t1+E(q){g(ζ∗1‖z‖Υ+ζ∗2‖ϕ‖Ph)}]≤Q+E(q)¯B‖z‖Υ≤Q+¯Bρ≤ρ. |
● Case Ⅱ. For any t∈(tk,sk],k=1,…,m;m∈N, we have
‖G(z)(t)‖Υ=‖μk(t,ϑ+ϰ)‖≤δk≤ρ. |
● Case Ⅲ. For any t∈(sk,sk+1],k=1,…,m;m∈N, we have
‖G(z)(t)‖Υ≤ϖ[δk+δ∗k(t−sk)+aΓ(2q)∫tsk(t−y)q−1‖h(y,ϑy+ϰy)‖dy]≤ϖ[δk+δ∗k(a−sk)+aΓ(2q)∫tsk(t−y)q−1{g(y)(ζ1(y)‖z‖Υ+ζ2(y)‖ϕ‖Ph)}dy]≤ϖ[δk+δ∗k(a−sk)+a(a−sk)qqΓ(2q){g(ζ∗1‖z‖Υ+ζ∗2‖ϕ‖Ph)}]≤ϖ[δk+δ∗k(a−sk)+Ek(q){g(ζ∗1‖z‖Υ+ζ∗2‖ϕ‖Ph)}]≤Qk+¯Bkρ≤ρ. |
By virtue of the above, we obtain ‖G(z)(t)‖Υ≤ρ. Thus the operator G maps bounded sets into bounded sets in Υ.
The following step is to confirm that the operator G2 maps bounded sets into equicontinuous sets in Υ. In light of the situation (E1), G2 is continuous. The following scenarios are therefore possible.
● Case Ⅰ. For each tk≤γ1<γ2≤sk and z∈Υρ, we have
‖G2(z)(γ2)−G2(z)(γ1)‖≤‖μk(γ2,ϑ+ϰ)−μk(γ1,ϑ+ϰ)‖. |
Due to the continuity of μ(t,u(t)). It is clear that the above inequality approaches zero when letting γ2→γ1.
● Case Ⅱ. For any sk≤γ1<γ2≤tk+1,k=1,…,m;m∈N and z∈Υρ, we have
‖G2(z)(γ2)−G2(z)(γ1)‖≤δk‖Rq(γ2−sk)−Rq(γ1−sk)‖+δ∗k∫γ2γ1‖Rq(y−sk)‖dy≤δk‖Rq(γ2−sk)−Rq(γ1−sk)‖+δ∗kϖ(γ2−γ1). |
Due to compactness of operator Rq(y) and Tq(t,y) (see Lemma 3.5), we infer that ‖G2(z)(γ1)−G2(z)(γ2)‖→0 as γ2→γ1. Thus, G2 is a relatively compact on Υ. By Arezela Ascoli Theorem the operator G2 is completely continuous on Υρ. The only thing left to do is provide evidence that G1 is a contraction mapping. Thus, two cases are thought about.
● Case Ⅰ. For any t∈[0,t1],k=1,…,m;m∈N and z∈Υρ, we have
‖G1(z)(t)−G1(z∗)(t)‖≤ϖt1Γ(2q)∫t0(t−y)q−1‖h(y,ϑy+ϰy)−h(y,ϑ∗y+ϰy)‖dy≤ϖt1Γ(2q)∫t0(t−y)q−1g(y)‖ϑy−ϑ∗y‖Phdy≤gϖt1Γ(2q)ζ∗1‖z−z∗‖Υ∫t0(t−s)q−1dy≤gϖtq+11qΓ(2q)ζ∗1‖z−z∗‖Υ=¯B‖ϑ−ϑ∗‖Υ. |
● Case Ⅱ. For any t∈(sk,tk+1],k=1,…,m;m∈N, we have
‖G1(z)(t)−G1(z∗)(t)‖≤∫tsk(t−y)q−1‖Tq(t−y)‖‖h(ϑy+ϰy)−h(ϑ∗y+ϰy)‖dy≤ϖaΓ(2q)∫tsk(t−y)q−1g(y)‖ϑy−ϑ∗y‖Phdy≤aΓ(2q)ϖgζ∗1‖z−z∗‖Υ∫tsk(t−y)q−1dy≤a(a−sk)q qΓ(2q)ϖgζ∗1‖z−z∗‖Υ=¯Bk‖z−z∗‖Υ. |
As a sense, the fractional evolution equation with non-instantaneous impulsive (1.1) has at least one mild solution on Υ, according to the Krasnoselskii Theorem. The evidence is now complete.
Remark 4.2. Also, it is useful to investigate the existence of the solution instead of the its uniqueness. The theorem above shaw that the mild solution of the problem (1.1) exists under the assumptions (E1)−(E3) and (E5) with Δ<1.
Presume the following fractional wave equation with impulsive effect and infinite delay
{u(t,x)=13sint,t∈(−∞,0],x∈[0,π],cDαtu(t,x)=∂2∂x2u(t,x)+h(t,ut),t∈(0,25]∪(45,1],x∈[0,π],u(t,x)=17t32+15sinu(t),t∈(25,45]x∈[0,π],u′(t)=314t12+18cosu(t),t∈(25,45]x∈[0,π],u(t,0)=u(t,π)=0,t∈[0,1],u′(0,x)=32e−x3,x∈[0,π]. |
Consider that
J=[0,1],0=t0=s0<t1=25<s1=45<t2<1=a,α=32⇒q=34,u0=32e−x3,A=∂2∂x2,x∈[0,π],H=116.Whileζ1(t)=35t32⇒ζ∗1=35{supt∈(0,1]t32}≤ζ1(1)=35.If we takeϖ=1⇒‖Tq(t,s)‖≤1Γ(34),0<s<t≤1. |
Case Ⅰ. Banach fixed point theorem.
In order to explain Theorem 4.1, we obtain:
h(t,ut)=t38√t+1+19ut. | (5.1) |
Clearly, h:[0,1]×Ph→R is continuous and satisfying, for ut,vt∈Ph, that
‖h(t,ut)−h(t,vt)‖≤19‖ut−vt‖Ph, |
it suggests that Ω=19. For all t∈(25,45] and u,v∈R, we get
‖μ(t,u)−μ(t,v)‖≤15‖sinu−sinv‖≤15‖u−v‖,‖ξ(t,u)−ξ(t,v)‖≤18‖cosu−cosv‖≤18‖u−v‖. |
As you can see, the Theorem 4.1 condition (E4) is satisfied with
Dk=15andD∗k=18. |
In summary, we have
Λ=maxk{BE(q),DkHζ∗1,Bk+BEk(q)}={0.0202,0.0075,0.0384}=0.0384<1. |
Thus all assumptions of this theorem are verified. Therefore, the problem (1.1) has a unique mild solution on (−∞,1].
Case Ⅱ. Krasnoselskii's theorem.
To realize Theorem 4.2, take h(t,ut) as given in (5.1). Therefore, g(t)=t38√t+1 is increasing function which admits the hypothesis (E5) with
‖g‖≤g(1)=18√2. |
These calculate that
Δ=maxk{¯B,¯Bk}=maxk{0.0161,0.0239}=0.0239<1. |
Since every requirements of Theorem 4.2 are met, it follows that there exists at least one mild solution of (1.1) on (−∞,1].
We analyzed a set of impulsive fractional evolution equations with infinite delay in the current work. Current functional analysis methodologies serve as the foundation for our conclusions. By using the unbounded operator A as the generator of the strongly continuous cosine family, we were able to suggest a mild solution for the suggested problem. In the instance of problem (1.1), we had two successful outcomes: While the second argument focuses on whether there are solutions for the given problem, the first argument concentrated on the existence and uniqueness of the solution.
The first result, which is built on a Banach fixed point theorem, provides criteria for ensuring that the problem at hand has no prior solutions by requiring the usage of h(t,ut) to satisfy the classic Lipschitz condition.
The second argument was based on a Krasnoselskii's theorem, which allows h(t,ut) to behave as ‖h(t,ut)‖≤g(t)‖ut‖Ph. The instruments used by fixed point theory in the scenario with simple assumptions. Finally, a numerical example that examines a function that satisfies all the prerequisites was provided to illustrate our conclusion.
In the next paper, we will study the controllability of mild solution to fractional evolution equations with an infinite time-delay and nonlocal condition by applying Krasnoselskii's theorem in the compactness case and the Sadvskii and Kuratowski measure of noncompactness.
The Deanship of Scientific Research (DSR) at King Abdulaziz University (KAU), Jeddah, Saudi Arabia has funded this project, under grant no. (KEP-PhD: 34-130-1443).
The authors declare that they have no conflicts of interest.
[1] |
D. C. Balmer, Impact of the A18. 1 ASME Standard on platform lifts and stairway chairlifts on accessibility and usability, Assist. Technol., 22 (2010), 46−50. https://doi.org/10.1080/10400430903520264 doi: 10.1080/10400430903520264
![]() |
[2] |
C. Cheng, S. Zhang, Z. Wang, L. Qiu, L. Tu, L. Zhu, et al., Surrogate-model-based dynamic-sensing optimization method for high-speed elevator car horizontal vibration reduction, Proc. I. Mech. Eng. Part C, 2024. https://doi.org/10.1177/09544062231217926 doi: 10.1177/09544062231217926
![]() |
[3] |
P. C. Liao, Z. Guo, T. Wang, J. Wen, C. H. Tsai, Interdependency of construction safety hazards from a network perspective: A mechanical installation case, Int. J. Occup. Saf. Ergo., 26 (2020), 245−255. https://doi.org/10.1080/10803548.2018.1426272 doi: 10.1080/10803548.2018.1426272
![]() |
[4] |
J. Lei, W. Sun, Y. Fang, N. Ye, S. Yang, J. Wu, A model for detecting abnormal elevator passenger behavior based on video classification, Electronics, 13 (2024), 2472. https://doi.org/10.3390/electronics13132472 doi: 10.3390/electronics13132472
![]() |
[5] | H. Hasegawa, S. Aida, Elevator monitoring system to guide user's behavior by visualizing the state of crowdedness, In: Lee, R. (eds) Big Data, Cloud Computing, and Data Science Engineering, Springer, Cham, 2020, 85−98. https://doi.org/10.1007/978-3-030-24405-7_6 |
[6] |
S. Liang, D. Niu, K. Huang, H. Wu, L. Ding, Y. Yang, An elevator door blocking behavior recognition method based on two-stage object detection networks, IEEE, 2022, 1374−1378. https://doi.org/10.1109/YAC57282.2022.10023898 doi: 10.1109/YAC57282.2022.10023898
![]() |
[7] |
Z. Wang, J. Chen, P. Yu, B. Feng, D. Feng, SC-YOLOv8 network with soft-pooling and attention for elevator passenger detection, Appl. Sci., 14 (2024), 3321. https://doi.org/10.3390/app14083321 doi: 10.3390/app14083321
![]() |
[8] |
S. Chai, X. I. Li, Y. Jia, Y. He, C. H. Yip, K. K. Cheung, et al., A non-intrusive deep learning based diagnosis system for elevators, IEEE Access, 9 (2021), 20993−21003. https://doi.org/10.1109/ACCESS.2021.3053858 doi: 10.1109/ACCESS.2021.3053858
![]() |
[9] |
S. C. Lai, M. L. Yang, R. J. Wang, J. Y. Jhuang, M. C. Ho, Y. C. Shiau, Remote-control system for elevator with sensor technology, Sensor. Mater., 34 (2022). https://doi.org/10.18494/SAM3827 doi: 10.18494/SAM3827
![]() |
[10] |
Z. Li, J. Ning, T. Li, Design of non-intrusive online monitoring system for traction elevators, Appl. Sci., 14 (2024), 4346. https://doi.org/10.3390/app14114346 doi: 10.3390/app14114346
![]() |
[11] |
W. Yao, A. Wang, Y. Nie, Z. Lv, S. Nie, C. Huang, et al., Study on the recognition of coal miners' unsafe behavior and status in the hoist cage based on machine vision, Sensors, 23 (2023), 8794. https://doi.org/10.3390/s23218794 doi: 10.3390/s23218794
![]() |
[12] |
T. Kong, W. Fang, P. E. D. Love, H. Luo, S. Xu, H. Li, Computer vision and long short-term memory: Learning to predict unsafe behaviour in construction, Adv. Eng. Inform., 50 (2021), 101400. https://doi.org/10.1016/j.aei.2021.101400 doi: 10.1016/j.aei.2021.101400
![]() |
[13] |
M. Casini, Extended reality for smart building operation and maintenance: A review, Energies, 15 (2022), 3785. https://doi.org/10.3390/en15103785 doi: 10.3390/en15103785
![]() |
[14] | R. D'Souza, IoT and the future of elevator maintenance business, Master Thesis, Technische Universität Wien, 2022. https://doi.org/10.34726/hss.2022.103532 |
[15] | X. P. Zhang, J. H. Ji, L. Wang, Z. He, S. Liu, A review of video-based human abnormal behavior recognition and detection methods, Control Decis., 37 (2022), 14−27. |
[16] |
A. F. Bobick, J. W. Davis, The recognition of human movement using temporal templates, IEEE T. Pattern Anal., 23 (2001), 257−267. https://doi.org/10.1109/34.910878 doi: 10.1109/34.910878
![]() |
[17] |
H. Wang, A. Kläser, C. Schmid, C. L. Liu, Dense trajectories and motion boundary descriptors for action recognition, Int. J. Comput. Vis., 103 (2013), 60−79. https://doi.org/10.1007/s11263-012-0594-8 doi: 10.1007/s11263-012-0594-8
![]() |
[18] |
L. Xu, C. Gong, J. Yang, Q. Wu, L. Yao, Violent video detection based on MoSIFT feature and sparse coding, IEEE, 2014, 3538−3542. https://doi.org/10.1109/ICASSP.2014.6854259 doi: 10.1109/ICASSP.2014.6854259
![]() |
[19] | H. Fujiyoshi, A. J. Lipton, T. Kanade, Real-time human motion analysis by image skeletonization, IEICE T. Inf. Syst., 87 (2004), 113−120. |
[20] |
M. S. Alzahrani, S. K. Jarraya, H. Ben-Abdallah, M. S. Ali, Comprehensive evaluation of skeleton features-based fall detection from Microsoft Kinect v2, Signal, Image Video P., 13 (2019), 1431−1439. https://doi.org/10.1007/s11760-019-01490-9 doi: 10.1007/s11760-019-01490-9
![]() |
[21] |
Z. Liao, H. Hu, J. Zhang, C. Yin, Residual attention unit for action recognition, Comput. Vis. Image Und., 189 (2019), 102821. https://doi.org/10.1016/j.cviu.2019.102821 doi: 10.1016/j.cviu.2019.102821
![]() |
[22] |
C. Feichtenhofer, A. Pinz, A. Zisserman, Convolutional two-stream network fusion for video action recognition, Proc. IEEE Conf. Comput. Vis. Pattern Recogn., 2016, 1933−1941. https://doi.org/10.1109/CVPR.2016.213 doi: 10.1109/CVPR.2016.213
![]() |
[23] |
S. Sudhakaran, O. Lanz, Learning to detect violent videos using convolutional long short-term memory, IEEE, 2017, 1−6. https://doi.org/10.1109/AVSS.2017.8078468 doi: 10.1109/AVSS.2017.8078468
![]() |
[24] | C. Y. Wang, H. Y. M. Liao, Y. H. Wu, P. Y. Chen, J. W. Hsieh, I. H. Yeh, CSPNet: A new backbone that can enhance learning capability of CNN, Montreal, BC, Canada, 2020, 390−391. |
[25] |
X. Hu, D. Kong, X. Liu, J. Zhang, D. Zhang, FM-STDNet: High-speed detector for fast-moving small targets based on deep first-order network architecture, Electronics, 12 (2023), 1−15. https://doi.org/10.3390/electronics12081829 doi: 10.3390/electronics12081829
![]() |
[26] |
S. Wang, Z. Miao, Anomaly detection in crowd scene, IEEE, 2010, 1220−1223. https://doi.org/10.1109/ICOSP.2010.5655356 doi: 10.1109/ICOSP.2010.5655356
![]() |
[27] |
R. Mehran, A. Oyama, M. Shah, Abnormal crowd behavior detection using social force model, IEEE, 2009, 935−942. https://doi.org/10.1109/CVPR.2009.5206641 doi: 10.1109/CVPR.2009.5206641
![]() |
[28] |
J. Kim, K. Grauman, Observe locally, infer globally: A space-time MRF for detecting abnormal activities with incremental updates, IEEE, 2009, 2921−2928. https://doi.org/10.1109/CVPR.2009.5206569 doi: 10.1109/CVPR.2009.5206569
![]() |
[29] |
M. Hasan, J. Choi, J. Neumann, A. K. Roy-Chowdhury, L. S. Davis, Learning temporal regularity in video sequences, Proc. IEEE Conf. Comput. Vis. Pattern Recogn., 2016, 733−742. https://doi.org/10.1109/CVPR.2016.86 doi: 10.1109/CVPR.2016.86
![]() |
[30] |
W. Luo, W. Liu, S. Gao, Remembering history with convolutional lstm for anomaly detection, IEEE, 2017, 439−444. https://doi.org/10.1109/ICME.2017.8019325 doi: 10.1109/ICME.2017.8019325
![]() |
[31] | Y. S. Chong, Y. H. Tay, Abnormal event detection in videos using spatiotemporal autoencoder, Springer, Cham, 2017, 189−196. https://doi.org/10.1007/978-3-319-59081-3_23 |
[32] |
Q. Sun, H. Liu, T. Harada, Online growing neural gas for anomaly detection in changing surveillance scenes, Pattern Recogn., 64 (2017), 187−201. https://doi.org/10.1016/j.patcog.2016.09.016 doi: 10.1016/j.patcog.2016.09.016
![]() |
[33] |
M. Ravanbakhsh, M. Nabi, E. Sangineto, L. Marcenaro, C. Regazzoni, N. Sebe, Abnormal event detection in videos using generative adversarial nets, IEEE, 2017, 1577−1581. https://doi.org/10.1109/ICIP.2017.8296547 doi: 10.1109/ICIP.2017.8296547
![]() |
[34] |
R. Hinami, T. Mei, S. Satoh, Joint detection and recounting of abnormal events by learning deep generic knowledge, Proc. IEEE Conf. Comput. Vis., 2017, 3619−3627. https://doi.org/10.1109/ICCV.2017.391 doi: 10.1109/ICCV.2017.391
![]() |
[35] | R. I. Tudor, S. Smeureanu, B. Alexe, M. Popescu, Unmasking the abnormal events in video, Proc. IEEE Conf. Comput. Vis., 2017, 2895−2903. |
[36] |
W. Luo, W. Liu, S. Gao, A revisit of sparse coding based anomaly detection in stacked RNN framework, Proc. IEEE Conf. Comput. Vis., 2017, 341−349. https://doi.org/10.1109/ICCV.2017.45 doi: 10.1109/ICCV.2017.45
![]() |
[37] |
W. Liu, W. Luo, D. Lian, S. Gao, Future frame prediction for anomaly detection—a new baseline, Proc. IEEE Conf. Comput. Vis. Pattern Recogn., 2018, 6536−6545. https://doi.org/10.1109/CVPR.2018.00684 doi: 10.1109/CVPR.2018.00684
![]() |
[38] |
D. Xu, Y. Yan, E. Ricci, N. Sebe, Detecting anomalous events in videos by learning deep representations of appearance and motion, Comput. Vis. Image Und., 156 (2017), 117−127. https://doi.org/10.1016/j.cviu.2016.10.010 doi: 10.1016/j.cviu.2016.10.010
![]() |
[39] |
M. Ravanbakhsh, M. Nabi, H. Mousavi, E. Sangineto, N. Sebe, Plug-and-play cnn for crowd motion analysis: An application in abnormal event detection, IEEE, 2018, 1689−1698. https://doi.org/10.1109/WACV.2018.00188 doi: 10.1109/WACV.2018.00188
![]() |
[40] | R. T. Ionescu, F. S. Khan, M. I. Georgescu, L. Shao, Object-centric auto-encoders and dummy anomalies for abnormal event detection in video, 2019, 7842−7851. https://doi.org/10.1109/CVPR.2019.00803 |
[41] |
C. Sun, Y. Jia, H. Song, Y. Wu, Adversarial 3d convolutional auto-encoder for abnormal event detection in videos, IEEE T. Multimedia, 23 (2020), 3292−3305. https://doi.org/10.1109/TMM.2020.3023303 doi: 10.1109/TMM.2020.3023303
![]() |
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