Doktoranduszok Fóruma
Messaudi Abderrazek, György Szeidl:
Stability investigation using Green’s function . . . 1 Mohammad Zaher Akkad, Tamás Bányai:
Modeling multi-echelon city logistics system . . . 8 Jawad Alshboul, Erika Baksáné-Varga:
A survey of domain model representations in intelligent tutoring systems . . . 14 Fatimah Nadhim Ameen, Angéla Váradiné Szarka:
The effect of using artificial intelligence models in improving human health:
A review . . . 21 Drótos Dániel:
Processzor használati módok osztályozása valós idej˝u méréssel . . . 30 Jemal Ebrahim, Zsolt Lukács:
Analyses of the relative velocity field in cold roll bonding of
multilayer sheet metal . . . 36 Gégény Dávid:
Multigranuláris durva halmazok optimista approximációk esetén . . . 42 Hegyi Gábor:
ALSE Long Wire módszer szimulációja az EMC mérés-kialakítások
jöv˝obeli vizsgálataihoz . . . 48 Nasraldeen Alnor Adam Khleel, Károly Nehéz:
Overview of modern software bug prediction approaches . . . 55 László Kovács, Szilárd Szabó, Betti Bolló:
Numerical simulation of different engine valve constructions on
in-cylinder flow behaviour . . . 62 Matyi Henriett, Tamás Péter:
Digitális iker technológia alkalmazása a logisztikai folyamatok fejlesztésében . . 68 Móré Ádám, Trohák Attila:
Létesítmény üzemeltetési rendszerek hatékonyság növel˝o
lehet˝oségeinek vizsgálata . . . 76
Hasan Nazha, Szabolcs Szávai:
Kinematics and dynamics of the mathematical models of
foot and ankle-foot orthosis . . . 85 Szabó Adél Anett, Illés Béla, Bányainé Tóth Ágota:
Járattervezési feladatok és irányzatok az intelligens elosztási logisztika
megvalósítására . . . 92 Réka Trencsényi, László Czap:
Optimisation techniques in speech processing . . . 98 Várföldi Krisztián:
Ipar 4.0 technológiák a magyar KKV-knál, különös tekintettel
a felh˝oalapú rendszerek alkalmazására . . . 105
STABILITY INVESTIGATION USING GREEN’S FUNCTIONS Messaudi Abderrazek1, Gy¨orgy Szeidl2
1PhD Student,2Professor Emeritus
1,2Institute of Applied Mechanics,
Faculty of Mechanical Engineering and Informatics
1abderrazekmessoudi1995@gmail.com,
2gyorgy.szeidl@uni-miskolc.hu
ABSTRACT
The paper presents a novel solution procedure to the stability problem of heterogeneous beams fixed at its ends and supported, in addition, by an intermediate roller support.
1. INTRODUCTION
Books [1, 2] contain the definition of the Green function for two point boundary value problems governed by ordinary linear differential equations and clarify their most important properties. In addition a number of Green functions are presented in closed forms in Table IV in [1]. The concept of the Green function considering three- point boundary value problems governed by linear ordinary differential equations is given in paper [3] functions.
The buckling problem of a compressed beam is a classical one. Our main objective is to present a novel solution procedure to the stability problem of a beam fixed at its ends and supported, in addition, by an intermediate roller support. We shall call it FrF beam. The material is linearly elastic, isotropic but the material distribution can change over the cross section – this material behavior is called cross sectional inhomogeneity [4]. We present the stability equations for the selected three-point boundary value problem. These are then replaced by a Fredholm integral equation, whose kernel is the second derivative of Green function which is also given in a closed-form. Numerical solutions of the integral equation are computed and presented using the boundary element technique.
2. DIFFERENTIAL EQUATIONS
2.1. Governing equations. The considered heterogeneous FrF beam is shown in Figure1. The beam has uniform cross section throughout its length. The E-weighted center line of the beam (or center line for short) coincides with the axisxˆof the coor- dinate systemx,ˆ y,ˆ zˆ. Its origin is at the left end of the center line. It is assumed that the coordinate planexˆzˆis a symmetry plane of the beam. It is also assumed that the modulus of elasticityE satisfy the relation E(ˆy,z) =ˆ E(−y,ˆ z)ˆ over the cross section A. In this case we speak about cross sectional heterogeneity. The length of the beam isL, the position of the middle support is given byˆb.
We remark that theE-weighted first momentQyˆis zero in this coordinate system:
N
L ŵ
x z
b
Figure 1. An FrF beam
Equilibrium problems of beams with cross sectional heterogeneity – the axial force N is zero – are governed by the ordinary differential equation [4]:
d4wˆ dˆx4 = fˆz
Iey , (2)
wherew(x)ˆ is the vertical displacement of the material points on the center line,fˆz(x) is the intensity of the distributed load acting on the center line and Iey is defined by the equation
Iey = Z
A
E(ˆy,z)zˆ 2dA . (3)
In what follows we shall use dimensionless variables defined by the following rela- tions
x= ˆx/L, ξ= ˆξ/L, w= ˆw/L, y= d ˆw
dˆx = dw
dx, b= ˆb/` ,ˆ `= x L
x=L = 1, (4) where ξˆis a coordinate on the axisx. Applying dimensionless quantities to equation (2) we have
d4w
dx4 =fz, fz = L3fˆz
Iey . (5)
This equation is associated with the following boundary and continuity conditions:
Table 1. Boundary and continuity conditions Boundary conditions
w(0) = 0, w(1)(0) = 0, w(`) = 0, w(1)(`) = 0. Continuity conditions
w(b−0) = 0, w(b+ 0) = 0,
w(1)(b−0) =w(1)(b+ 0), w(2)(b−0) = w(2)(b+ 0).
If we know the Green function for the boundary value problem determined by ODE (5) and the boundary and continuity conditions presented in Table 1the solution for the dimensionless deflectionwis given by the integral
w(x) = Z `
0
G(x, ξ)fz(ξ) dξ . (6)
2.2. Stability problem. Equilibrium problems of a uniform heterogeneous beams subjected to an axial forceN are governed by the differential equation
w(4)± Nw(2)=fz, N =L2N/Iey, (7) where the axial forceN is constant (N > 0) while the sign ofN in this equation is positive if the axial force is compressive and it is negative if the force is tensile.
If the stability problem is considered the axial force is compressive andfz = 0. We have, therefore, an eigenvalue problems – the eigenvalue sought is N – determined by the differential equation
w(4)=−Nw(2) (8)
and the boundary and continuity conditions in Table1. If we write−N w(2)forfz in (6) we get
w(x) =−N Z `
0
G(x, ξ)d2w(ξ)
dξ2 dξ=−N G(x, ξ)dw(ξ) dξ
`
ξ=0
− Z `
0
∂G(x, ξ)
∂ξ
dw(ξ) dξ dξ
! ,
where
G(x, ξ)dw(ξ) dξ
` ξ=0
= 0
since the Green function should satisfy the boundary conditions. Hence w(x) = N
Z ` 0
∂G(x, ξ)
∂ξ
dw(ξ) dξ dξ . Deriving this equation with respect toxyields
dw dx =N
Z ` 0
∂2G(x, ξ)
∂x ∂ξ
dw(ξ) dξ dξ . After introducing new variables:
dw
dx =y, ∂2G(x, ξ)
∂x ∂ξ =K(x, ξ) we arrive at a homogeneous Fredholm integral equation:
y(x) =N Z `
0 K(x, ξ)y(ξ)dξ . (9)
In this way the eigenvalue problem determined by differential equation (8) and the homogeneous boundary and continuity conditions presented in Table1is reduced to
3. GREEN FUNCTION OF FrF BEAMS
The Green function of the boundary value problem given in Table1has the following structure [5]
G(x, ξ) =
G1I(x, ξ) if x, ξ∈[0, `],
G2I(x, ξ) if x∈[b, `]andξ ∈[0, `], G1II(x, ξ) if x∈[0, b]andξ∈[b, `], G2II(x, ξ) if x, ξ∈[b, `].
; (10)
where
G1I(x, ξ) =
− 1
12ξ3± 1 12ξ3
+ 3ξ2
12 ±
−3ξ2 12
x+
+ 3ξ
12`b2 −ξb2+`b2+ξ2b−3b`ξ+`ξ2
±3ξ 12
x2+ +
− 1
12`b3 3bξ3−3b2ξ2−3`ξ2b+`ξ3+`b3
± −1 12
x3, (11a)
G2I(x, ξ) = 1
4`b(`−b)ξ2(`−x)2(x−b) (ξ−b) =G1II(ξ, x), (11b)
G2II(x, ξ) = (11c)
=− 1
D1 −3`b3ξ2+b3`3+b3ξ3−3`3b2ξ+ 6b2ξ2`2−3`2bξ3+`3ξ3
± ξ3 12+ +
3 D1
(−b3ξ2+`3b2+ 2b2ξ3−3`bξ3−3bξ`3+ 3ξ2`2b+ξ2`3)± −3ξ2 12
x+
+
− 3
D1` b3ξ`−b3ξ2−`2b3+2`3b2+b2ξ3−3bξ`3+4ξ2`3−ξ`4−2`2ξ3
±3ξ 12
x2+ +
1
D1` 3bξ3−3b2ξ2−9bξ`2−4`ξ3+6ξ2`2−`b3+6b2ξ`−`4+3b`3
±−1 12
x3 (11d) and
D1 = 12 (`−b)3. (12)
4. STABILITY PROBLEM OF BEAMS WITH THREE SUPPORTS
There are at least two possibilities to find the critical load. (i) We can solve the eigenvalue problem determined by the homogeneous Fredholm integral equation (8) numerically if we apply the boundary element technique or (ii) we can set up the characteristic equations – these are nonlinear equations for the unknown critical load – which can also be solved numerically. We shall prefer the use of the boundary element technique and the numerical solution of the characteristic equation will serve as a possibility for checking the results obtained with the boundary element technique.
As regards the boundary element technique the solution steps are detailed in book [6]
– the reader is referred to Subsection 8.15.2.
0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 1.8
1.9 2.0 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3.0
/2 /2 x
b
b b L L L
Ncrit /
Figure 2. The graph of the functionp
Ncrit/π(b)
The kernel in equation (8) assumes the following form
K(x, ξ) =
K1I(x, ξ) if x, ξ ∈[0, `],
K2I(x, ξ) if x∈[b, `]andξ ∈[0, `], K1II(x, ξ) if x∈[0, b]andξ∈[b, `], K2II(x, ξ) if x, ξ ∈[b, `].
; (13a)
where
K1I(x, ξ) = ∂2G1I(x, ξ)/∂x ∂ξ, K2I(x, ξ) =∂2G2I(x, ξ)/∂x ∂ξ,
K1II(x, ξ) = ∂2G1II(x, ξ)/∂x ∂ξ, K2II(x, ξ) =∂2G2II(x, ξ)/∂x ∂ξ. (13b) Hence
K1I(x, ξ) = ξ 2 ± ξ
2+ 1
2b2` −2b2ξ+`b2+ 3bξ2−6`bξ+ 3`ξ2
±1 2
x+
1
4b3` 6b2ξ−9bξ2+ 6`bξ−3`ξ2
x2, (14a) K2I(x, ξ) =− 1
4b ξ
`(`−b)(2b−3ξ) (`−x) (2b−3x+`) , (14b) K1II(x, ξ) = −1
4b x
`(`−b)(2b−3x) (`−ξ) (2b−3ξ+`) , (14c) K2II(x, ξ) = 1
4 (`−b)3 −2b3ξ+ 6b2ξ2−9bξ2`+ 6bξ`2−3b`3+ 2ξ`3
±
−ξ 2
+
+ 1
2`(`−b)3 2b3ξ−b3`−3b2ξ2+ 3b`3+ 6ξ2`2−8ξ`3+`4 x± 1
2x+
+ 3
4`(`−b)3(`−ξ) 4ξ`−3b`−3bξ+ 2b2
x2. (14d)
The third column in Table2contains the approximations computed using the pre-
Table 2. p
Ncrit/π against b=x
x=b q
Ncrit/π q
N(x)/π 0.000 2.00000 2.00026 0.025 2.03821 2.03806 0.050 2.07788 2.07777 0.075 2.11907 2.11911 0.100 2.16181 2.16197 0.125 2.20614 2.20632 0.150 2.25208 2.25218 0.175 2.29962 2.29960 0.200 2.34871 2.34858 0.225 2.39928 2.39912 0.250 2.45114 2.45104 0.275 2.50404 2.50407 0.300 2.55756 2.55773 0.325 2.61108 2.61133 0.350 2.66371 2.66391 0.375 2.71416 2.71419 0.400 2.76076 2.76056 0.425 2.80126 2.80101 0.450 2.83306 2.83312 0.475 2.85352 2.85398 0.500 2.86060 2.86019
Table 2 is a summary of the computed results.
The first column contains the dimensionless param- eter b which identifies the location of the middle roller support. For symmetry reasons it is sufficient to consider its value in the interval[0,0.5]. The sec- ond column contains the critical value for the di- mensionless compressive force more precisely the quantityp
Ncrit/π against the discrete values ofb. We remark that a polynomial of degree five was fitted onto the point pairs taken from the first two columns: the computed discrete points are depicted by small diamonds, however, the polynomial itself is drawn by a continuous line:
pN(x)/π =
=−34.584 864x5+ 23.541 708x4−5.871 904x3+ + 1.877 290x2+ 1.468 021x+ 2.000 267. (15) Note that for b = 0 the beam behaves as if it were a fixed-fixed beam for which pN(x)/π = 2.00 is the exact value. It is obvious that the critical force reaches its maximum ifb = 0.5. It can be proved that the characteristic equation, which provides the critical load for FrF beams, has the following form:
2p(sinp(b−1) + sinp−sinbp)− 1
2p2(cos (p−2bp)−4 cosp(b−1) + 3 cosp)−
−2bp2(cosp(b−1)−cosbp) +bp3(b−1) sinp= 0, p=p Ncrit. We also solved this non-linear equation numerically. The solutions obtained are the same with five to six digits accuracy as those computed by solving the algebraic eigenvalue problem the Fredholm integral equation (9) is reduced to.
5. CONCLUDING REMARKS
Making use of the Green function that describe the mechanical behavior of FrF beams the corresponding linear stability problem is transformed into an eigenvalue problems governed by a homogeneous Fredholm integral equation:
y(x) =N Z `
0
K(x, ξ)y(ξ)dξ, K(x, ξ) = ∂2G(x, ξ)
∂x ∂ξ , y(x) = dw(x)
dx . (16) Then the eigenvalue problem (16) is replaced by an algebraic eigenvalue problem using the boundary element technique. This solution procedure is a novel one with the advantage that it is based on the use of the Green function.
References
[1] COLLATZ, L.: Eigenwertaufgaben mit Technischen Anwendungen. Russian Edi- tion in 1968. Akademische Verlagsgesellschaft Geest & Portig K.G., 1963.
[2] COLLATZ, L.: The Numerical Treatment of Differential Equations. 3rd Edition.
Springer-Verlag, Berlin-Heidelberg GMBH, 1966.
[3] SZEIDL, G. and KISS, L.: Green Functions for Three Point Boundary Value Problems with Applications to Beams.Advances in Mathematics Research. Ed.
by Albert R. RASWELL. New York: Nova Science Publisher, Inc., 2020. Chap. Chap- ter 5, pp. 121–161.
[4] BAKSA, A. and ECSEDI, I.: A note on the pure bending of nonhomogeneous prismatic bars.International Journal of Mechanical Engineering Education37.2 (2009), pp. 1108–129.
[5] MESSAUDI, A.: Green functions for some beam problems.Di´aktudom´any 2021.
Accepted for publication. University of Miskolc, 2021.
[6] SZEIDL, G. and KISS, L. P.: Mechanical Vibrations, an Introduction. Ed. by Vladimir I. BABITSKY and Jens WITTENBURG. Foundation of Engineering Mechanics. Springer Nature, Switzerland, 2005. Chap. 10. DOI: 10 . 1007 / 978-3-030-45074-8.
MODELING MULTI-ECHELON CITY LOGISTICS SYSTEM
Mohammad Zaher Akkad1, Tamás Bányai2
1PhD Candidate, 2PhD habil, Associate Professor
1,2 Institute of Logistics, Faculty of Mechanical Engineering and Informatics
1qgezaher@uni-miskolc.hu, 2alttamas@uni-miskolc.hu
ABSTRACT
City logistics area has developed to be more ramified with the several solutions especially recently with the innovations that are being developed in the transportation and supply chains applications using the Industry 4.0 technologies. Also, using e-cars and e-bikes that are spreading fast and provide a wide possibility to raise sustainability and environmental saving. Sustainability is a significant topic that is taking priority within the current world development goals. For that, it is important to evaluate and create mathematical modeling for the existing traditional solutions next to the modern optimized solutions as an important step to research, develop and test the data. Within the frame of this work, the mathematical modeling of a developed multi-echelon city logistics system is described regarding the minimization of the total length route and fuel consumption.
1. INTRODUCTION
Urban area increase together with transport numbers growth currently defines two main challenges in the area of city logistics. In the European Union (EU) in 2020, 75% of the inhabitants live in urban areas and this number is increasing by 0.5%
annually [1]. Also, road freight transport is increasing in the EU, with 0.9% as an average growth rate of since 2000, leading to a total of 1722 billion-ton kilometers in 2015, which is important because the overall road transport (freight and passenger) causes 72.9% of all transport-related greenhouse gas (GHG) emissions [2]. Also, road freight transport contributes to noise and congestion that can be summarized as disturbance, which has negative impact effects on the people near heavily used streets. The size of European road freight transport is further boosted by the increasing number of e-commerce users, which increased from 28% in 2009 up to 45% in 2016, which made the size of the orders smaller and smaller as well as the increasing number of home deliveries that have short delivery times [3]. These mentioned developments make it challenging and more difficult to supply the inhabitants with the required goods without, at the same time, deteriorating the quality of life due to high traffic next to the increasing amounts of GHG emissions, noise, and congestion. One way of dealing with these negative effects of urban freight transport is the usage of small emission-free vehicles for the required goods’
deliveries. Especially vehicles like cargo bikes, cargo tricycles, or small electric vehicles that can be used to deliver goods in densely populated city areas without contributing to GHG emissions and noise. The congestion also can be considered to be reduced because, for instance, cargo bikes usually use different parts of the road infrastructure. Another additional benefit, it is easier to supply city zones where traffic is limited, like for instance historical cities centers. Nevertheless, two negative sides of these vehicles have to be considered. First, they have a limited load capacity.
Second, the operational distance is restricted [4]. Therefore, vehicles such as cargo bikes are not efficiently be used for the total length of urban freight. However, they
can be combined with conventional types of vehicles, such as vans or city cars that can transport bigger amounts of goods over a longer distance.
On the other hand, it is not always possible or convenient in transportation to deliver the goods directly to the destination. In fact, more and more transportation systems utilize intermediary terminals where some operations take place. The different vehicles that belong to these systems stop at some of these points, and in some cases, the freight changes vehicle or even mode of transport. Moreover, some additional services, like labeling, packaging, assembling, etc, can be realized at these intermediary terminals. These systems are called multi-echelon [5] because they are composed of one or more levels or echelons. Usually, in transport optimization, these systems are decomposed into several single-echelon independent problems, and eventually, they are separately solved. Therefore, researching the multi echelon system that considers the different new options of transportation ion city logistics is important. A fundamental step of this research is to make mathematical modeling describes the suggested solutions. In the following parts of this work, multi echelon city logistics system is presented next to its described mathematical modeling.
2. PROBLEM DESCRIPTION
For conventional city logistics solutions case, the supply of pick-up, and delivery points (households, supermarkets, shops, etc.) are processed directly. However, in the optimized methodology for a multi-echelon city logistics solution, the external logistics service providers are transporting goods to/from logistics centers located outside of the urban area (city border). As a scenario for such a case, a described optimized system from previous work is used [6]. The collection and distribution of goods to/from pick-up and delivery points are processed from this intermediate storage directly by e-trucks and micro-mobility e-vehicles (Fig. 1). The whole process optimization is centralized. In this case, it means that there is strong cooperation among transportation resources and not only the fuel consumption but also the emission of various greenhouse gases can be reduced. The intelligent agent optimizes scheduling, assignment, routing layout design, and controlling tasks that focus on time, distance, energy consumption, and emission-related objective functions, while capacity, availability, suitability, time-window, energy, and service level related constraints can limit the optimal solution. In order to provide measurements for the optimization, it is important to describe the mathematical model for this case. With taking into consideration the mentioned details of this scenario, the mathematical modeling is described in the next chapter.
In this scenario, the following parameters are taken into consideration as input parameters of the optimization task regarding the city area, including locations and tasks: location of pick-up and delivery points, the weight of pick-up and delivery tasks, upper- and lower-time limits for pick-up and delivery tasks. The following input parameters are linked to the logistics center: the capacity of loading devices, warehouse capacity, location of warehouses, available resources for transportation and materials handling, specific emission, and energy consumption of resources.
Fig. 1.
Multi-echelon collection and distribution system [6]
3. DEVELOPED SYSTEM MATHEMATICAL MODELING
For the optimized system that was shown in the previous figure, the minimization of total route length and consumed fuel is discussed. The first objective function is the minimization of the total length of transportation routes
𝐿 = ∑ ∑ 𝑙 (𝑝𝑦𝑥𝑎,𝑏, 𝑝𝑦
𝑥𝑎,𝑏+1) → 𝑚𝑖𝑛
𝑏𝑚𝑎𝑥𝛼 −1
𝑏=1 𝑎𝑚𝑎𝑥
𝑎=1
, (1)
where 𝑥𝑎,𝑏 is the decision variable of the optimization problem, 𝐿 is the total length of the transportation routes within the optimization time, 𝑎 is the number of delivery trucks, 𝑏𝛼 is the number of pick-up and delivery points assigned to collection route 𝑎, 𝑥𝑎,𝑏 is the ID number of pick-up and delivery task assigned to truck 𝑎 as pick-up
or delivery task 𝑏, 𝑦𝑥𝑎,𝑏 defines the ID of pick-up or delivery point, 𝑝𝑦
𝑥𝑎,𝑏 is the position of pick-up or delivery point assigned to truck 𝑎 as pick-up or delivery task 𝑏 and 𝑙 is the length of transportation route as a function of positions of pick-up and delivery points. The second objective function is the minimization of fuel consumption
𝐶𝑒𝐹𝑈𝐸𝐿 = 𝐶𝑇𝐹𝑈𝐸𝐿(𝑙, 𝑣, 𝑐𝑎,𝑏𝐹𝑇) + 𝐶𝑀𝐻𝐹𝑈𝐸𝐿(𝑐𝑎,𝑏𝐹𝑀𝐻) → 𝑚𝑖𝑛, (2) where 𝐶𝑒𝐹𝑈𝐸𝐿 is the energy consumption of e-trucks in kWh, 𝐶𝑇𝐹𝑈𝐸𝐿 is the fuel consumption of the whole transportation process without material handling (loading and unloading), 𝑐𝑎,𝑏𝐹𝑇 is the specific fuel consumption of transportation, 𝐶𝑀𝐻𝐹𝑈𝐸𝐿 is the fuel consumption of material handling operations at the pick-up and delivery points, 𝑐𝑎,𝑏𝐹𝑀𝐻 is the specific fuel consumption regarding material handling operations and 𝑣 is the average speed of the truck.
𝐶𝑇𝐹𝑈𝐸𝐿 = ∑ ∑ 𝑙 (𝑝𝑦𝑥𝑎,𝑏, 𝑝𝑦
𝑥𝑎,𝑏+1) ∙ 𝑞𝑥𝑎,𝑏 ∙ 𝑐𝑎,𝑏𝐹𝑇(𝑞𝑥𝑎,b)
𝑏𝑚𝑎𝑥𝛼 −1
𝑏=1 𝑎𝑚𝑎𝑥
𝑎=1
, (3)
where 𝑞𝑥𝑎,𝑏 is the pick-up or delivery volume assigned to route 𝑎 as pick-up or delivery task 𝑏. The specific fuel consumption of the transportation process can be calculated as follows:
𝑐𝑎,b𝐹𝑇 = 𝑐𝑎,𝑚𝑖𝑛𝐹𝑇 +𝑐𝑎,𝑚𝑎𝑥𝐹𝑇 − 𝑐𝑎,𝑚𝑖𝑛𝐹𝑇
𝑐𝑎,𝑚𝑎𝑥𝐹𝑇 ∙ (𝑞𝑎𝑚𝑎𝑥𝑇𝑅𝐴𝑁𝑆− ∑ 𝑞𝑥𝑎,b
𝑏
𝑏=1
), (4)
where 𝑐𝑎,𝑚𝑖𝑛𝐹𝑇 and 𝑐𝑎,𝑚𝑎𝑥𝐹𝑇 are the lower and upper limit of fuel consumption of transportation depending on the weight of loading, and 𝑞𝑎𝑚𝑎𝑥𝑇𝑅𝐴𝑁𝑆 is the upper limit of the loading weight. The fuel consumption of the loading and unloading operations performed by the truck mounted crane can be given by
𝐶𝑀𝐻𝐹𝑈𝐸𝐿 = ∑𝑎𝑎=1𝑚𝑎𝑥∑𝑏𝑏=1𝑚𝑎𝑥𝑎 𝑐𝑎,𝑏𝐹𝑀𝐻(𝑞𝑥𝑎,𝑏). (5) The specific fuel consumption of material handling processes can be calculated as follows:
𝑐𝑎,b𝐹𝑀𝐻 = 𝑐𝑎,𝑚𝑖𝑛𝐹𝑀𝐻 +𝑐𝑎,𝑚𝑎𝑥𝐹𝑀𝐻 − 𝑐𝑎,𝑚𝑖𝑛𝐹𝑀𝐻
𝑐𝑎,𝑚𝑎𝑥𝐹𝑀𝐻 ∙ (𝑞𝑎𝑚𝑎𝑥𝑀𝐻 − 𝑞𝑥𝑎,b), (6) where 𝑐𝑎,𝑚𝑖𝑛𝐹𝑀𝐻 and 𝑐𝑎,𝑚𝑎𝑥𝐹𝑀𝐻 are the lower and upper limit of fuel consumption of material handling depending on the weight of loading and 𝑞𝑎𝑚𝑎𝑥𝑀𝐻 is the upper limit of the material handling weight.
Table 1.
Nomenclature used in the mathematical model
Symbol Definition
𝐿 the total length of the transportation routes 𝑎 the number of delivery trucks
𝑏𝛼 the number of pick-up and delivery points assigned to collection route 𝛼
𝑥𝛼.𝑏 the ID number of pick-up/delivery task assigned to truck 𝑎 for task 𝑏
𝑦𝑥𝛼.𝑏 the ID of pick-up or delivery point 𝑝𝑦
𝑥𝛼.𝑏
the position of pick-up or delivery point assigned to route 𝑎 for task 𝑏
𝑙 the transportation route length as a function of positions of pick- up/delivery points
𝐶𝑇𝐹𝑈𝐸𝐿 the fuel consumption of the whole transportation process without material handling (loading and unloading) 𝑐𝑎.𝑏𝐹𝑇 the specific fuel consumption of transportation
𝐶𝑀𝐻𝐹𝑈𝐸𝐿 the fuel consumption of material handling operations at the pick-up/delivery points
𝑐𝑎.𝑏𝐹𝑀𝐻 the specific fuel consumption regarding material handling operations
𝑣 the average speed of the truck
𝑞𝑥𝑎.𝑏 the pick-up or delivery volume assigned to route 𝛼 as pick-up or delivery task 𝑏
𝑐𝛼.𝑚𝑖𝑛𝐹𝑇 and 𝑐𝛼.𝑚𝑎𝑥𝐹𝑇
the lower and upper limit of fuel consumption of transportation depending on the weight of loading
𝑞𝑎𝑚𝑎𝑥𝑇𝑅𝐴𝑁𝑆 the upper limit of the loading weight 𝑐𝑎.𝑚𝑖𝑛𝐹𝑀𝐻 and
𝑐𝑎.𝑚𝑎𝑥𝐹𝑀𝐻
the lower and upper limit of fuel consumption of material handling depending on the weight of loading
𝑞𝑎𝑚𝑎𝑥𝑀𝐻 the upper limit of the material handling weight
𝐶𝑒𝐹𝑈𝐸𝐿 the energy consumption of e-trucks and micro-mobility vehicles in kWh
4. CONCLUSIONS
This work showed a mathematical modeling of an optimized system that aims to minimize the total length of the transportation routes and consumed fuel for delivery and pick up system in city logistics. The further step is to use test data and a real case study for validating this system.
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A SURVEY OF DOMAIN MODEL REPRESENTATIONS IN INTELLIGENT TUTORING SYSTEMS
Jawad Alshboul1, Erika Baksáné-Varga2
1PhD Student, 2PhD, Associate Professor
1,2Institute of Informatics, Faculty of Mechanical Engineering and Informatics
1jawad@iit.uni-miskolc.hu, 2vargae@iit.uni-miskolc.hu
ABSTRACT
This paper provides interested readers with an introduction to the field of Intelligent Tutoring Sys- tems (ITS) and to different approaches of knowledge representations in ITS. ITSs are computer software that utilizes the powerful techniques of artificial intelligence to enhance and personalize learning experience during the learning process. The main purposes of this paper are to provide a general view of the architecture and building of ITSs and to offer a general view of the domain model component of ITS regarding different approaches taken by ITSs for representing the domain knowledge.
1. INTRODUCTION
Learning is an activity that differs from one person to another in terms of pedagogi- cal diversity. Therefore, E-Learning systems should support such diverse difference [1]. E-learning is a platform which employs information and communication tech- nologies (ICT) to empower teaching and learning activities [2]. The two major as- pects of E-Learning are technological and pedagogical [3]. The technology aspect consists of infrastructures and platforms that are used to deliver learning-teaching content for their users. The pedagogical aspect deals with learning-teaching content for improving the knowledge of the learners. Intelligent Tutoring Systems (ITS) is one of the methods that is considered a type of computer-based training adopting artificial intelligence techniques in which the system utilizes a knowledge base to deliver feedback to the student as the student gets involved with the system and to offer customized learning material and feedback to learners without the need for human instructors or with minimum intervention.
2. ARCHITECTURE OF INTELLIGENT TUTORING SYSTEMS
Current Intelligent Tutoring Systems consist of four components explained by [4]
based on the research conducted by John Self in 1990 to extend the traditional three-component architecture consisting of domain, student, and tutoring. Figure 1 shows the four-component architecture. The domain model that stores knowledge to be learned, rules, curriculum, problem-solving steps, and concepts that are all struc- tured and organized in different pedagogical contexts. The student model contains the cognitive ability of the student which advances as the learning activity progress- es. The student model has several functions to implement regarding gathering data about learners, analyzing the collected data, and interpreting the progress of student learning process in terms of pedagogy. These functions help in several roles related to diagnostics, evaluation, and prediction. The Tutoring model makes decisions re-
garding tutoring strategies and intervention after receiving inputs from both domain and student models. The user interface component provides the interaction facility with learners and users following the tutoring decisions then gives access to the domain knowledge through the learning environment.
Fig. 1.
The four-component architecture [4]
3. BUILDING INTELLIGENT TUTORING SYSTEMS
Building an ITS is a challenging task since all the resources required for this pur- pose coming from several research areas. These research areas include Artificial Intelligence, Cognitive Science, Education, Human-Computer Interaction, and Software Engineering [5]. Users interested in building ITSs are generally catego- rized into two classes: users with programming skills and users without program- ming skills [5]. Consequently, there are two generalized frameworks for building Intelligent Tutoring Systems: shells and tools. The tool, authoring system, or au- thoring tool is an ITS shell combined with a user interface dedicated for users who do not have programming skills to enable them present or formalize their knowledge [6]. Authoring tools like Intelligent Tutoring System Builder (ITSB) are built to help tutors in constructing ITSs for different fields and domains. ITSB au- thoring tool is an application that contains two systems. The first system is called the teacher system where teachers add, edit, and adjust course materials and exams.
The second system is called the student system where students learn the course con- tent, do exercises, and finish exams. The system architecture of ITSB follows the normal four-component ITS architecture including the models: domain, teaching (tutoring), student, and user interfaces [7]. The shell-based approach is common in artificial intelligence within expert systems field. A shell is a platform that includes the core parts used for building expert systems and it does not focus much on the user interface. In this approach, a shell is built with a possibility to be used for a different domain knowledge based on the inferencing approaches available in the shell. The general-purpose expert system shell, E-Mycin, was built similarly based on Mycin. Drawing on the idea of shell-based approach used for E-Mycin, ITS was viewed as an expert system with general knowledge employed to facilitate decision-
braic manipulation, AMALIA, depended on a shell called KEPLER to implement domain and tutoring models [5].
4. DOMAIN MODEL IN INTELLIGENT TUTORING SYSTEMS
Every ITS has a knowledge base system for storing knowledge on what will be taught to the learners. Thus, having a suitable domain knowledge representation, for such knowledge and reasoning with it, is needed to offer more facilities to the learn- ing process. Knowledge representation and reasoning is a field of study under the umbrella of artificial intelligence (AI) devoted to encoding human knowledge and experiences about the world into a useful symbolic language. This symbolic lan- guage enables computer systems to learn from that knowledge and experiences. The general goal is to develop formalisms for providing high level description of the world that can be effectively used to build intelligent applications. There are three general options to encode knowledge in ITS domain models. First, black box model which is a knowledge representation delivering only the final results. Second, glass box model which is a knowledge representation that inspects every step by using
"if-then" rules extracted from human experts, and keep in mind that knowledge ac- quisition is time-consuming. Third, cognitive model that aims to develop a cogni- tive model of the domain knowledge that simulates the way knowledge is represent- ed in the human brain in order to make ITSs progress in the same way the human does during problem-solving. Whether the chosen model is black box, a glass box, or cognitive, choosing the suitable language to be used in representing knowledge is critical.
The focus here is on some languages that can be described as classical general- purpose languages and that are based on logic. There are three ingredients of logic:
syntax, semantics, and calculus. The syntax describes a formal language for logic.
The semantics defines the meanings of logical formulas. The calculus ingredient provides a formal description of the inferences. These languages can be summarized as production rules, semantic networks, conceptual graphs, frame-based, and ontol- ogy [8].
4.1 Production Rules
The rule-based modeling technique is based on the belief that procedural knowledge can be effectively represented using production rules in the form of “if then” state- ments [9]. A tracing model is used to anticipate all the moves which the student can use and base the correctness of the student’s actions on the desired actions. For Ex- ample, if there are three numbers, A, B, and C. Two of them have values, assume A and B, then Rule 1 is: if C = ‘?’ AND Operator = ‘+’ then C = A + B.
4.2 Semantic Networks
The basic idea of a semantic network representation is that we can store our knowledge in the form of a graph, with nodes corresponding to objects or classes of objects in the domain (concepts) and links between nodes corresponding to relations
between these objects [10]. Generally, standardized semantic networks are ex- pressed as semantic triples (RDF triple) as shown in Figure 2: exam- ple.name#Tweety RDF#instance-of example.name#Bird.
Fig. 2.
A sample semantic network 4.3 Conceptual Graphs
They are based on semantic networks but they take the semantics from first-order predicate logic (It looks like a semantic network graphical interface for first-order predicate logic). Nodes correspond to concepts and links correspond to relation- ships between these concepts [11]. For example as shown in Figure 3: Fido the dog is sleeping on a table. Every box is called a concept node, and every oval is called a relation node. In Conceptual Graph Interchange Format:
[Dog Fido] [Sleeping *x] [Table *y] (agent ?x Fido) (location ?x ?y). Brackets sur- round the information inside the concept nodes, and parentheses surround the in- formation inside the relation nodes.
Fig. 3.
A sample conceptual graph 4.4 Frame-based Representation
Frame-based representation is a developed version of semantic networks. A frame system consists of a group of frames (corresponding to nodes in semantic net- works), that are linked together by relations (corresponding to links in semantic
Fig. 4.
A sample frame-based representation 4.5 Ontology-based Representation
Ontology is a formal description of a set of concepts in a specific domain and the relationships between them. The knowledge base of any ontology consists of two different types of statements. First, Terminology Box component (TBox) which stores terminology axioms (concepts and role descriptions); for example, All Stu- dents are Persons. Second, Assertions Box component (ABox) which contains indi- viduals (concept and role instances); for example, Person(john), john is a Person, isFatherOf (james, john).
The Semantic Web community has adopted Web Ontology Language (OWL) as a formal language for authoring ontologies and it is built on Description Logics.
Description Logics offer a number of reasoning services that might be offered to Ontology applications including subsumption, classification and satisfiability. Sub- sumption seeks to check whether a concept is a subset of another. Classification seeks to check whether an instance belongs to a concept. Satisfiability seeks to check the consistency of a concept definition by inspecting whether the membership criteria holds. In description logics, there are three different types of elements: indi- viduals/constants (example: john), concepts/unary relations (example: Person), and roles/binary relations (example: isFatherOf ).
For the purpose of learning personalization, ontologies have been employed late- ly in learning systems for knowledge representation. Ontology gives access to a shared vocabulary for domain modeling in which it describes the concepts present in the domain and their properties and relationships [12]. Ontology is promising for knowledge management as it can give a standardized form and a reasoning engine for the related knowledge management tasks. On the other hand, ontology needs a support for procedural knowledge related to the domain ontology which can be of- fered by Semantic Web Rule Language (SWRL). Semantic Web Rule Language is a standard language consisting of the union of OWL and Horn Logic rules. Figure 5 shows a snapshot of a sample course ontology in Protégé.
5. CONCLUSION
Every ITS has a knowledge base system for storing knowledge on what will be taught to the learners. Thus, having a suitable domain knowledge representation, for such knowledge and reasoning with it, is needed to offer more facilities to the learn- ing process. The domain model incorporates a representation of the knowledge to be learned and a foundation for the student model. The domain model can take many different shapes, depending on the knowledge representation employed, the topic it represents, and the level of granularity. A significant portion of ITS development time is devoted on achieving such elements. The challenge is that we seek a tem- plate-based, general-purpose domain model that a domain expert can simply fill out in order to get ITS up and running as easily as possible.
Fig. 5.
A sample course ontology in protégé [12]
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[1] POLAKOWSKI, B. and AMBORSKI, K.: Ontologies for eLearning.
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[10] SHIKNABIEVA, T.: Knowledge-Based Model Representation for a Modern Digital University. Proceedings of Smart Education and e-Learning, 2020, pp. 55–65, doi: https://doi.org/10.1007/978-981-15-5584-8_5.
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THE EFFECT OF USING ARTIFICIAL INTELLIGENCE MODELS IN IMPROVING HUMAN HEALTH : A REVIEW
Fatimah Nadhim Ameen1, Angéla Váradiné Szarka2
1PhD Student, 2PhD
1Institute of Automation and Infocommunication
2Research Institute of Electronics and Information Technology
1fatema.n.ameen@gmail.com, 2elkvsza@uni-miskolc.hu
ABSTRACT
Artificial intelligence is one of the main areas of investment in human health because it is an ele- ment of personal potential. The applications of artificial intelligence related to health aim to ana- lyze the relationship between patient outcomes and methods of disease prevention and treatment.
Artificial intelligence and its applications have been developed for the patient’s prospects, diagno- sis of his condition, drug development, in addition to the development of treatment protocols. The development of artificial intelligence and big data methods contributes to providing new opportuni- ties for health, opens a new era for science and allows the analysis of large amounts of information and the practice of health management.This proposal provides an overview of the use of artificial intelligence methods in the fields of health care.
1. INTRODUCTION
Artificial intelligence (AI) is an area of computer science that explores the potential of acceptable thinking and actions using computers, where the algorithm for ad- dressing the problem is usually unknown in advance. Modern artificial intelligence is a science and technology based on computer science, biology, psychology, lan- guages, and mathematics. The Artificial Neural Network (ANN) is the most im- portant recent trend in AI. It is a mathematical model, as well as its software or hardware implementation, based on the architecture and behavior of biological neu- ral networks. This idea came from research into the processes that occur in the brain and attempts to simulate these processes. The generated models began to be em- ployed for practical reasons after the advent of learning algorithms: in forecasting issues, pattern recognition, control, and so on. Neural network training is a multi- parameter non-linear optimization issue from a mathematical standpoint. Learning is the process of determining the coefficients of connections between neurons. The neural network can both recognize and generalize complicated dependencies be- tween input and output data. This indicates that, in the event of successful training, the network will be able to deliver the correct result based on data that was not in- cluded in the training sample, as well as incomplete and/or "noisy," partially dis- torted data. The interaction of AI with people, the expansion and development of their abilities - their personal potential (PP) - is one of the most important directions in the development of AI. From the perspective of management theory, Linear Per- ceptron LP indicates the individual's sensitivity to external environmental variables as well as his own physical and mental state. Healthcare continues to be one of the most popular areas for AI investment, and it is continuously expanding. Human
