Multi-label MRF Optimization via a Least Squares s - t Cut
Summary (3 min read)
1. Introduction
- A solid oxide fuel cell has an advantage of high power generation efficiency even in systems with a relatively small capacity.
- Steam is electrochemically decomposed into hydrogen and oxygen while consuming electrical energy.
- They observed a decrease in the discharge capacity but nearly 40% of the storage metal remained active even after 10,000 cycles.
- Because this new battery is still its early stage of the development, the authors assume one of the simplest configurations of the SOIAB systems to clarify the most fundamental characteristics of the system.
- The analyzed system is based on 0-D models of the constituent components such as the SOEC, redox metal reactor and heat exchangers introduced in their previous study [35].
2. Numerical modeling
- 1. Outline of the system Figure 1 (a) schematically shows one of the simplest configurations of the SOIAB and its concept.
- The authors need to introduce two blowers in this configuration.
- The SOEC functions in an electrolysis mode.
- (1) O2- migrates in the electrolyte to the air electrode, where oxygen is released to the air flow.
- Fe3O4 in the Fe box is reduced by hydrogen and the resultant steam-rich mixture gas is recirculated to the fuel electrode for further electrolysis by the fuel-blower.
2.2. Assumptions and conditions
- Because the simulation method is basically the same as the one the authors developed in their previous study, it is only briefly explained in this section.
- Effective thermal insulation will be important in order to realize the system at an allowable size and cost.
- The operating temperatures of the SOEC and the Fe box are assumed to be the same for simplicity and set at 600 oC.
- The average current densities during charge/discharge processes are 50 mA/cm2 and 100 mA/cm2, respectively.
- The effects of the operation conditions are discussed in sections 3.2- 3.4.
2.3. System round-trip efficiency
- Considering the thermal energy input to the system, the authors define the system round-trip efficiency, η, as DCC D QQP Pη ++ = . (9) where PC and PD are the input and output electricity.
- QC and QD are the heat inputs to the system during the charge and discharge processes, respectively.
- In the calculation of the system round-trip efficiency, QC and QD are allowed to take non-negative values.
- If there is excess heat generation in the system, the authors assume that the excess heat is exhausted to the atmosphere and QC or QD is set to zero when calculating the system round-trip efficiency.
2.4. Generation/absorption of thermal energy and heat transfer
- In Fig. 1, the main heat input/output during the operations are shown with the red, blue and yellow arrows.
- The authors first explain the heat input/output assuming the simple system shown in Fig. 1 (a) and then the system with thermal recirculation shown in Fig. 1 (b) is explained at the last of this section.
- The total heat input during the charge and discharge operation, QC and QD, respectively, are obtained by summing all the terms related to the heat input/output except for the heat generated by the losses in the SOEC.
- The reduction of Qair and Qfuel is effective for enhancing the system round-trip efficiency.
3.1. Energy budget of fundamental system with/without thermal recirculation
- To clarify fundamental characteristics of the SOIAB, the authors start with a preliminary discussion of a simple system and stepwisely take more realistic factors into account.
- If the authors consider the thermal input, however, the round-trip efficiency of this ideal system is evaluated to be 73% from Eq. (9).
- For this case, a Sankey diagram is shown in Fig. 2 (a), which shows the energy flow in the system.
- Since the thermal energy input to preheat the gases is markedly less than that shown in Fig. 2 (b), the reaction heat in the charge process becomes the main component of the heat input.
- Considering the heat-loss penalty of a small-scale regenerator, however, the use of a regenerator will be more suitable for large-scale systems.
3.2. Effects of heat exchanger effectiveness on round-trip efficiency
- Figure 3 shows the effects of the heat exchanger effectiveness on the system round-trip efficiency.
- This is reasonable because the amount of heat exchanged by HEX3 is the largest among the three heat exchangers.
- At the point marked with a square, which is “a heat-balanced point”, the input heat during the discharge process, QD, is equal to zero.
- The effectiveness of HEX1 also substantially affects the round-trip efficiency.
- The line comes to an end at a point marked “x” in this case.
3.3. Effects of gas utilization factors on round-trip efficiency and efficient and
- The effects of the gas utilization factors are shown in Fig.
- Because the air flow rate is generally higher than the fuel flow rate, the effect of the air utilization factor on the round-trip efficiency is more prominent.
- To observe the effects of the gas utilization factors within the limitation of the 0-D model simulation, the authors calculate the EMF at the SOEC exit, Eout, for each combination of fuel and air utilization factors, which is normalized by the EMF at the inlet, Ein, and is shown in Fig. 4 (b) as black contour lines.
- On the other hand, the red line in Fig. 4 (b) indicates the limit at which the fuel-blower temperature reaches its maximum allowable value of 150 oC; the system must be operated in the region to the left of the red line in the figure.
- The acute-angled region bounded by the blue and red lines in Fig. 4 (b) shows conditions for efficient and uniform operation under the constraints set in this study.
3.4. Effects of current density on round-trip efficiency
- As shown in Fig. 5, the round-trip efficiency changes with the current density during the charge or discharge operation.
- Again, note that the operation time changes with the current density according to the relationship tC iC = tD iD.
- If the current density in the charge operation is low, the required heat transfer surface area of the HEXs decreases owing to the low flow rates.
- This results in an insufficient heat recovery for the preheating of air during the discharge operation.
- The demand for additional heat input during the discharge operation increases, lowering the round-trip efficiency.
4. Conclusions
- A system simulation model of an SOIAB system was developed to investigate the fundamental characteristics during the charge and discharge operations.
- The system round-trip efficiency can be recovered by introducing heat exchangers to circulate thermal energy from the exhaust gases.
- (2) The effects of the heat exchanger effectiveness, gas utilization and current density were examined.
- The system round-trip efficiency considerably drops under these operation conditions.
- This constraint gives a lower limit for the air utilization factor.
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Frequently Asked Questions (16)
Q2. What future works have the authors mentioned in the paper "Multi-label mrf optimization via a least squares s − t cut" ?
More elaborate analysis of the algorithm ( e. g. error bounds, value of the minimized energy, computational complexity, running times ) and comparison with state-of-the-art approaches on standard benchmarks is left for future work.
Q3. What is the LS error in edge weights?
The LS error in edge weights induces error in the s − t cut or binary labeling, which is decoded into a suboptimal solution to the multi-label problem.
Q4. What is the way to label a graph?
Many visual computing tasks can be formulated as graph labeling problems, e.g. segmentation and stereo-reconstruction [1], in which one out of k labels is assigned to each graph vertex.
Q5. What is the simplest way to decode labels?
The authors perform a single (non-iterative and initialization-independent) s − t cut to obtain a “Gray” binary encoding, which is then unambiguously decoded into the k labels.
Q6. how much is a severing edge in g2?
the cost of severing intra-links in G2 to assign li to vertex vi in G isDintrai (li) =b∑m=1b∑n=m+1(lim ⊕ lin) wvim,vin (3)where ⊕ denotes binary XOR.
Q7. what is the solution to the original multi-label problem?
every sequence of b binary labels (vij)bj=1 is decoded to a decimal label li ∈ Lk = {l0, l1, ..., lk−1}, ∀vi ∈ V , i.e. the solution to the original multi-label MRF problem.
Q8. What is the noise level of the edge weights?
The authors then construct GLSE , a noisy version of G, by adding uniformly distributed noise with support [0, noise level] to the edge weights.
Q9. What is the purpose of this paper?
the authors are exploring the use of non-negative least squares (e.g. Chapter 23 in [16]) to guarantee non-negative edge weights as well as quantifying the benefits of the Gray encoding.
Q10. what is the way to label a graph?
The Markov random field (MRF) formulation captures this desired label interaction via an energy ξ(l) to be minimized with respect to the vertex labels l.ξ(l) = ∑vi∈V Di(li) + λ∑(vi,vj)∈E Vij(li, lj , di, dj) (1)where Di(li) penalizes labeling vi with li, and Vij , aka prior, penalizes assigning labels (li, lj) to neighboring vertices1.
Q11. what is the simplest solution to the multi-label problem?
If Vij(li, lj , di, dj) = Vij(di, dj), i.e. label-independent, the authors can simply ignore the outcome of li ⊕ lj by setting it to a constant.
Q12. What is the way to minimize the LS error in a linear system of equations?
The calculated edge weights are optimal in the sense that they minimize the least squares (LS) error when solving a linear system of equations capturing the original MRF penalties.
Q13. what is the cost of severing a tlink in g2?
(4)The interaction penalty Vij(li, lj, di, dj) for assigning li to vi and lj to neighboring vj in G must equal the cost of assigning a sequence of binary labels (lim)bm=1 to (vim)bm=1 and (ljn) b n=1 to (vin) b n=1 in G2.
Q14. what is the cost of severing t-links in g2?
the cost of severing t-links in G2 to assign li to vertex vi in G is calculated asDtlinksi (li) =b∑j=1lijwvij ,s + l̄ijwvij ,t (2)where l̄ij denotes the unary complement (NOT) of lij .
Q15. how many pairs of labels can be substituted?
For b = 2, (5) simplifies toVij(li, lj , di, dj) = (li1 ⊕ lj1)wvi1,vj1 + (li1 ⊕ lj2)wvi1,vj2+ (li2 ⊕ lj1)wvi2,vj1 + (li2 ⊕ lj2)wvi2,vj2 (15)The authors can now substitute all possible 2b2b = 22b = 16 combinations of the pairs of interacting labels (li, lj)∈{l0, l1, l2, l3}×{l0, l1, l2, l3}, or equivalently, ((li)2, (lj)2) ∈ {00, 01, 10, 11}× {00, 01, 10, 11}.
Q16. What is the solution to the multi-label problem?
in the general case when Vij depends on the labels li and lj of the neighboring vertices vi and vj , a single edge weight is insufficient to capture such elaborate label interactions, intuitively, because wi,j needs to take on a different value for every pair of labels.