Color Correction for Multi-view Video Using Energy Minimization of View Networks

Systems using numerous cameras are emerging in many fields due to their ease of production and reduced cost, and one of the fields where they are expected to be used more actively in the near future is in image-based rendering （IBR）. Color correction between views is necessary to use multi-view systems in IBR to make audiences feel comfortable when views are switched or when a free viewpoint video is displayed. Color correction usually involves two steps： the first is to adjust camera parameters such as gain, brightness, and aperture before capture, and the second is to modify captured videos through image processing. This paper deals with the latter, which does not need a color pattern board. The proposed method uses scale invariant feature transform （SIFT） to detect correspondences, treats RGB channels independently, calculates lookup tables with an energy-minimization approach, and corrects captured video with these tables. The experimental results reveal that this approach works well.

Variance minimization for continuous-time Markov decision processes: two approaches

This paper studies the limit average variance criterion for continuous-time Markov decision processes in Polish spaces. Based on two approaches, this paper proves not only the existence of solutions to the variance minimization optimality equation and the existence of a variance minimal policy that is canonical, but also the existence of solutions to the two variance minimization optimality inequalities and the existence of a variance minimal policy which may not be canonical. An example is given to illustrate all of our conditions.

Implementation of Radial Basis Function Artificial Neural Network into an Adaptive Equivalent Consumption Minimization Strategy for Optimized Control of a Hybrid Electric Vehicle

Continued increases in the emission of greenhouse gases by passenger vehicles have accelerated the production of hybrid electric vehicles. With this increase in production, there has been a parallel demand for continuously improving strategies of hybrid electric vehicle control. The goal of an ideal control strategy is to maximize fuel economy while minimizing emissions. Methods exist by which the globally optimal control strategy may be found. However, these methods are not applicable in real-world driving applications since these methods require a priori knowledge of the upcoming drive cycle. Real-time control strategies use the global optimal as a benchmark against which performance can be evaluated. The goal of this work is to use a previously defined strategy that has been shown to closely approximate the global optimal and implement a radial basis function (RBF) artificial neural network (ANN) that dynamically adapts the strategy based on past driving conditions. The strategy used is the Equivalent Consumption Minimization Strategy (ECMS), which uses an equivalence factor to define the control strategy and the power train component torque split. An equivalence factor that is optimal for a single drive cycle can be found offline with a priori knowledge of the drive cycle. The RBF-ANN is used to dynamically update the equivalence factor by examining a past time window of driving characteristics. A total of 30 sets of training data (drive cycles) are used to train the RBF-ANN. For the majority of drive cycles examined, the RBF-ANN implementation is shown to produce fuel economy values that are within ±2.5% of the fuel economy obtained with the optimal equivalence factor. The advantage of the RBF-ANN is that it does not require a priori drive cycle knowledge and is able to be implemented in real-time while meeting or exceeding the performance of the optimal ECMS. Recommendations are made on how the RBF-ANN could be improved to produce better results across a greater array of driving conditions.

On Cost Minimization for Cache-Enabled D2D Networks with Recommendation

To accommodate the tremendous increase of mobile data traffic,cache-enabled device-to-device(D2D)communication has been taken as a promising technique to release the heavy burden of cellular networks since popular contents can be pre-fetched at user devices and shared among subscribers.As a result,cellular traffic can be offloaded and an enhanced system performance can be attainable.However,due to the limited cache capacity of mobile devices and the heterogeneous preferences among different users,the requested contents are most likely not be proactively cached,inducing lower cache hit ratio.Recommendation system,on the other hand,is able to reshape users’request schema,mitigating the heterogeneity to some extent,and hence it can boost the gain of edge caching.In this paper,the cost minimization problem for the social-aware cache-enabled D2D networks with recommendation consideration is investigated,taking into account the constraints on the cache capacity budget and the total number of recommended files per user,in which the contents are sharing between the users that trust each other.The minimization problem is an integer non-convex and non-linear programming,which is in general NP-hard.Therewith,we propose a timeefficient joint recommendation and caching decision scheme.Extensive simulation results show that the proposed scheme converges quickly and significantly reduces the average cost when compared with various benchmark strategies.

A review of optimization methods for computation offloading in edge computing networks

Handling the massive amount of data generated by Smart Mobile Devices(SMDs)is a challenging computational problem.Edge Computing is an emerging computation paradigm that is employed to conquer this problem.It can bring computation power closer to the end devices to reduce their computation latency and energy consumption.Therefore,this paradigm increases the computational ability of SMDs by collaboration with edge servers.This is achieved by computation offloading from the mobile devices to the edge nodes or servers.However,not all applications benefit from computation offloading,which is only suitable for certain types of tasks.Task properties,SMD capability,wireless channel state,and other factors must be counted when making computation offloading decisions.Hence,optimization methods are important tools in scheduling computation offloading tasks in Edge Computing networks.In this paper,we review six types of optimization methods-they are Lyapunov optimization,convex optimization,heuristic techniques,game theory,machine learning,and others.For each type,we focus on the objective functions,application areas,types of offloading methods,evaluation methods,as well as the time complexity of the proposed algorithms.We discuss a few research problems that are still open.Our purpose for this review is to provide a concise summary that can help new researchers get started with their computation offloading researches for Edge Computing networks.

Neural Network Performance for Complex Minimization Problem

We have analyzed the important problem of contemporary high-energy physics concerning the estimation of some parameters of the observed complex phenomenon. The standard statistical method of the data analysis and minimization was confronted with the Neural Network approaches. For the Natural Neural Networks we have used brains of high school students involved in our Roland Maze Project. The excitement of active participation in real scientific work produced their astonishing performance what is described in the present work. Some preliminary results are given and discussed.

Two-stage robust power cost minimization in a natural gas compressor station

Optimal operation of a compressor station is important since it accounts for 25%to 50%of a company’s total operating budget.In short-term management of a compressor station,handling demand uncertainty is important yet challenging.Previous studies either require precise information about the distribution of uncertain parameters or greatly simplify the compressor model.We build a two-stage robust optimization framework of power cost minimization in a natural gas compressor station with nonidentical compressors.In the first stage,decision variables are the ON/OFF state of each compressor and discharge pressure.The worst-case cost of the second stage is incorporated in the first stage.Firststage decision variables feasibility is discussed and proper feasibility cuts are also proposed for the first stage.We employ a piece-wise approximation and propose accelerate methods.Our numerical results highlight two advantages of robust approach when managing uncertainty in practical settings:(1)the feasibility of first-stage decision can be increased by up to 45%,and(2)the worst-case cost can be reduced by up to 25%compared with stochastic programming models.Furthermore,our numerical experiments show that the designed accelerate algorithm has time improvements of 1518.9%on average(3785.9%at maximum).

Interference Alignment in Two-Way Relay Networks via Rank Constraints Rank Minimization

Interference alignment(IA) is one of the promising measures for the multi-user network to manage interference. The rank constraints rank minimization means that interference spans the lowest dimensional subspace and the useful signal spans all available spatial dimensions. In order to improve the performance of two-way relay network, we can use rank constrained rank minimization(RCRM) to solve the IA problem. This paper proposes left reweighted nuclear norm minimization-γalgorithm and selective coupling reweighted nuclear norm minimization algorithm to implement interference alignment in two-way relay networks. The left reweighted nuclear norm minimization-γ algorithm is based on reweighted nuclear norm minimization algorithm and has a novel γ choosing rule. The selective coupling reweighted nuclear norm minimization algorithm weighting methods choose according to singular value of interference matrixes. Simulation results show that the proposed algorithms considerably improve the sum rate performance and achieve the higher average achievable multiplexing gain in two-way relay interference networks.

Minimization of Electric Power Losses on 132 kV and 220 kV Uganda Electricity Transmission Lines

The classical minimization of power losses in transmission lines is dominated by artificial intelligence techniques, which do not guarantee global optimum amidst local minima. Revolutionary and evolutionary techniques are encumbered with sophisticated transformations, which weaken the techniques. Power loss minimization is crucial to the efficient design and operation of power transmission lines. Minimization of losses is one way to meet steady grid supply, especially at peak demand. Thus, this paper has presented a gradient technique to obtain optimal variables and values from the power loss model, which efficiently minimizes power losses by modifying the traditional power loss model that combines Ohm and Corona losses. Optimality tests showed that the unmodified model does not support the minimization of power losses on transmission lines as the Hessian matrix portrayed the maximization of power losses. However, the modified model is consistent with the gradient method of optimization, which yielded optimum variables and values from the power loss model developed in this study. The unmodified (modified) models for Bujagali-Kawanda 220 kV and Masaka West-Mbarara North 132 kV transmission lines in Uganda showed maximum power losses of 0.406 (0.391) and 0.452 (0.446) kW/km/phase respectively. These results indicate that the modified model is superior to the unmodified model in minimizing power losses in the transmission lines and should be implemented for the efficient design and operation of power transmission lines within and outside Uganda for the same transmission voltages.

Modified MMS: Minimization Approach for Model Subset Selection

Considering the recent developments in the digital environment,ensuring a higher level of security for networking systems is imperative.Many security approaches are being constantly developed to protect against evolving threats.An ensemble model for the intrusion classification system yielded promising results based on the knowledge of many prior studies.This research work aimed to create a more diverse and effective ensemble model.To this end,selected six classification models,Logistic Regression(LR),Naive Bayes(NB),K-Nearest Neighbor(KNN),Decision Tree(DT),Support Vector Machine(SVM),and Random Forest(RF)from existing study to run as independent models.Once the individual models were trained,a Correlation-Based Diversity Matrix(CDM)was created by determining their closeness.The models for the ensemble were chosen by the proposed Modified Minimization Approach for Model Subset Selection(Modified-MMS)from Lower triangular-CDM(L-CDM)as input.The proposed algorithm performance was assessed using the Network Security Laboratory—Knowledge Discovery in Databases(NSL-KDD)dataset,and several performance metrics,including accuracy,precision,recall,and F1-score.By selecting a diverse set of models,the proposed system enhances the performance of an ensemble by reducing overfitting and increasing prediction accuracy.The proposed work achieved an impressive accuracy of 99.26%,using only two classification models in an ensemble,which surpasses the performance of a larger ensemble that employs six classification models.

An Optimized Test Case Minimization Technique Using Genetic Algorithm for Regression Testing

Regression testing is a widely used approach to confirm the correct functionality of the software in incremental development.The use of test cases makes it easier to test the ripple effect of changed requirements.Rigorous testingmay help in meeting the quality criteria that is based on the conformance to the requirements as given by the intended stakeholders.However,a minimized and prioritized set of test cases may reduce the efforts and time required for testingwhile focusing on the timely delivery of the software application.In this research,a technique named Test Reduce has been presented to get a minimal set of test cases based on high priority to ensure that the web applicationmeets the required quality criteria.A new technique TestReduce is proposed with a blend of genetic algorithm to find an optimized and minimal set of test cases.The ultimate objective associated with this study is to provide a technique that may solve the minimization problem of regression test cases in the case of linked requirements.In this research,the 100-Dollar prioritization approach is used to define the priority of the new requirements.

A Note on Sharp Affine Poincaré-Sobolev Inequalities and Exact in Minimization of Zhang’s Energy on Bounded Variation and Exactness

As for the affine energy, Edir Junior and Ferreira Leite establish the existence of minimizers for particular restricted subcritical and critical variational issues on BV(Ω). Similar functionals exhibit deeper weak* topological traits including lower semicontinuity and affine compactness, and their geometry is non-coercive. Our work also proves the result that extremal functions exist for certain affine Poincaré-Sobolev inequalities.

Low-complexity iterative equalization for OTFS based on alternating minimization

To achieve robust communication in high mobility scenarios,an iterative equalization algorithm based on alternating minimization(AM)is proposed for the orthogonal time frequency space(OTFS)system.The algorithm approximates the equalization problem to a convex function optimization problem in the real-valued domain and solves the problem iteratively using the AM algorithm.In the iterative process,the complexity of the proposed algorithm is reduced further based on the study of the cyclic structure and sparse property of the OTFS channel matrix in the delay-Doppler(DD)domain.The new method for OTFS is simulated and verified in a high-speed mobile scenario and the results show that the proposed equalization algorithm has excellent bit error rate performance with low complexity.

Simulation and Experimental Design of Load Adaptive Braking System on Two Wheeler

The braking quality is considered the main execution of the adaptive control framework that impacts the vehicle safety and rides solace astoundingly notably the stopping distance.This research work aims to create a pattern and design of an electromechanically adjusted lever that multiplies the applied braking force depending on the inputs given by the sensors to reduce the stopping distance of the vehicle.It is carried out using two main parts of the two-wheeler vehicle:thefirst part deals with the detection of load acting on the vehicle and identifying the required braking force to be applied,and the second part deals with the micro-controller which activates the stepper motor for varying the mechanical leverage ratio from various loads on the vehicle using two actively movable wedges.The electromechanically operated variable braking force system is developed to actuate the braking system based on the load on the motorcycle.The MATLAB simulation and experimental work are carried out for various loading(driver and pillion)conditions on a two-wheeler.The results indicate that the proposed electronically operated braking system is more effective than the conventional braking system for various loads and vehicle speeds.Specifically,the stopping distance of the vehicle is decreased significantly by about 4.9%between the con-ventional braking system and the simulated proposed system.Further,the experi-mental results show that the stopping distance is condensed by about 4.1%.The validation between simulated and experimental results revealed a great deal with the least error percentage of about 0.8%.

Two-Parameter Block Triangular Splitting Preconditioner for Block Two-by-Two Linear Systems

This paper proposes a two-parameter block triangular splitting(TPTS)preconditioner for the general block two-by-two linear systems.The eigenvalues of the corresponding preconditioned matrix are proved to cluster around 0 or 1 under mild conditions.The limited numerical results show that the TPTS preconditioner is more efficient than the classic block-diagonal and block-triangular preconditioners when applied to the flexible generalized minimal residual(FGMRES)method.

Optimal Placement and Sizing of Distributed Generations for Power Losses Minimization Using PSO-Based Deep Learning Techniques

The integration of distributed generations (DGs) into distribution systems (DSs) is increasingly becoming a solution for compensating for isolated local energy systems (ILESs). Additionally, distributed generations are used for self-consumption with excess energy injected into centralized grids (CGs). However, the improper sizing of renewable energy systems (RESs) exposes the entire system to power losses. This work presents an optimization of a system consisting of distributed generations. Firstly, PSO algorithms evaluate the size of the entire system on the IEEE bus 14 test standard. Secondly, the size of the system is allocated using improved Particles Swarm Optimization (IPSO). The convergence speed of the objective function enables a conjecture to be made about the robustness of the proposed system. The power and voltage profile on the IEEE 14-bus standard displays a decrease in power losses and an appropriate response to energy demands (EDs), validating the proposed method.

2023年度非小细胞肺癌外科治疗进展

肺癌是当前世界各地最普遍和最致命的癌症,严重威胁人们的生命健康。外科手术切除是最早治疗肺癌的手段,也是目前肺癌最主要的治疗手段。随着医学技术的进步,外科手术方法和辅助治疗策略不断革新,这为肺癌患者带来了新的希望。因此总结肺癌外科治疗领域的最新研究进展具有重要意义。本文旨在综述2023年度非小细胞肺癌(non-small cell lung cancer,NSCLC)外科治疗的主要进展,包括手术技术的创新、微创手术、辅助分子靶向治疗及免疫治疗在NSCLC围手术期的应用等。

移植前控制营养状况和移植后微小残留病灶对多发性骨髓瘤患者自体造血干细胞移植预后的影响

目的:探讨多发性骨髓瘤患者自体造血干细胞移植前控制营养状况(CONUT)和移植后微小残留病灶对预后的影响。方法:回顾性分析2011-2020年在本院行自体造血干细胞移植的79例患者的临床资料,根据首次移植前CONUT评分将患者分为Low-CONUT组(0-4分,62例)和High-CONUT组(5-12分,17例),对比分析两组患者的临床特征、造血重建与不良反应、疗效和生存情况,同时对预后进行危险因素分析,并用时间依赖的ROC曲线验证结果。结果:High-CONUT组男性患者比例和初诊时骨髓浆细胞>30%的患者比例均较Low-CONUT组高(均P<0.05),而在造血重建与不良反应(2级以上的非血液学不良反应)方面无显著差异。Low-CONUT组移植前完全缓解率、完全缓解+非常好的部分缓解率都显著高于High-CONUT组(均P<0.05),移植后3个月后者优势仍然保持(P<0.01),但前者已无显著差异(P>0.05)。Low-CONUT组总生存期、无进展生存期均优于High-CONUT组(均P<0.05)。移植前的CONUT评分低(0-4分)、移植后6个月微小残留病阴性是患者总生存期和无进展生存期的有利因素(均P<0.05),初诊时国际骨髓瘤工作组预后危险分层为高危和移植前乳酸脱氢酶>250 U/L仅是无进展生存期的危险因素(均P<0.05)。时间相关的ROC曲线分析提示,CONUT评分和移植后6个月微小残留病灶状态可单独或联合预测1和2年总生存期、无进展生存期,且联合预测效果更好。结论:结合移植前CONUT评分和移植后微小残留病灶状态可以较好地预测多发性骨髓瘤患者的预后。

麦默通微创旋切术与环乳晕微切口术分别联合水动力吸脂治疗男性乳腺发育症的疗效对比

目的:探究麦默通微创旋切手术与环乳晕微切口手术分别联合水动力吸脂治疗男性乳腺发育症的临床疗效及美学效果。方法:选取2021年10月-2022年7月笔者科室收治的80例Ⅱa级男性乳腺发育症患者,按随机数字表法将患者分为研究组和对照组各40例。研究组采用麦默通微创旋切手术联合水动力吸脂治疗,对照组采用环乳晕微切口手术联合水动力吸脂治疗。统计比较两组手术相关指标、临床疗效、并发症、复发情况及患者满意度。结果:两组住院时间和术中出血量比较差异均无统计学意义(P>0.05);研究组切口长度、手术时间及伤口愈合时间短于对照组(P<0.05),但医疗费用高于对照组(P<0.05)。两组均未出现复发和乳头及皮肤坏死的情况;研究组并发症发生率为20.00%,低于对照组的42.50%(P<0.05)。研究组治疗总有效率为97.50%,高于对照组的85.00%(P<0.05)。研究组患者对胸部平坦度、两侧对称度、切口瘢痕增生情况、皮肤及乳头乳晕感觉的满意度评分均高于对照组(P<0.05)。结论:与环乳晕微切口手术联合水动力吸脂相比,麦默通微创旋切手术联合水动力吸脂治疗男性乳腺发育症临床疗效更好,术后发生并发症概率更低,且术后胸部美学效果更理想、患者满意度更高,但医疗费用也更高。

胸腹腔镜微创手术联合食管-胃三层包埋吻合在食管癌快速康复中的应用

目的:探究胸腹腔镜微创手术联合食管-胃三层包埋吻合在食管癌快速康复中的应用效果。方法:选取2019年2月—2021年2月在扬中市人民医院接受治疗的68例食管癌患者作为研究对象,按随机数表法将患者分为对照组和观察组,各34例。对照组采用胸腔镜食管癌根治术联合管型吻合器行端侧吻合加间断缝合包埋,观察组采用胸腹腔镜微创手术联合食管-胃三层包埋吻合。比较两组手术总时间、胸腹部操作时间、颈部操作时间、术后首次下床时间、术后首次排气时间、术后首次排便时间、术后住院时间及术后并发症发生情况。结果:观察组手术总时间、胸腹部操作时间、颈部操作时间、术后首次下床时间、术后首次排气时间、术后首次排便时间、术后住院时间短于对照组,术后吻合口瘘、肺部感染、心律失常、胃排空延迟、术后鼻咽不适感发生率低于对照组,差异有统计学意义(P<0.05)。结论:胸腹腔镜微创手术联合食管-胃三层包埋吻合在食管癌快速康复中的应用效果较好,手术用时少,术后恢复快,并发症少,利于患者的术后康复。