A Hybrid Unit Commitment Approach Incorporating Modified Priority List with Charged System Search Methods

This paper presents a new hybrid approach that combines Modified Priority List (MPL) with Charged System Search (CSS), termed MPL-CSS, to solve one of the most crucial power system’s operational optimization problems, known as unit commitment (UC) scheduling. The UC scheduling problem is a mixed-integer nonlinear problem, highly-dimensional and extremely constrained. Existing meta-heuristic UC solution methods have the problems of stopping at a local optimum and slow convergence when applied to large-scale, heavily-constrained UC applications. In the first step of the proposed method, initial hourly optimum solutions of UC are obtained by Modified Priority List (MPL);however, the obtained UC solution may still be possible to be further improved. Therefore, in the second step, the CSS is utilized to achieve higher quality solutions. The UC is formulated as mixed integer linear programming to ensure the tractability of the results. The proposed method is successfully applied to a popular test system up to 100 units generators for both 24-hr and 168-hr system. Computational results show that both solution cost and execution time are superior to those of published methods.

IDENTIFICATION OF ELASTIC-PLASTIC MECHANICAL PROPERTIES FOR BIMETALLIC SHEETS BY HYBRID-INVERSE APPROACH

Analysis, evaluation and interpretation of measured signals become important components in engineering research and practice, especially for material characteristic parameters which can not be obtained directly by experimental measurements. The present paper proposes a hybrid-inverse analysis method for the identification of the nonlinear material parameters of any individual component from the mechanical responses of a global composite. The method couples experimental approach, numerical simulation with inverse search method. The experimental approach is used to provide basic data. Then parameter identification and numerical simulation are utilized to identify elasto-plastic material properties by the experimental data obtained and inverse searching algorithm. A numerical example of a stainless steel clad copper sheet is consid- ered to verify and show the applicability of the proposed hybrid-inverse method. In this example, a set of material parameters in an elasto-plastic constitutive model have been identified by using the obtained experimental data.

A phased approach to enable hybrid simulation of complex structures

Hybrid simulation has been shown to be a cost-effective approach for assessing the seismic performance of structures. In hybrid simulation,critical parts of a structure are physically tested,while the remaining portions of the system are concurrently simulated computationally,typically using a finite element model. This combination is realized through a numerical time-integration scheme,which allows for investigation of full system-level responses of a structure in a cost-effective manner. However,conducting hybrid simulation of complex structures within large-scale testing facilities presents significant challenges. For example,the chosen modeling scheme may create numerical inaccuracies or even result in unstable simulations; the displacement and force capacity of the experimental system can be exceeded; and a hybrid test may be terminated due to poor communication between modules(e.g.,loading controllers,data acquisition systems,simulation coordinator). These problems can cause the simulation to stop suddenly,and in some cases can even result in damage to the experimental specimens; the end result can be failure of the entire experiment. This study proposes a phased approach to hybrid simulation that can validate all of the hybrid simulation components and ensure the integrity largescale hybrid simulation. In this approach,a series of hybrid simulations employing numerical components and small-scale experimental components are examined to establish this preparedness for the large-scale experiment. This validation program is incorporated into an existing,mature hybrid simulation framework,which is currently utilized in the Multi-Axial Full-Scale Sub-Structuring Testing and Simulation(MUST-SIM) facility of the George E. Brown Network for Earthquake Engineering Simulation(NEES) equipment site at the University of Illinois at Urbana-Champaign. A hybrid simulation of a four-span curved bridge is presented as an example,in which three piers are experimentally controlled in a total of 18 degrees of freedom(DOFs). This simulation illustrates the effectiveness of the phased approach presented in this paper.

Hybrid Intelligent Approach for the Selection of Third-Party Reverse Logistics Provider under Uncertainty

A hybrid intelligent approach is proposed to help the decision maker to select the appropriate third-party reverse logistics provider. The following process is included: firstly,the evaluation team is established to determine the selection criteria and evaluate them by triangular fuzzy numbers; secondly,calculate the weight of criteria by the proposed hybrid algorithm integrating particle swarm optimization( PSO) and simulated annealing( SA); then, the performance evaluation for each supplier is predicted by the proposed self-feedback neural network( SFBNN) based on the historical data. A numerical example is also presented to interpret the methodology above.

Building Sustainable Score (BSS)—A Hybrid Process Approach for Sustainable Building Assessment in China

Sustainable building in China has gained attention both domestically and abroad. Despite the fast increase in sustainable assessment tools developed locally or adopted from overseas, there are still criticisms about the current situation of weak implementation and lack of comprehensive consideration. The lack of consideration of economic and social aspects or building performance on whole building life cycle all lead to departure from the true meaning of sustainable development. And lack of participation on the part of stakeholders makes it too theoretical to be carried out. This research aims to develop a model to address this problem. This research started with review of current sustainable assessment tools applied in China. As the assessment indicators have clear regional disparities, and almost no current tool considers all three pillars of environmental, economic and social in building life cycle. An industry survey was therefore designed for generation of indicators at different building stages, and personal interviews relevant to different occupation in building industry were conducted to complement the questionnaire survey. After that, the model Building Sustainable Score (BSS) was developed based on the stakeholders’ participation. Finally, the model is verified by a case study.

Transileocolic endovascular treatment by a hybrid approach for severe acute portal vein thrombosis with bowel necrosis:Two case reports

BACKGROUND Acute portal vein thrombosis(PVT)with bowel necrosis is a fatal condition with a 50%-75%mortality rate.This report describes the successful endovascular treatment(EVT)of two patients with severe PVT.CASE SUMMARY The first patient was a 22-year-old man who presented with abdominal pain lasting 3 d.The second patient was a 48-year-old man who presented with acute abdominal pain.Following contrast-enhanced computed tomography,both patients were diagnosed with massive PVT extending to the splenic and superior mesenteric veins.Hybrid treatment(simultaneous necrotic bowel resection and EVT)was performed in a hybrid operating room(OR).EVTs,including aspiration thrombectomy,catheter-directed thrombolysis(CDT),and continuous CDT,were performed via the ileocolic vein under laparotomy.The portal veins were patent 4 and 6 mo posttreatment in the 22-year-old and 48-year-old patients,respectively.CONCLUSION Hybrid necrotic bowel resection and transileocolic EVT performed in a hybrid OR is effective and safe.

Detection of Behavioral Patterns Employing a Hybrid Approach of Computational Techniques

As far as the present state is concerned in detecting the behavioral pattern of humans(subject)using morphological image processing,a considerable portion of the study has been conducted utilizing frontal vision data of human faces.The present research work had used a side vision of human-face data to develop a theoretical framework via a hybrid analytical model approach.In this example,hybridization includes an artificial neural network(ANN)with a genetic algorithm(GA).We researched the geometrical properties extracted from side-vision human-face data.An additional study was conducted to determine the ideal number of geometrical characteristics to pick while clustering.The close vicinity ofminimum distance measurements is done for these clusters,mapped for proper classification and decision process of behavioral pattern.To identify the data acquired,support vector machines and artificial neural networks are utilized.A method known as an adaptiveunidirectional associative memory(AUTAM)was used to map one side of a human face to the other side of the same subject.The behavioral pattern has been detected based on two-class problem classification,and the decision process has been done using a genetic algorithm with best-fit measurements.The developed algorithm in the present work has been tested by considering a dataset of 100 subjects and tested using standard databases like FERET,Multi-PIE,Yale Face database,RTR,CASIA,etc.The complexity measures have also been calculated under worst-case and best-case situations.

混合随机波动模型下带随机工资的DC型养老金最优投资策略

针对养老金的保值增值问题,分析具有利率风险和波动风险的DC(defined contribution)型养老金最优投资策略。假设金融市场包含一种无风险资产和一种股票,其中股票价格服从混合随机波动(Heston-Hull-White)模型。设定养老金计划成员的工资是随机的。基于终端财富预期效用最大化标准,在CARA效用函数下,通过建立相应的HJB(Hamilton-Jacob-Bellman)方程,求解出最优投资策略,并利用数值算例分析主要模型参数对最优投资策略的影响。结果表明:风险规避系数的增大会导致投资者对股票的投资比例下降,利率风险和波动风险对DC型养老金的最优投资策略有显著影响。所得结果对基金管理者具有一定的指导作用。

杂化化学方法“单分子燃料”热物性参数预估方法

杂化化学方法是当前燃烧化学反应动力学建模新方法,尤其用于表征和预测多组分、复杂、真实液态燃料的基础燃烧化学特性.该建模方法主要特征之一是将实际多组分复杂燃料集总为一个“单分子燃料”(其化学式用燃料混合物的平均分子式表示),极大简化了模型规模并提高了预测精度.然而对该类“单分子燃料”热物性参数(包括焓、熵、热容等)的准确预估及其对模型预测精度的影响尚无研究.本文采用3种预估方法,即直接替代燃料法、修正替代法和化学官能团贡献法,获得了我国航空燃料RP-3“单分子燃料”热物性参数预估数据,并讨论燃料替代组分、平均分子式等对热物性参数预估的影响,进而考察了以上方法预估的物性参数对模型预测燃料宏观燃烧特性(点火延迟时间和层流火焰燃烧速率)的影响.

Practice and Thought on Developing Hybrid Rice for Super High Yield by Exploiting Inter-subspecific Heterosis

Since the breakthrough of grain yield owing to the development of dwarf rice and three-line system hybrid rice, rice breeding for high yield hardly had showed significant progress in the next successive two decades. It was considered that utilizing heterosis between subspecific varieties (Oryza sativa L.) would be an effective approach to increase yield further. During 1987-1993, an indica-japonica hybrid Yayou 2 yielded as high as 10.5 t/ha; however, it failed to be commercialized because of seed purity problem due to non-uniform emasculation by chemical agent in seed production, and sensitivity of seed setting in Ft plants to environmental conditions. In the past decade, two inter-subspeific hybrids, Liangyoupeijiu (Peiai 64S/9311, javanica/indica) and Liangyou E32 (Peiai 64S/E32, javanica/ laponica); both of them exhibited grain yield higher than 10.5 t/ha, and were widely judged as the pioneers of super hybrid rice. Liangyoupeijiu has been successfully popularized over 4 million hectare in wide climatic areas, while Liangyou E32 made a yield record and offered a model of plant ideotype for super hybrid rice. It was considered that in combination with plant ideotype, active physiological functions, and wide-range adaptability to ecological conditions, exploitation of indica-japonica heterosis would be the key approach for super hybrid rice breeding.

跨领域推荐方法研究综述

近年来,随着信息技术的迅速发展的爆发性增长,这一爆发式增长推动了跨领域推荐系统的出现和发展。跨领域推荐系统的设计和实现面临着诸多挑战,包括数据异构性、领域知识融合等问题。因此,书写跨领域推荐方法的研究变得尤为重要。这些方法旨在有效地整合来自不同领域的数据和信息,同时保持推荐系统的高效性和准确性。为实现这一目标,研究者们提出了各种跨领域推荐方法,包括基于迁移学习方法、基于多任务学习的方法等跨领域推荐方法,文章将从处理步骤及优缺点梳理各跨领域推荐系统方法。

A Hybrid Neural Network Model for ENSO Prediction in Combination with Principal Oscillation Pattern Analyses

El Niño-Southern Oscillation(ENSO)can be currently predicted reasonably well six months and longer,but large biases and uncertainties remain in its real-time prediction.Various approaches have been taken to improve understanding of ENSO processes,and different models for ENSO predictions have been developed,including linear statistical models based on principal oscillation pattern(POP)analyses,convolutional neural networks(CNNs),and so on.Here,we develop a novel hybrid model,named as POP-Net,by combining the POP analysis procedure with CNN-long short-term memory(LSTM)algorithm to predict the Niño-3.4 sea surface temperature(SST)index.ENSO predictions are compared with each other from the corresponding three models:POP model,CNN-LSTM model,and POP-Net,respectively.The POP-based pre-processing acts to enhance ENSO-related signals of interest while filtering unrelated noise.Consequently,an improved prediction is achieved in the POP-Net relative to others.The POP-Net shows a high-correlation skill for 17-month lead time prediction(correlation coefficients exceeding 0.5)during the 1994-2020 validation period.The POP-Net also alleviates the spring predictability barrier(SPB).It is concluded that value-added artificial neural networks for improved ENSO predictions are possible by including the process-oriented analyses to enhance signal representations.

Bayesian Multidimensional Scaling for Location Awareness in Hybrid-Internet of Underwater Things

Localization of sensor nodes in the internet of underwater things(IoUT)is of considerable significance due to its various applications,such as navigation,data tagging,and detection of underwater objects.Therefore,in this paper,we propose a hybrid Bayesian multidimensional scaling(BMDS)based localization technique that can work on a fully hybrid IoUT network where the nodes can communicate using either optical,magnetic induction,and acoustic technologies.These communication technologies are already used for communication in the underwater environment;however,lacking localization solutions.Optical and magnetic induction communication achieves higher data rates for short communication.On the contrary,acoustic waves provide a low data rate for long-range underwater communication.The proposed method collectively uses optical,magnetic induction,and acoustic communication-based ranging to estimate the underwater sensor nodes’final locations.Moreover,we also analyze the proposed scheme by deriving the hybrid Cramer-Rao lower bound(H-CRLB).Simulation results provide a complete comparative analysis of the proposed method with the literature.

颈椎前路Hybrid术对脊髓型颈椎病患者颈椎矢状位参数及颈椎功能恢复的影响

目的:探讨颈椎前路杂交(Hybrid)术对脊髓型颈椎病患者颈椎矢状位参数及颈椎功能恢复的影响。方法:选取72例经手术治疗的脊髓型颈椎病患者,根据手术方式不同分为常规组(40例)和试验组(32例)。常规组采用Smith-Robinson经椎间隙减压融合术(ACDF)治疗,试验组采用ACDF联合颈椎人工间盘置换(cADR)的Hybrid术治疗。比较两组患者围手术期指标、手术前后影像学指标、日本骨科协会(JOA)评分和颈痛视觉模拟量表(VAS)评分以及术后并发症发生情况。结果:两组手术时间、术中出血量、住院时间、住院费用比较无统计学差异(P>0.05)。两组术后3、6、12个月的Cobb角高于治疗前,T 1倾斜角、颈椎整体活动度(ROM)低于治疗前,且试验组术后3、6个月的ROM低于常规组(P<0.05)。两组术后12个月颈部VAS评分低于术前,JOA评分高于术前,且试验组术后12个月JOA评分高于常规组(P<0.05)。两组术后并发症发生率比较无统计学差异(P>0.05)。结论:颈椎前路Hybrid术可以改善脊髓型颈椎病患者颈椎矢状位参数,加快患者术后颈椎功能的恢复。

A Hybrid Algorithm Based on PSO and GA for Feature Selection

One of the main problems of machine learning and data mining is to develop a basic model with a few features,to reduce the algorithms involved in classification’s computational complexity.In this paper,the collection of features has an essential importance in the classification process to be able minimize computational time,which decreases data size and increases the precision and effectiveness of specific machine learning activities.Due to its superiority to conventional optimization methods,several metaheuristics have been used to resolve FS issues.This is why hybrid metaheuristics help increase the search and convergence rate of the critical algorithms.A modern hybrid selection algorithm combining the two algorithms;the genetic algorithm(GA)and the Particle Swarm Optimization(PSO)to enhance search capabilities is developed in this paper.The efficacy of our proposed method is illustrated in a series of simulation phases,using the UCI learning array as a benchmark dataset.

NEW APPROACH TO HARMONIC RETRIEVAL IN NOISE

This paper addresses the harmonic retrieval problem in non-Gaussian ARMA noise. A hybrid ESPRIT approach using second-and third-order statistics is proposed. First, third-order statistics are used to identify the AR part of the non-Gaussian noise process, then the noisy measurements are filtered by AR polynomial, finally, the harmonic signal parameters are estimated. Simulation examples show the effectiveness and high resolution of the new approach.

Transport Properties of Novel Hybrid Cation-Exchange Membranes on the Base of MF-4SC and Halloysite Nanotubes

The diffusion permeability through new hybrid materials based on a Nafion-type membrane (MF- 4SC) and nanotubes of halloysite is investigated using the Nernst-Planck approach. A method of quantitative evaluation of physicochemical parameters (averaged and individual diffusion coefficients and averaged distribution coefficients of ion pairs in the membrane) of system “electrolyte solution—ion-exchange membrane—water”, which was proposed earlier, is further developed. The parameters of hybrid membranes on the base of MF-4SC and nanotubes of halloysite (5% wt and 8% wt) are obtained from experimental data on diffusion permeability of NaCl solutions using theoretical calculations. New model of three-layer membrane system can be used for refining calculated results with taking into account both diffusive layers. It is shown that adding of halloysite nanotubes into the membrane volume noticeably affects exchange capacity as well as structural and transport characteristics of original perfluorinated membranes. Hybrid membranes on the base of MF-4SC and halloysite nanotubes can be used in fuel cells and catalysis.

Jacobian Elliptic Function Method and Solitary Wave Solutions for Hybrid Lattice Equation

在这篇论文，我们成功地扩大了 Jacobian 椭圆形的功能扩大途径到非线性的微分差别的方程。混合格子方程被选择说明这条途径。作为后果，有混合格子方程的不同参数的 Jacobian 椭圆形的函数解决方案的 12 个家庭被获得。什么时候模量 m → 1 或 0，二倍地周期的答案分别地堕落到 solitonic 答案和三角法的功能答案。

基于线上线下混合式的“工程数学—线性代数”课程建设的探索与实践

基于线上线下混合式的“工程数学—线性代数”课程建设的探索与实践,从课程目标的设定作为起点,课程目标达成分析作为阶段性终点和下一轮开始的起点。为了达成课程目标,该课程的建设从教师队伍能力提升、教学模式优化、课程内容改进、课程资源完善、思政案例点库建设、多元化评价体系形成、课程目标达成分析7个方面进行探索与实践。

数据驱动的扭曲均值-半方差投资组合选择

运用样本平均近似的数据驱动方法研究了带概率扭曲的均值-半方差投资组合优化模型,结合经典的遗传算法(ICA)和帝国竞争算法(GA),提出了ICA-GA混合算法.利用真实市场数据,对模型进行实证分析并求解有效前沿.最后,通过比较算法程序运行时间,表明本文创新的ICA-GA混合算法融合了帝国竞争算法和遗传算法两者的优势,比它们有更好的表现.