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A dynamic opposite learning assisted grasshopper optimization algorithm for the flexible job scheduling problem
Get PDFJob shop scheduling problem (JSP) is one of the most difficult optimization problems in manufacturing industry, and flexible job shop scheduling problem (FJSP) is an extension of the classical JSP, which further challenges the algorithm performance. In FJSP, a machine should be selected for each process from a given set, which introduces another decision element within the job path, making FJSP be more difficult than traditional JSP. In this paper, a variant of grasshopper optimization algorithm (GOA) named dynamic opposite learning assisted GOA (DOLGOA) is proposed to solve FJSP. )e recently proposed dynamic opposite learning (DOL) strategy adopts the asymmetric search space to improve the exploitation ability of the algorithm and increase the possibility of finding the global optimum. Various popular benchmarks from CEC 2014 and FJSP are used to evaluate the performance of DOLGOA. Numerical results with comparisons of other classic algorithms show that DOLGOA gets obvious improvement for solving global optimization problems and is well-performed when solving FJSP
A Co9S8 microsphere and N-doped carbon nanotube composite host material for lithium-sulfur batteries
Get PDFLithium-sulfur batteries have emerged as extraordinarily favorable energy storage devices due to their high specific capacity and energy density, safety and low cost. Unfortunately, the wide applications of lithium-sulfur batteries are hampered by several issues, such as the low electronic conductivity and slow redox kinetics, serious volumetric expansion and polysulfide “shuttle effect”. To overcome these issues, in our work, we design and synthesize a composite sulfur host material of Co9S8 microspheres and N-doped carbon nanotubes, where the metallic sulfide Co9S8 with a good conductivity enables the immobilization of the polar lithium polysulfides owing to the strong polar chemisorptive capability, and the one dimensional N-doped carbon nanotubes can provide channels for fast electron and lithium-ion transport. As the lithium polysulfides are well confined, and the redox conversions are promoted, the Co9S8@N-CNTs/S-based lithium-sulfur battery possesses a superior energy storage performance, exhibiting a large specific capacity of 1233 mAh g-1 at 0.1 C and an outstanding cyclic performance, with a low decay of 0.045% per cycle and a Coulombic efficiency of more than 99% after 1000 cycles
Abstract test case prioritization using repeated small-strength level-combination coverage
Get PDFAbstract—Abstract Test Cases (ATCs) have been widely used in practice, including in combinatorial testing and in software product line testing. When constructing a set of ATCs, due to limited testing resources in practice (for example in regression testing), Test Case Prioritization (TCP) has been proposed to improve the testing quality, aiming at ordering test cases to increase the speed with which faults are detected. One intuitive and extensively studied TCP technique for ATCs is λ-wise Level-combination Coverage based Prioritization (λLCP), a static, black-box prioritization technique that only uses the ATC information to guide the prioritization process. A challenge facing λLCP, however, is the necessity for the selection of the fixed prioritization strength λ before testing — testers need to choose an appropriate λ value before testing begins. Choosing higher λ values may improve the testing effectiveness of λLCP (for example, by finding faults faster), but may reduce the testing efficiency (by incurring additional prioritization costs). Conversely, choosing lower λ values may improve the efficiency, but may also reduce the effectiveness. In this paper, we propose a new family of λLCP techniques, Repeated Small-strength Level-combination Coverage-based Prioritization (RSLCP), that repeatedly achieves the full combination coverage at lower strengths. RSLCP maintains λLCP’s advantages of being static and black box, but avoids the challenge of prioritization strength selection. We performed an empirical study involving five different versions of each of five C programs. Compared with λLCP, and Incremental strength LCP (ILCP), our results show that RSLCP could provide a good trade-off between testing effectiveness and efficiency. Our results also show that RSLCP is more effective and efficient than two popular techniques of Similarity-based Prioritization (SP). In addition, the results of empirical studies also show that RSLCP can remain robust over multiple system releases
The algebraic geometry of perfect and sequential equilibrium: an extension
Get PDFWe extend the generic equivalence result of Blume and Zame (Econometrica 62: 783-794, 1994) to a broader context of perfectly and sequentially rational strategic behavior (including equilibrium and nonequilibrium behavior) through a unifying solution concept of "mutually acceptable course of action" (MACA) proposed by Greenberg et al. (2009). As a by-product, we show, in the affirmative, Dekel et al.'s (1999) conjecture on the generic equivalence between the sequential and perfect versions of rationalizable self-confirming equilibrium. JEL Classification: C70, C7
Forecasting stock market return with nonlinearity: a genetic programming approach
Get PDFThe issue whether return in the stock market is predictable remains ambiguous. This paper attempts to establish new return forecasting models in order to contribute on addressing this issue. In contrast to existing literatures, we first reveal that the model forecasting accuracy can be improved through better model specification without adding any new variables. Instead of having a unified return forecasting model, we argue that stock markets in different countries shall have different forecasting models. Furthermore, we adopt an evolutionary procedure called Genetic programming (GP), to develop our new models with nonlinearity. Our newly-developed forecasting models are testified to be more accurate than traditional AR-family models. More importantly, the trading strategy we propose based on our forecasting models has been verified to be highly profitable in different types of stock markets in terms of stock index futures trading
Effects of pore widening vs oxygenation on capacitance of activated carbon in aqueous sodium sulfate electrolyte
Get PDFThe commercial activated carbon has a relatively low specific capacitance in the Na2SO4 electrolyte, which hinder the development of asymmetrical supercapacitors with high voltage. Re-activation and oxidative etching methods were applied to change the pore structure of activated carbon, respectively, to study the capacitive behavior of carbon in the Na2SO4 electrolyte. The pore distributions combining with capacitive properties deduce that 0.85 nm is the threshold diameter of the ion-accessible micropores for hydrated Na+ and SO42−. The specific capacitances of both the carbon materials by re-activation and oxidative etching methods are increased by 40 %, in comparison with the commercial activated carbon. The enhanced capacitive performances of the carbon materials were mainly attributed to the increased ion-accessible specific surface area and pseudocapacitance, respectively. The oxidative etching is a more facile and economical method for practice application. Combining with MnO2 as the positive electrode, the asymmetrical supercapacitor with a high voltage of 1.8 V exhibits a maximum specific cell capacitance of 50 F g–1 and specific energy of 22.5 Wh kg–1
Supporting computer science student reading through multimodal engagement interfaces
Get PDFWhile many computer science (CS) curricula are increasingly addressing a demand for more communicative and ethical graduates, reports of CS student difficulties with nontechnical subjects, such as Professional Ethics, persist. These seem compounded for students learning through a second or foreign language. This paper explores the impact that multimodal engagement interfaces can have on content comprehension. 30 participants of varying English language ability were asked to engage with four unrelated articles under four different conditions: baseline reading (C1); guided reading (sentence-by-sentence) (C2); audio/listening only (C3); and concurrent (multi-modal) presentation of C2 & C3 (C4). After each engagement, participants were asked to complete a comprehension test on the material that they had just encountered. A subjective survey evaluating the “comfort” and “engagement quality” of each interface was also completed after each interaction. Our results paint a complex picture with the guided reading interface (C2) producing both the best performance, and the poorest subjective evaluation from participants. This result aligns with existing findings identified in the field of reading education. The results highlight how varying language levels in participants impact subjective and performance metrics, suggesting how future interfaces may better support readers, according to their language ability or intended outcomes of reading
A proactive mobile edge cache policy based on the prediction by partial matching
Get PDFThe proactive caching has been an emerging approach to cost-effectively boost the network capacity and reduce access latency. While the performance of which extremely relies on the content prediction. Therefore, in this paper, a proactive cache policy is proposed in a distributed manner considering the prediction of the content popularity and user location to minimise the latency and maximise the cache hit rate. Here, a backpropagation neural network is applied to predict the content popularity, and prediction by partial matching is chosen to predict the user location. The simulation results reveal our proposed cache policy is around 27%-60% improved in the cache hit ratio and 14%-60% reduced in the average latency, compared with the two conventional reactive policies, i.e., LFU and LRU policies
Overdetermined problems for p-Laplace and generalized Monge–Ampére equations
Get PDFWe investigate overdetermined problems for p-Laplace and generalized Monge-Amp´ere equations. By using the theory of domain derivative we find duality results and a characterization of the overdetermined boundary conditions via minimization of suitable functionals with respect to the domain
A generalized input impedance model of multiple active bridge converter
Get PDFThe electrical power distribution system (EPDS) of the more electric aircraft (MEA) is a fundamental component that needs to be efficient and resilient. The commonly considered architectures feature separate buses to achieve separation between different subsections of the EPDS. Although effective, this implies an over design, since all subsections are sized for the local worst case scenarios. In the MEA concept, multiport converters could connect the whole EPDS while guaranteeing the galvanic isolation between buses. Since multiport converters would give rise to a completely different EPDS topology, dominated by power electronics interfaces, the stability of such a system must be assessed. This article investigates the input impedance of multiple active bridge (MAB) converters when interfaced with a single dc bus and multiple resistive loads. A transfer function-based input impedance model of the MAB converter is proposed. To validate the proposed input impedance model, the verification of input impedances of a triple active bridge (TAB) converter and a quadruple active bridge (QAB) converter is carried out using both simulation and experimental results. © 2015 IEEE
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