Nelinearno upravljanje s unutarnjim modelom za pogon s prekidačkim reluktantnim motorom bez oscilacija momenta

Based on the nonlinear internal-model control (IMC), associated with the suitable commutation strategy, a novel control solution for switched reluctance motor (SRM) is formulated and designed. The commutation strategy uses a definite critical rotor position as commutation point, which reduces the computational burden. The nonlinear IMC-based voltage control scheme for SRM extracts the simplicity of the feedback linearization control and the robustness of IMC structure, which ensures the torque ripple-free and the drive\u27s robustness in spite of the plant-model mismatch disturbances. Some important properties are presented. Simulation results show that the high-performance control for SRM has been achieved.Predloženo je i razrađeno novo rješenje za upravljanje sklopnim reluktantnim motorom (SRM) zasnovano na nelinearnom upravljanju s unutarnjim modelom (IMC) i prikladnoj strategiji komutacije. Strategija komutacije koristi definiranu kritičnu poziciju rotora kao točku komutacije što doprinosi smanjenju računskih zahtjevnosti. Shema za upravljanje naponom SRM-a zasnovana na nelinearnom IMC-u osigurava linearizaciju zatvorenog sustava i robusnost IMC strukture što rezultira ukupnom robusnošću pogona bez oscilacija momenta unatoč nepodudaranju modela smetnji sa stvarnim smetnjama. Opisana su neka važna svojstva ovoga načina upravljanja. Simulacijskim se rezultatima pokazuje visoka kvaliteta upravljanja SRM-a

Research on the Demand Forecasting Method of Sichuan Social Logistics Based on Positive Weight Combination

The macro-social logistics demand forecast is of great strategic significance to optimize the national or regional economic structure, improve the investment environment and improve the overall competitiveness of regional economy. In this study, the total amount of social logistics in Sichuan province was selected to reflect the social logistics demand, the factors influencing the social logistics demand in Sichuan province were analyzed, and eight economic indicators were summarized. This study first USES the time series prediction model (including the time response model GM (1, 1)), an exponential smoothing model, causal relation model (including multidimensional prediction model GM (1, n) and BP neural network model), to build four methods combination model, weight given solution of linear programming each forecast model, the forecasting result of combination forecast model deviation is minimal. The posterior difference test was applied to the above five models to compare the prediction results of each prediction method

Research on the Approach and Strategy of Traditional Logistics Enterprise Transformation Under the Context of the Internet

In order to study the approach and strategy of traditional logistics enterprises to transform to green logistics enterprises under the background of the Internet. In Sichuan province, 1,203 samples were taken and analyzed by SPSS data. Finally, the influence factors of consumers’ usage intentions are obtained. Based on the influence factors, the packaging and lines are designed to ensure the recycle. At the same time, the damage detection function of relevant magnetic stripe is used as auxiliary function, collecting the data information of consumers

Integrated Application and Improvement of Selection Method of Storage Sales Industry

In recent years, in order to adapt to the rapid development of the warehouse-storage sales industry and to solve the problems of location cost and efficiency and optimization of the methods of the new retail store, we have integrated and innovated the barycenter method and grey correlation method, and analyzed the grey correlation method with the weight obtained by the comprehensive analysis. In order to achieve the optimal cost effect, we choose the optimal solution from several alternative address schemes. It is found that using the integrated method as the reference standard for the location calculation of Warehouse Logistics Enterprises under the new retail background is helpful to improve the accuracy rate, and reduce the defects and defects caused by the independent use of the various methods, and adapt to the more practical and concrete conditions of the location selection of warehouse storage enterprises. At the same time, it is also an innovative attempt to cross discrete and continuous boundaries

Learning Meta Model for Zero- and Few-shot Face Anti-spoofing

Face anti-spoofing is crucial to the security of face recognition systems. Most previous methods formulate face anti-spoofing as a supervised learning problem to detect various predefined presentation attacks, which need large scale training data to cover as many attacks as possible. However, the trained model is easy to overfit several common attacks and is still vulnerable to unseen attacks. To overcome this challenge, the detector should: 1) learn discriminative features that can generalize to unseen spoofing types from predefined presentation attacks; 2) quickly adapt to new spoofing types by learning from both the predefined attacks and a few examples of the new spoofing types. Therefore, we define face anti-spoofing as a zero- and few-shot learning problem. In this paper, we propose a novel Adaptive Inner-update Meta Face Anti-Spoofing (AIM-FAS) method to tackle this problem through meta-learning. Specifically, AIM-FAS trains a meta-learner focusing on the task of detecting unseen spoofing types by learning from predefined living and spoofing faces and a few examples of new attacks. To assess the proposed approach, we propose several benchmarks for zero- and few-shot FAS. Experiments show its superior performances on the presented benchmarks to existing methods in existing zero-shot FAS protocols.Comment: Accepted by AAAI202

Confidence Ranking for CTR Prediction

Model evolution and constant availability of data are two common phenomena in large-scale real-world machine learning applications, e.g. ads and recommendation systems. To adapt, the real-world system typically retrain with all available data and online learn with recently available data to update the models periodically with the goal of better serving performance. In this paper, we propose a novel framework, named Confidence Ranking, which designs the optimization objective as a ranking function with two different models. Our confidence ranking loss allows direct optimization of the logits output for different convex surrogate functions of metrics, e.g. AUC and Accuracy depending on the target task and dataset. Armed with our proposed methods, our experiments show that the introduction of confidence ranking loss can outperform all baselines on the CTR prediction tasks of public and industrial datasets. This framework has been deployed in the advertisement system of JD.com to serve the main traffic in the fine-rank stage.Comment: Accepted by WWW202