Online Robot Introspection via Wrench-based Action Grammars

Robotic failure is all too common in unstructured robot tasks. Despite well-designed controllers, robots often fail due to unexpected events. How do robots measure unexpected events? Many do not. Most robots are driven by the sense-plan act paradigm, however more recently robots are undergoing a sense-plan-act-verify paradigm. In this work, we present a principled methodology to bootstrap online robot introspection for contact tasks. In effect, we are trying to enable the robot to answer the question: what did I do? Is my behavior as expected or not? To this end, we analyze noisy wrench data and postulate that the latter inherently contains patterns that can be effectively represented by a vocabulary. The vocabulary is generated by segmenting and encoding the data. When the wrench information represents a sequence of sub-tasks, we can think of the vocabulary forming a sentence (set of words with grammar rules) for a given sub-task; allowing the latter to be uniquely represented. The grammar, which can also include unexpected events, was classified in offline and online scenarios as well as for simulated and real robot experiments. Multiclass Support Vector Machines (SVMs) were used offline, while online probabilistic SVMs were are used to give temporal confidence to the introspection result. The contribution of our work is the presentation of a generalizable online semantic scheme that enables a robot to understand its high-level state whether nominal or abnormal. It is shown to work in offline and online scenarios for a particularly challenging contact task: snap assemblies. We perform the snap assembly in one-arm simulated and real one-arm experiments and a simulated two-arm experiment. This verification mechanism can be used by high-level planners or reasoning systems to enable intelligent failure recovery or determine the next most optima manipulation skill to be used.Comment: arXiv admin note: substantial text overlap with arXiv:1609.0494

ERNIE-mmLayout: Multi-grained MultiModal Transformer for Document Understanding

Recent efforts of multimodal Transformers have improved Visually Rich Document Understanding (VrDU) tasks via incorporating visual and textual information. However, existing approaches mainly focus on fine-grained elements such as words and document image patches, making it hard for them to learn from coarse-grained elements, including natural lexical units like phrases and salient visual regions like prominent image regions. In this paper, we attach more importance to coarse-grained elements containing high-density information and consistent semantics, which are valuable for document understanding. At first, a document graph is proposed to model complex relationships among multi-grained multimodal elements, in which salient visual regions are detected by a cluster-based method. Then, a multi-grained multimodal Transformer called mmLayout is proposed to incorporate coarse-grained information into existing pre-trained fine-grained multimodal Transformers based on the graph. In mmLayout, coarse-grained information is aggregated from fine-grained, and then, after further processing, is fused back into fine-grained for final prediction. Furthermore, common sense enhancement is introduced to exploit the semantic information of natural lexical units. Experimental results on four tasks, including information extraction and document question answering, show that our method can improve the performance of multimodal Transformers based on fine-grained elements and achieve better performance with fewer parameters. Qualitative analyses show that our method can capture consistent semantics in coarse-grained elements.Comment: Accepted by ACM Multimedia 202

Genistein-Inhibited Cancer Stem Cell-Like Properties and Reduced Chemoresistance of Gastric Cancer

Key Projects of Fujian Province Technology [2010D026]; China Postdoctoral Science Foundation [2012M521279]; Medical innovations Topic in Fujian Province [2012-CXB-9]; projects of Xiamen scientific and technological plan [3502Z20124018, 3502Z20134011]Genistein, the predominant isoflavone found in soy products, has exerted its anticarcinogenic effect in many different tumor types in vitro and in vivo. Accumulating evidence in recent years has strongly indicated the existence of cancer stem cells in gastric cancer. Here, we showed that low doses of genistein (15 M), extracted from Millettia nitida Benth var hirsutissima Z Wei, inhibit tumor cell self-renewal in two types of gastric cancer cells by colony formation assay and tumor sphere formation assay. Treatment of gastric cancer cells with genistein reduced its chemoresistance to 5-Fu (fluorouracil) and ciplatin. Further results indicated that the reduced chemoresistance may be associated with the inhibition of ABCG2 expression and ERK 1/2 activity. Furthermore, genistein reduced tumor mass in the xenograft model. Together, genistein inhibited gastric cancer stem cell-like properties and reduced its chemoresistance. Our results provide a further rationale and experimental basis for using the genistein to improve treatment of patients with gastric cancer

Real-Time Energy Management Strategy of Hydrogen Fuel Cell Hybrid Electric Vehicles Based on Power Following Strategy–Fuzzy Logic Control Strategy Hybrid Control

Fuel cell hybrid electric vehicles have the advantages of zero emission, high efficiency and fast refuelling, etc. and are one of the key directions for vehicle development. The energy management problem of fuel cell hybrid electric vehicles is the key technology for power distribution. The traditional power following strategy has the advantage of a real-time operation, but the power correction is usually based only on the state of charge of a lithium battery, which causes the operating point of the fuel cell to be in the region of a low efficiency. To solve this problem, this paper proposes a hybrid power-following-fuzzy control strategy, where a fuzzy logic control strategy is used to optimise the correction module based on the power following strategy, which regulates the state of charge while correcting the output power of the fuel cell towards the efficient operating point. The results of the joint simulation with Matlab + Advisor under the Globally Harmonised Light Vehicle Test Cycle Conditions show that the proposed strategy still ensures the advantages of real-time energy management, and for the hydrogen fuel cell, the hydrogen consumption is reduced by 13.5% and 4.1% compared with the power following strategy and the fuzzy logic control strategy, and the average output power variability is reduced by 14.6% and 5.1%, respectively, which is important for improving the economy of the whole vehicle and prolonging the lifetime of fuel cell

Genistein-Inhibited Cancer Stem Cell-Like Properties and Reduced Chemoresistance of Gastric Cancer

Genistein, the predominant isoflavone found in soy products, has exerted its anticarcinogenic effect in many different tumor types in vitro and in vivo. Accumulating evidence in recent years has strongly indicated the existence of cancer stem cells in gastric cancer. Here, we showed that low doses of genistein (15 µM), extracted from Millettia nitida Benth var hirsutissima Z Wei, inhibit tumor cell self-renewal in two types of gastric cancer cells by colony formation assay and tumor sphere formation assay. Treatment of gastric cancer cells with genistein reduced its chemoresistance to 5-Fu (fluorouracil) and ciplatin. Further results indicated that the reduced chemoresistance may be associated with the inhibition of ABCG2 expression and ERK 1/2 activity. Furthermore, genistein reduced tumor mass in the xenograft model. Together, genistein inhibited gastric cancer stem cell-like properties and reduced its chemoresistance. Our results provide a further rationale and experimental basis for using the genistein to improve treatment of patients with gastric cancer

Deep Desulfurization of High-Sulfur Petroleum Coke via Alkali Catalytic Roasting Combined with Ultrasonic Oxidation

The sulfur in petroleum coke is harmful to carbon products, underscoring the importance of desulfurization for high-sulfur petroleum coke. This paper proposes a method combining alkaline catalytic roasting with ultrasonic oxidation for the deep desulfurization of high-sulfur petroleum coke. The results show that the desulfurization rate reaches 88.99% and the sulfur content is reduced to 0.83 wt.% under a coke particle size of 96–75 μm, sodium-hydroxide-to-petroleum-coke ratio of 50%, roasting temperature of 700 °C, and holding time of 2 h. The alkali-calcined petroleum coke is ultrasonically oxidized and desulfurized in peracetic acid. The results show that, under a hydrogen peroxide content of 10%, hydrogen-peroxide-(liquid)-to-petroleum-coke (solid) ratio of 20 mL/g, acetic acid content of 5 mL, ultrasonic power of 300 W, reaction temperature of 60 °C, and reaction duration of 4 h, the sulfur content is reduced to 0.15 wt.% and the total desulfurization reaches 98.01%. Through a series of characterizations, the proposed desulfurization mechanism is verified. Alkali roasting effectively removes a significant portion of sulfur in petroleum coke. However, the elimination of certain sulfur compounds, such as the more complex thiophene, presents challenges. The thiophene content is subsequently removed via ultrasonic oxidation

Machine Learning Sorting Method of Bauxite Based on SE-Enhanced Network

A fast and accurate bauxite recognition method combining an attention module and a clustering algorithm is proposed in this paper. By introducing the K-means clustering algorithm into the YOLOv4 network and embedding the SE attention module, we calculate the corresponding anchor box value, enhance the feature learning ability of the network to bauxite, automatically learn the importance of different channel features, and improve the accuracy of bauxite target detection. In the experiment, 2189 bauxite photos were taken and screened as the target detection datasets, and the targets were divided into four categories: No. 55, No. 65, No. 70, and Nos. 72–73. By selecting the category volume balanced datasets, the optimal YOLOv4 network model was obtained after training 7000 times, so that the average accuracy of bauxite sorting reached 99%, and the reasoning speed was better than 0.05 s. Realizing the high-speed and high-precision sorting of bauxite greatly improves the mining efficiency and accuracy of the bauxite industry. At the same time, the model provides key technical support for the practical application of the same type of engineering

Optimal operation of microgrid with consideration of V2G's uncertainty

Abstract The aggregation of the remaining battery capacity of electric vehicles (EVs) can be used as distributed energy storage to participate in the microgrid optimisation through vehicle‐to‐grid (V2G) technology. However, the reliability of this service to be delivered is affected by the uncertainty of EVs’ behaviour. The un‐reliable service will bring risks in the operation of microgrid when EVs’ V2G electricity is regarded as an important flexibility resource. This paper, first proposes an analytical method to quantify the reliability of EV aggregation's (EVA's) V2G electricity based on the analysis of historical charging data and compound Poisson process. Based on this model, electric vehicles aggregators can evaluate the V2G bid quantity for any time slot on day t+1 according to the reliability requirements. Secondly, an optimal operation model of microgrid with consideration of V2G's reliability is considered. Finally, a test system with photovoltaics (PVs) and EVA on top of the original IEEE 33‐node system is used to verify the effectiveness of the proposed model. The results show that with the proposed reliability evaluation method of V2G electricity, the optimal operation of the microgrid can be reached with appropriate evaluation of the capability of the EVA

Near-Field Microwave Imaging Method of Monopole Antennas Based on Nitrogen-Vacancy Centers in Diamond

Visualizing the near-field distribution of microwave field in a monopole antenna is very important for antenna design and manufacture. However, the traditional method of measuring antenna microwave near field distribution by mechanical scanning has some problems, such as long measurement time, low measurement accuracy and large system volume, which seriously limits the measurement effect of antenna microwave near field distribution. In this paper, a method of microwave near-field imaging of a monopole antenna using a nitrogen-vacancy center diamond is presented. We use the whole diamond as a probe and camera to achieve wide-field microwave imaging. Because there is no displacement structure in the system, the method has high time efficiency and good stability. Compared with the traditional measurement methods, the diamond probe has almost no effect on the measured microwave field, which realizes the accurate near-field imaging of the microwave field of the monopole antenna. This method achieves microwave near-field imaging of a monopole antenna with a diameter of 100 µm and a length of 15 mm at a field of view of 5 × 5 mm, with a spatial resolution of 3 µm and an imaging bandwidth of 2.7~3.2 GHz, and an optimal input microwave phase resolution of 0.52° at a microwave power of 0.8494 W. The results provide a new method for microwave near-field imaging and measurement of monopole antennas