A Non-Intrusive Pressure Sensor by Detecting Multiple Longitudinal Waves

Pressure vessels are widely used in industrial fields, and some of them are safety-critical components in the system - for example, those which contain flammable or explosive material. Therefore, the pressure of these vessels becomes one of the critical measurements for operational management. In the paper, we introduce a new approach to the design of non-intrusive pressure sensors, based on ultrasonic waves. The model of this sensor is built based upon the travel-time change of the critically refracted longitudinal wave (LCR wave) and the reflected longitudinal waves with the pressure. To evaluate the model, experiments are carried out to compare the proposed model with other existing models. The results show that the proposed model can improve the accuracy compared to models based on a single wave

Synthesis of Novel Polythiophenes Having Alkylamide Substituents for Organic Solar Cells

Solar energy is one of the most important sustainable energy resources which can satisfy increasingly growing demand of power and energy and reduce carbon footprint. In order to utilize the solar energy, solar cells are feasible and promising technologies to convert sunlight into electricity. Among the various types of solar cells, organic solar cells (OSCs), also known as organic photovoltaics (OPVs), are versatile technologies, because carbon based organic materials have relatively cheap, finely tunable physical properties and solution processibility that enables the manufacture of OSCs through roll-to-roll printing processes. P3HT has been one of the most intensely studied donor semiconductors for OSCs because of its easy synthesis (low cost), good solution processability, and high hole transport property. However, its relative high HOMO level results in a low open circuit voltage (Voc) and thus low power conversion efficiency (PCE) in OSCs. In this thesis, six new π-conjugated polythiophene derivatives based on the newly designed [2,2'-bithiophene]-4,4'-dicarboxamide (BTDCA) electron-deficient building block were synthesized and characterized. Firstly, P1 and P2 comprising BTDCA with different lengths of side chain and bithiophene (BT) were synthesized to balance the solubility and interchain packing. The HOMO levels of P1 and P2 were successfully lowered from -4.9 eV for P3HT to -5.3 eV for both P1 and P2. P2 showed a higher degree of crystallinity than P1, leading to its higher hole mobility (up to 1.4 × 10-2 cm2 V-1 s-1) compared to P1 (up to 2.0 × 10-3 cm2 V-1 s-1). Preliminary tests of P1 and P2 in solar cells were conducted using the blend of P1 or P2 with PC61BM. The Voc for the blend of P3HT with PC61BM was 0.59 V, while a significantly higher Voc of up to 0.87 V was obtained with the new materials P1 and P2 as donors. The Jsc values of P2 based solar cells were low, possibly due to the large grain size resulting from the higher degree of crystallinity. Then P3 was synthesized with a smaller comonomer unit, thieno[3,2-b]thiophene (TT), to decrease the crystallinity. The hole mobility for P3 dropped to 4.5 × 10-4 cm2 V-1 s-1 due to the decreased crystallinity compared with P2. Interestingly, higher solar cell performance was achieved for P3, most likely due to the optimized crystallinity property and thin film morphology. The PCE of the solar cell with the blend of P3 and ITIC (PCE = 3.5%) was three times that of P3HT and ITIC (PCE = 1.3%). After that, thiophene (T) was used as comonomer and copolymerized with BTDCA to form P4. Unfortunately, the structure was over tuned, thus resulting in very disordered chain packing and poor OTFT and OPV performances. Hydrogen bonding was then introduced into the BTDCA building block. P5 and P6 with different length of side chains were copolymerized with BT. Both P5 and P6 showed poor solubility which could be dissolved in common organic solvents such as chloroform, toluene, chlorobenzene and dichlorobenzene while P6 could be dissolved in a more polar solvent m-cresol. The new polymer P6 showed high hole mobility (up to 2.6 × 10-2 cm2 V-1 s-1) which is two times that of P2 (up to 1.4 × 10-2 cm2 V-1 s-1), due to the self-assembling property of hydrogen bonding, which improves the intermolecular interaction thus better inter-chain connectivity and higher mobility. P6 based solar cells did not show good performance due to its poor solubility which might not form bi-continuous and interpenetrating morphology in the active layer of solar cells

Pressure measurement based on multi-waves fusion algorithm

Measuring the pressure of a pressure vessel accurately is one of fundamental requirements of the operation of many complex engineering systems. Ultrasonic technique has been proposed to be a good alteration of non-intrusive measurement. Based on the study of acoustoelastic effect and thin-shell theory, it has been identified that the travel-time changes of the critically refracted longitudinal wave (LCR wave) and other reflected longitudinal waves are all proportional to the inner pressure. Considering the information redundancy in these waves, we proposed an approach for pressure measurement by using the information fusion algorithm on multiple reflected longitudinal waves. In the paper, we discussed the fusion algorithm in details and proposed a pressure measurement model, which represents an accurate relationship between the pressure and the travel-time changes of multiple waves. Through the experiment, the analysis of data collected from experiment system showed that the pressure measurement based on the multi-wave model is notably more accurate than the one based on the single-wave model (the average relative error (ARE) can be less than 7.24% and the root-mean-square error (RMSE) can be lower than 0.3MPa)

On The I/O Complexity of Dynamic Distinct Counting

In dynamic distinct counting, we want to maintain a multi-set S of integers under insertions to answer efficiently the query: how many distinct elements are there in S? In external memory, the problem admits two standard solutions. The first one maintains $S$ in a hash structure, so that the distinct count can be incrementally updated after each insertion using O(1) expected I/Os. A query is answered for free. The second one stores S in a linked list, and thus supports an insertion in O(1/B) amortized I/Os. A query can be answered in O(N/B log_{M/B} (N/B)) I/Os by sorting, where N=|S|, B is the block size, and M is the memory size. In this paper, we show that the above two naive solutions are already optimal within a polylog factor. Specifically, for any Las Vegas structure using N^{O(1)} blocks, if its expected amortized insertion cost is o(1/log B}), then it must incur Omega(N/(B log B)) expected I/Os answering a query in the worst case, under the (realistic) condition that N is a polynomial of B. This means that the problem is repugnant to update buffering: the query cost jumps from 0 dramatically to almost linearity as soon as the insertion cost drops slightly below Omega(1)

Semisupervised hypergraph discriminant learning for dimensionality reduction of hyperspectral image.

Semisupervised learning is an effective technique to represent the intrinsic features of a hyperspectral image (HSI), which can reduce the cost to obtain the labeled information of samples. However, traditional semisupervised learning methods fail to consider multiple properties of an HSI, which has restricted the discriminant performance of feature representation. In this article, we introduce the hypergraph into semisupervised learning to reveal the complex multistructures of an HSI, and construct a semisupervised discriminant hypergraph learning (SSDHL) method by designing an intraclass hypergraph and an interclass graph with the labeled samples. SSDHL constructs an unsupervised hypergraph with the unlabeled samples. In addition, a total scatter matrix is used to measure the distribution of the labeled and unlabeled samples. Then, a low-dimensional projection function is constructed to compact the properties of the intraclass hypergraph and the unsupervised hypergraph, and simultaneously separate the characteristics of the interclass graph and the total scatter matrix. Finally, according to the objective function, we can obtain the projection matrix and the low-dimensional features. Experiments on three HSI data sets (Botswana, KSC, and PaviaU) show that the proposed method can achieve better classification results compared with a few state-of-the-art methods. The result indicates that SSDHL can simultaneously utilize the labeled and unlabeled samples to represent the homogeneous properties and restrain the heterogeneous characteristics of an HSI

ChatGPT is a Potential Zero-Shot Dependency Parser

Pre-trained language models have been widely used in dependency parsing task and have achieved significant improvements in parser performance. However, it remains an understudied question whether pre-trained language models can spontaneously exhibit the ability of dependency parsing without introducing additional parser structure in the zero-shot scenario. In this paper, we propose to explore the dependency parsing ability of large language models such as ChatGPT and conduct linguistic analysis. The experimental results demonstrate that ChatGPT is a potential zero-shot dependency parser, and the linguistic analysis also shows some unique preferences in parsing outputs.Comment: 10 page

An innovative micromechanics-based three-dimensional long-term strength criterion for fracture assessment of rock materials

Rocks may exhibit time-dependent behaviors. Long-term strength criterion significantly dominates creep failure of rocks. Rocks contain many microcracks, which lead to degrade of long-term strength. In this paper, it is assumed that there exist three-dimensional penny-shaped microcracks in rocks. The mode II stress intensity factors at tips of three-dimensional penny-shaped microcracks in Burgers viscoelastic rock matrix is derived. A novel micromechanics-based three-dimensional long-term strength criterion is established to consider the effects of time and the intermediate principal stress on creep failure of rocks. By comparison with the previous experimental data, it is found that the novel micromechanics-based three-dimensional long-term strength criterion is in good agreement with the experimental data

CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platforms

How to optimally dispatch orders to vehicles and how to tradeoff between immediate and future returns are fundamental questions for a typical ride-hailing platform. We model ride-hailing as a large-scale parallel ranking problem and study the joint decision-making task of order dispatching and fleet management in online ride-hailing platforms. This task brings unique challenges in the following four aspects. First, to facilitate a huge number of vehicles to act and learn efficiently and robustly, we treat each region cell as an agent and build a multi-agent reinforcement learning framework. Second, to coordinate the agents from different regions to achieve long-term benefits, we leverage the geographical hierarchy of the region grids to perform hierarchical reinforcement learning. Third, to deal with the heterogeneous and variant action space for joint order dispatching and fleet management, we design the action as the ranking weight vector to rank and select the specific order or the fleet management destination in a unified formulation. Fourth, to achieve the multi-scale ride-hailing platform, we conduct the decision-making process in a hierarchical way where a multi-head attention mechanism is utilized to incorporate the impacts of neighbor agents and capture the key agent in each scale. The whole novel framework is named as CoRide. Extensive experiments based on multiple cities real-world data as well as analytic synthetic data demonstrate that CoRide provides superior performance in terms of platform revenue and user experience in the task of city-wide hybrid order dispatching and fleet management over strong baselines.Comment: CIKM 201