Current Situation and Measures of Environment Affected by Mine Water in Hunan Limin Coal Mine

The pump drainage of mine water in Hunan Limin Coal Mine has caused various problems including huge depression cone and collapse of goaf roof, resulting in serious leakage of surface water. Therefore, a great need of measures should be conducted. In this paper, the mine water properties and the hydrogeological characteristics were studied. The results indicate that the PH value of main shaft water of Limin Well is 2.21-3.81 (average: 2.90), Fe2+ is 178-1,308 mg/L, Mn2+ is 3.93-8.65 mg/L; the PH value of auxiliary shaft water is 3.45-4.08, Fe2+ is 876-1,264 mg/L, Mn2+ is 6.27-8.71 mg/L; the PH value of Shuikoushan old pithead water is 2.33-3.91, Fe2+ is 1,059-2,207 mg/L and Mn2+ is 23.00-34.00 mg/L, which indicates that the mine water in the area is strongly acidic, and Fe2+ and Mn2+ exceed the standard seriously. Based on the characteristics of mining tunnel in the area, the wellhead of artesian water in Limin Well should be closed with shaft without pressure so as to ensure that the mine water can be discharged out in a concentrated and orderly way. The sewage treatment facilities at the main shaft of Limin Well need to be expanded, and new sewage treatment facilities should be built at Shuikoushan old pithead. Through hydrologic survey and water balance analysis, Zhoutou Reservoir can be used as a water source to construct drinking water project, which can completely solve the problem of drinking water safety in the area. The study would solve the bad situation of Limin Coal mine by providing the corresponding measures, which could provide the references for the similar mines when facing these problems

The Complexity of SORE-definability Problems

Single occurrence regular expressions (SORE) are a special kind of deterministic regular expressions, which are extensively used in the schema languages DTD and XSD for XML documents. In this paper, with motivations from the simplification of XML schemas, we consider the SORE-definability problem: Given a regular expression, decide whether it has an equivalent SORE. We investigate extensively the complexity of the SORE-definability problem: We consider both (standard) regular expressions and regular expressions with counting, and distinguish between the alphabets of size at least two and unary alphabets. In all cases, we obtain tight complexity bounds. In addition, we consider another variant of this problem, the bounded SORE-definability problem, which is to decide, given a regular expression E and a number M (encoded in unary or binary), whether there is an SORE, which is equivalent to E on the set of words of length at most M. We show that in several cases, there is an exponential decrease in the complexity when switching from the SORE-definability problem to its bounded variant

OpenFed: A Comprehensive and Versatile Open-Source Federated Learning Framework

Recent developments in Artificial Intelligence techniques have enabled their successful application across a spectrum of commercial and industrial settings. However, these techniques require large volumes of data to be aggregated in a centralized manner, forestalling their applicability to scenarios wherein the data is sensitive or the cost of data transmission is prohibitive. Federated Learning alleviates these problems by decentralizing model training, thereby removing the need for data transfer and aggregation. To advance the adoption of Federated Learning, more research and development needs to be conducted to address some important open questions. In this work, we propose OpenFed, an open-source software framework for end-to-end Federated Learning. OpenFed reduces the barrier to entry for both researchers and downstream users of Federated Learning by the targeted removal of existing pain points. For researchers, OpenFed provides a framework wherein new methods can be easily implemented and fairly evaluated against an extensive suite of benchmarks. For downstream users, OpenFed allows Federated Learning to be plug and play within different subject-matter contexts, removing the need for deep expertise in Federated Learning.Comment: 18 pages, 3 figures, 1 tabl

Deep Learning for Hybrid Beamforming with Finite Feedback in GSM Aided mmWave MIMO Systems

Hybrid beamforming is widely recognized as an important technique for millimeter wave (mmWave) multiple input multiple output (MIMO) systems. Generalized spatial modulation (GSM) is further introduced to improve the spectrum efficiency. However, most of the existing works on beamforming assume the perfect channel state information (CSI), which is unrealistic in practical systems. In this paper, joint optimization of downlink pilot training, channel estimation, CSI feedback, and hybrid beamforming is considered in GSM aided frequency division duplexing (FDD) mmWave MIMO systems. With the help of deep learning, the GSM hybrid beamformers are designed via unsupervised learning in an end-to-end way. Experiments show that the proposed multi-resolution network named GsmEFBNet can reach a better achievable rate with fewer feedback bits compared with the conventional algorithm.Comment: 4 pages, 4 figures. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notic

Quantization Adaptor for Bit-Level Deep Learning-Based Massive MIMO CSI Feedback

In massive multiple-input multiple-output (MIMO) systems, the user equipment (UE) needs to feed the channel state information (CSI) back to the base station (BS) for the following beamforming. But the large scale of antennas in massive MIMO systems causes huge feedback overhead. Deep learning (DL) based methods can compress the CSI at the UE and recover it at the BS, which reduces the feedback cost significantly. But the compressed CSI must be quantized into bit streams for transmission. In this paper, we propose an adaptor-assisted quantization strategy for bit-level DL-based CSI feedback. First, we design a network-aided adaptor and an advanced training scheme to adaptively improve the quantization and reconstruction accuracy. Moreover, for easy practical employment, we introduce the expert knowledge of data distribution and propose a pluggable and cost-free adaptor scheme. Experiments show that compared with the state-of-the-art feedback quantization method, this adaptor-aided quantization strategy can achieve better quantization accuracy and reconstruction performance with less or no additional cost. The open-source codes are available at https://github.com/zhang-xd18/QCRNet.Comment: 9 pages, 8 figures, 5 tables. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notic

Cueing roles of new energy vehicle manufacturers’ technical capability and reputation in influencing purchase intention in China

Promoting new energy vehicle (NEV) is one of the main ways to save energy and reduce transport emissions, China has provided substantial subsidies for this since 2009. With the impending end of the subsidy policy ending in 2022, NEV manufacturers need to strengthen their competitiveness to continuously attract customers. Under the framework of cue utilization theory, this study takes NEV manufacturers’ technical capability as an intrinsic cue and reputation as an extrinsic cue to explore the mechanism in which two cues stimulate customers’ perceptions of travel quality and brand value, and subsequently motivate purchase intention. Based on a sample of 207 respondents from China, proposed hypotheses have been tested using Likert scale questionnaires through SPSS and AMOS. Structural equation modeling techniques were used to analyze the measurement scales and variable relationships. The results show that manufacturers’ reputation is more influential on both perceived travel quality and perceived brand value than technical capability; Technological turbulence plays a moderating role in the influence between perceived brand value and purchase intention. This article provides references for deepening related theories, and pragmatic insights for manufacturer strategic response and government policy making

Adaptive Virtual Impedance Droop Control Based on Consensus Control of Reactive Current

It is difficult to achieve accurate distribution of reactive power based on conventional droop control due to the line impedance mismatch in an islanded microgrid. An adaptive virtual impendence method based on consensus control of reactive current is proposed in this paper. A distributed control structure without the central controller has been established. In this structure, each distributed generation unit (DG) is an independent agent, one-way communication is used between the adjacent DGs, and the reactive power sharing is equivalent to a problem of reactive power current consensus. It has been proven that the system is asymptotically stable under the proposed control strategy. When the adjacent DG’s reactive power is not proportionally distributed, the current weight error term will generate a virtual impedance correction term through the proportional-integral controller based on the reactive current consensus control strategy, thus introducing adaptive virtual impedance to eliminate mismatches in output impedance between DGs. Reactive power auto-proportional distribution can be achieved without knowing the line impedance. At the same time, the power control loop is simplified and the virtual impedance compensation angle is employed to compensate the decreased reference voltage magnitude and varied phase angle due to the introduction of the virtual impedance, so the stability of the system can be improved. Finally, the correctness and effectiveness of the proposed strategy are verified by modeling analysis and microgrid simulations. Abstract It is difficult to achieve accurate distribution of reactive power based on conventional droop control due to the line impedance mismatch in an islanded microgrid. An adaptive virtual impendence method based on consensus control of reactive current is proposed in this paper. A distributed control structure without the central controller has been established. In this structure, each distributed generation unit (DG) is an independent agent, one-way communication is used between the adjacent DGs, and the reactive power sharing is equivalent to a problem of reactive power current consensus. It has been proven that the system is asymptotically stable under the proposed control strategy. When the adjacent DG’s reactive power is not proportionally distributed, the current weight error term will generate a virtual impedance correction term through the proportional-integral controller based on the reactive current consensus control strategy, thus introducing adaptive virtual impedance to eliminate mismatches in output impedance between DGs. Reactive power auto-proportional distribution can be achieved without knowing the line impedance. At the same time, the power control loop is simplified and the virtual impedance compensation angle is employed to compensate the decreased reference voltage magnitude and varied phase angle due to the introduction of the virtual impedance, so the stability of the system can be improved. Finally, the correctness and effectiveness of the proposed strategy are verified by modeling analysis and microgrid simulations

Semantics-Empowered Communication: A Tutorial-cum-Survey

Along with the springing up of the semantics-empowered communication (SemCom) research, it is now witnessing an unprecedentedly growing interest towards a wide range of aspects (e.g., theories, applications, metrics and implementations) in both academia and industry. In this work, we primarily aim to provide a comprehensive survey on both the background and research taxonomy, as well as a detailed technical tutorial. Specifically, we start by reviewing the literature and answering the "what" and "why" questions in semantic transmissions. Afterwards, we present the ecosystems of SemCom, including history, theories, metrics, datasets and toolkits, on top of which the taxonomy for research directions is presented. Furthermore, we propose to categorize the critical enabling techniques by explicit and implicit reasoning-based methods, and elaborate on how they evolve and contribute to modern content & channel semantics-empowered communications. Besides reviewing and summarizing the latest efforts in SemCom, we discuss the relations with other communication levels (e.g., conventional communications) from a holistic and unified viewpoint. Subsequently, in order to facilitate future developments and industrial applications, we also highlight advanced practical techniques for boosting semantic accuracy, robustness, and large-scale scalability, just to mention a few. Finally, we discuss the technical challenges that shed light on future research opportunities.Comment: Submitted to an IEEE journal. Copyright might be transferred without further notic