Search

Deep Neural Network (DNN)-based point cloud semantic segmentation has presented significant breakthrough using large-scale labeled aerial laser point cloud datasets. However, annotating such large-scaled point clouds is time-consuming. Self-Supervised Learning (SSL) is a promising approach to this problem by pre-training a DNN model utilizing unlabeled samples followed by a fine-tuned downstream task involving very limited labels. The traditional contrastive learning for point clouds selects the hardest negative samples by solely relying on the distance between the embedded features derived from the learning process, potentially evolving some negative samples from the same classes to reduce the contrastive learning effectiveness. This work proposes a hard-negative sample-aware self-supervised contrastive learning algorithm to pre-train the model for semantic segmentation. We designed a k-means clustering-based Absolute Positive And Negative samples (AbsPAN) strategy to filter the possible false-negative samples. Experiments on two typical ALS benchmark datasets demonstrate that the proposed method is more appealing than supervised training schemes without pre-training. Especially when the labels are severely inadequate (10% of the ISPRS training set), the results obtained by the proposed HAVANA method still exceed 94% of the supervised paradigm performance with full training set. [ABSTRACT FROM AUTHOR]

The study on the Zhangjialiang No. 2 coal of Middle Jurassic Yan'an Formation in the northeastern Ordos Basin has been analyzed by gas chromatography-mass spectrometry (GC-MS) to determine geochemical characteristics and reconstruct palaeovegetation and palaeoclimate changes. The distribution of n -alkanes is mainly unimodal, with a medium and high molecular weight. Pr/Ph ratio (2.03–10.6) is high in most samples. The values of Tm/Ts ratio are between 1.42 and 4.92, and a predominance of C29 over C27 steranes has been found for the analyzed samples. These parameters indicate that all samples were deposited in an oxidizing freshwater environment, and the organic matter was mainly derived from terrestrial higher plants, with a small amount of aquatic organisms. Thermal maturity parameters such as C3122S/(22S + 22R) homohopane, C30αβ/(αβ + βα) hopane, together with the total organic carbon (TOC) contents (43.1% - 58.6% (except floor 2-13)) and the detected of C2717β(H)-22, 29, 30-trisnorhopane (βTm), suggest that most of samples have a relatively low maturity in the early stage. Besides, gymnosperm-derived biomarkers, including labdane, norpimarane, isopimarane, phyllocladane, abietane, rimuane, ent-beyerane, ent-kaurane, suggest that the vegetation during the Middle Jurassic Yan'an Formation was characterized by conifers. [ABSTRACT FROM AUTHOR]

Chemical mechanical polishing (CMP) technology is one of the key technologies to realize the global planarization of semiconductor wafer surfaces. With the increasing popularity and universality of its application, more and higher requirements are put forward for ultra-precision machining. As an important part of the CMP system, polishing pads occupy a dominant position. In this paper, a self-regressive fixed abrasive polishing pad (SR-FAPP) was prepared by photo-curing. The physical and mechanical properties of the SR-FAPP and the retreat threshold of the abrasive particles on the SR-FAPP were studied. After the CMP of the SiC wafer with a polyurethane polishing pad and the SR-FAPP, it was found that the material removal rate of the former was 75% higher than that of the latter, and the surface roughness of the latter was 75% higher than that of the former. In the micro-morphology, the scratches on the surface of the latter's polished SiC wafer were obviously reduced, which effectively improved the unevenness of the scratches on the surface of the SiC wafer after polishing, thus providing a reference for the preparation and performance research of the polishing pad. [ABSTRACT FROM AUTHOR]

In order to reduce the dependence on accumulated experience that is difficult to replicate for the human dispatchers to make power generation scheduling more automatically, quickly, and intelligently, higher requirements are placed on the level of the auxiliary decision-making system. In this paper, a short-term scheduling problem was treated as a regression task that concentrates on how to learn a reliable statistic model that concludes the intrinsic logic of the dispatching policy from extensive historical dispatching experience. In this way, since Kullback-Leibler distance can better measure the distance between two distributions, we designed a novel GAN-based learning method for the scheduling task. Also, we proposed a feasible framework that combines the stage of learning, decision-making, and deployment to support the practical implementation of the proposed algorithm. In the experiment, a real case that takes short-term scheduling tasks on a regional large-scale power system is considered in our experiments. As a result, the comparison of several methods further shows the superiority of the proposed GAN-based method under our implementations. [ABSTRACT FROM AUTHOR]

To mismatch various power disturbances of interconnected power grid, an integrated generation‐control and power‐dispatch (IGCPD) for performance‐based frequency regulation market is proposed. Unlike the conventional automatic generation control with an independent controller and a power distributor, IGCPD can significantly improve the dynamic control performance via combining these two parts into an integrated decision agent. The IGCPD eliminates the waste of frequency regulation resources and repeated regulation of the units caused by the mismatch of the controller and power distributor. In addition, by setting the reward function reasonably, IGCPD can save regulatable capacity of fast units for regulation in the next large‐scale disturbance. In order to enhance the decision ability, a swarm intelligence‐based deep deterministic policy gradient (SI‐DDPG) algorithm is proposed to acquire the control knowledge and implement a high‐quality decision for this agent. Moreover, the swarm intelligence‐based training sample can achieve a high learning robustness for SI‐DDPG which solve the problem of low robustness in conventional deep reinforcement learning. The proposed technique is verified on an England two‐area load frequency control model and the west China two‐area LFC model. IGCPD improves the area control error control performance and reduce the frequency regulation mileage payment of the interconnected power grid. [ABSTRACT FROM AUTHOR]

With the increasing attraction of unmanned aerial vehicles (UAVs) in civil, public, and military applications, multi-UAV systems can perform environmental and disaster monitoring, border surveillance, and search and rescue. It is foreseen that these multi-UAV-based applications will be an important trend for edge computing scenarios. However, due to UAVs’ limited energy supplies as well as their continuous increase in the number of sensors, energy efficiency is a critical issue in multi-UAV systems. We believe that the fusion of edge computing and cloud computing can provide effective support for energy savings. This article presents an energy-efficient edge cloud architecture called RESERVE for intelligent multi-UAV. Under RESERVE, we study the energy-efficient computation offloading decision-making problem in a decentralized manner. The problem is formulated as a three-layer game in which the discretionary approach to reaching Nash Equilibrium is presented. Based on the proposed game, we design decentralized algorithms for two different cases. The algorithms can both achieve Nash Equilibrium. Furthermore, we propose a decentralized computation offloading mechanism and analyze the performance of the game by its efficiency ratio. We conduct simulation experiments and design a framework prototype. Evaluation results demonstrate that the proposed game methods can achieve more than 30 percent extra energy consumption reduction compared with the state-of-the-art decentralized algorithm and less than 10 percent performance loss relative to the centralized solution. The prototype framework we have developed proves the concept we propose. [ABSTRACT FROM AUTHOR]

The fast access to data and high parallel processing in high-performance computing instigates an urgent demand on the improvement of the NVMe storage within modern data centers. However, the former NVMe virtualization’s unsatisfactory performance demonstrates that NVMe devices are often underutilized within cloud platforms. An NVMe virtualization mechanism with high performance and device sharing has captured researchers and developers’ attention. This article introduces MDev-NVMe, a new virtualization solution for NVMe storage device with (1) full NVMe storage virtualization for VMs running native NVMe driver, (2) a mediated pass-through mechanism for NVMe management, and (3) adaptive configuration of active polling optimization to simultaneously achieve high throughput, low latency performance, and substantial device scalability. We practically implement the MDev-NVMe as a Linux kernel module. This article subsequently evaluates MDev-NVMe with Intel OPTANE and P3600 SSD by comparing several mainstream NVMe virtualization mechanisms using application-level I/O benchmarks. MDev-NVMe with active polling can demonstrate a 142 percent improvement over native (interrupt-driven) throughput and over 2.5 × the Virtio throughput with only 70 percent native average latency and 31 percent Virtio average latency. Finally, the advantages of MDev-NVMe and the importance of adaptive polling are discussed, offering evidence that MDev-NVMe is a superior virtualization choice for cloud storage. [ABSTRACT FROM AUTHOR]

Storage virtualization is an important component of large-scale online services in multi-tenant clouds. It typically shares the physical storage among guest machines and performs transactional operations for high-performance data processing. However, even with the recent mediated pass-through virtualization optimization, the operations of multi-tenant storage I/O meet the bottleneck, and thus degrade the throughput performance of the cloud storage services. We observe that the root cause of the problem is the unawareness of varying and imbalanced workload inefficiency of resource management in the multi-tenant cloud storage setting. In this paper, we present FinNVMe, a new throughput-oriented NVMe storage virtualization management mechanism, that (1) passes-through I/O performance-critical resources and emulates privileged resources to provide high throughput in a workload-aware manner among multi-tenant VMs, (2) enables fine-grained scheduling for I/O resources to achieve promising flexibility and scalability with respective to virtualization, and (3) adopts the queue binding and the queue shuffling to reduce the virtualization and management overhead, and involves active polling for further I/O acceleration. This article subsequently evaluates FinNVMe with micro benchmarks on two typical scenarios (both balanced and imbalanced workload) and the real-world storage workloads to show its high throughput performance, along with the flexibility and scalability of virtualization and resource management. For example, FinNVMe achieves up to 20 percent throughput improvement with more stable latency in the varying and imbalanced workload. [ABSTRACT FROM AUTHOR]

Tianjin Economic and Development Zone in the Bohai Rim in China has attracted a large foreign capital which will help in its economic development. US $200-400 million will be invested in the region by approximately 100 companies of the Republic of Korea, between 1993 and 1995. Bohai Rim is rich in natural resources and has excellent transportation and telecommunication facilities, which is ideal for foreign investors.

The Yangtze River Valley experiences a surge in economic development due to the opening of the New Pudong Area and the surrounding cities along the Yangtze River. The central government's decision to grant morepreferential policies to the Pudong new Area and its approval for the construction of the Three Gorges Project signals the start of the region's development. Pudong presents a viable investment venture because of its good technological, economic and resource bases. The area and its adjacent provinceshave been strengthening their position by improving investment policies, infrastructure and facilities.

Hebei Province is eager to attract foreign investments. Despite its proximity to Beijing, it lags behind other coastal provinces in foreign investments. The Japan External Trade Organization ranked Hebei eighth among China's 30 provinces in economic potential and cited its capacity to attract foreign investments. The province can become a strong commodity economy, with 760 projected joint ventures with foreign firms.

China is planning to build a dam, a hydroelectric power plant and improve its inland shipping facilities by constructing the Yangtze River Three Gorges Project. The project is designed to conserve water, control floods, generate electric power, widen shipping lanes, reduce water current and improve facilities for the aquaculture industry. The latest feasibility study has confirmed the technological and economic viability of the scheme. The proposal has been submitted to the National People's Congress and its approval is expected by Mar 1992.

Cloud applications can achieve similar performance with diverse multi-resource configurations, allowing cloud service providers to benefit from optimal resource allocation for reducing their operation cost. This paper aims to solve the problem of multi-resource negotiation with considerations of both the service-level agreement (SLA) and the cost efficiency so that the performance requirement for cloud services is satisfied and the cost of resource usage is also minimized. The performance and resource demand are usually application-dependent, making the optimization problem complicated, especially when the dimension of multi-resource configurations is large. To this end, we use reinforcement learning to solve the optimal problem of multi-resource configuration with simultaneous optimization of the learning efficiency and performance guarantee. The developed prototype named SmartYARN is an extended Apache YARN equipped with our learning algorithm which can enable cloud applications to negotiate multiple resources cost-effectively. The extensive evaluations with four typical benchmarks show that SmartYARN performs well in reducing the cost of resource usage while maintaining compliance with the SLA constraints of cloud service simultaneously. [ABSTRACT FROM AUTHOR]

Sludge from 6 municipal sewage treatment plants in Hangzhou was collected and its basic physicochemical properties were analyzed. The contents and morphological characteristics of 8 heavy metals was studied, which would provide reference for the recycling and disposal of municipal sludge in Hangzhou. The results showed high land use potential of municipal sludge in Hangzhou due to the high nutrient content and the main heavy metal content lower than the standard limit. The mean content of each element ranked as Zn (380.3 mg/kg) > Ni (91.9 mg/kg) >Cu (89.1 mg/kg) > Pb (48.0 mg/kg) > Cr (46.6 mg/kg) > As (14.7 mg/kg) > Hg (2.7 mg/kg) > Cd (1.4 mg/kg). The morphological characteristic results showed that the distribution proportions of heavy metals in different sewage plant sludge were basically similar. As and Cd showed the highest bioavailable proportion of more than 80%. High bioavailable fraction was also found in Zn, Ni, and Cu, thus more attention should be paid to their potential ecological hazards when reuse. Considering the morphological characteristics of key metals in sludge, biofiltration can be used for detoxification before sludge resource utilization. [ABSTRACT FROM AUTHOR]

This paper introduces gMig, an open-source and practical vGPU live migration solution for full virtualization. Taking the advantage of the dirty pattern of GPU workloads, gMig presents the One-Shot Pre-Copy mechanism combined with the hashing based Software Dirty Page technique to achieve efficient vGPU live migration. Particularly, we propose three core techniques for gMig: 1) Dynamic Graphics Address Remapping, which parses and manipulates GPU commands to adjust the address mapping and adapt to a different environment after migration, 2) Software Dirty Page, which utilizes a hashing based approach with sampling pre-filtering to detect page modification, overcomes the commodity GPU's hardware limitation, and speeds up the migration by only sending the dirtied pages, 3) Overlapped Migration Process, which significantly compresses the hanging overhead by overlapping the dirty page verification and transmission concurrently. Our evaluation shows that gMig achieves GPU live migration with an average downtime of 302 ms on Windows and 119 ms on Linux. With the help of Software Dirty Page, the number of GPU pages transferred during the downtime is effectively reduced by up to 80.0 percent. The design of sampling filter and overlapped processing can bring about further 30.0 and 10.0 percent improvements in page processing. [ABSTRACT FROM AUTHOR]

This paper systematically expounded the theory of optimal frequency hopping patterns based on Welch Costas arrays with 1-gap row, a theory established after studying the properties and mathematical models of the frequency hopping patterns obtained by making two-dimensional cyclic shift to Welch Costas arrays. The algebraic construction and the autocorrelation and cross-correlation properties of the Welch Costas arrays with 1-gap row were studied and certain theorems were proved. The optimal frequency hopping patterns will have ideal autocorrelation and cross-correlation when designed by means of Welch Costas arrays with 1-gap row. [ABSTRACT FROM AUTHOR]

The Three Gorges Project is seen as feasible after a study is conducted regarding its financial strategies, material and technical needs and design. The super dam, in view of China's present economic status, is feared to be too ambitious in terms of finance. A previous similar project, the Gezhouba Hydroelectric Power Station, albeit on a smaller scale serves as the model for Three Gorges. The project will nevertheless require a certain amount of foreigner-supplied technological expertise in order for the project to succeed.

Environmental studies reveal that the Three Gorges water control program would have a significant impact on the environment. Despite the greatly reduced flood threat along river areas, the dam completion will produce a number of unfavourable effects unless preventive measures are adopted. Reservoir waters will engulf a number of historical sites as local resettlement programs have to be made for people likely to be affected by the project. Although the program will not produce change on the scenery, the construction will likely alter the area's present aquatic ecological system.

Considering the performance improvement the cloud technology provides by processing workloads in parallel, applications and services are now migrating to online clouds. In a cloud platform, workloads can be executed in a virtualized environment to have a great improvement of the resource utilization. However, there is a new challenge in the allocation problem, which is quantifying and optimizing the fairness and efficiency of heterogeneous resources (CPUs and GPUs) required by applications such as cloud gaming. The solving approach needs scalarization methods of the requirement vector, relevant functions for fairness metrics, and an acceptable algorithm to solve that, where the difficulties mainly locate. We design an iterative, dynamic-adaptive heuristic solving algorithm Fairness-Efficiency Allocation (FEA) and optimize the implementation on a virtualized platform, which collects runtime data, allocates resources and reports differences. Data are recorded and analyzed to discover the effect of the allocation in different situations, including the promotion of fairness and the effect on the frame rate of the workloads. The result indicates that there is a considerable fairness improvement after the resource allocation, especially in situations that many virtual machines are executing simultaneously. Compared with the VGASA strategy, the fairness metric value improved 45 percent in three virtual machines' situation. [ABSTRACT FROM AUTHOR]