Influence of temperature on acoustic emission source location accuracy in underground structure

An acoustic emission (AE) experiment was carried out to explore the AE location accuracy influenced by temperature. A hollow hemispherical specimen was used to simulate common underground structures. In the process of heating with the flame, the pulse signal of constant frequency was stimulated as an AE source. Then AE signals received by each sensor were collected and used for comparing localization accuracy at different temperatures. Results show that location errors of AE keep the same phenomenon in the early and middle heating stages. In the later stage of heating, location errors of AE increase sharply due to the appearance of cracks. This provides some beneficial suggestions on decreasing location errors of structural cracks caused by temperature and improves the ability of underground structure disaster prevention and control.     

The effect of biomass feeding location on rice husk gasification for hydrogen production

The objective of this study is to investigate the impact of biomass feeding location on rice husk gasification for hydrogen production. By comparing the results between top-feed and bottom-feed of the feedstock of the fluidized bed biomass gasification at the reaction temperature between 600～1000 °C and ER = 0.2, 0.27, and 0.33 without steam, the optimum low heating value was increase by 2.35 kJ/g-rice husk by the top-feed to gasifier. Although the yield of hydrogen was decreased by 42% for the rice husk gasification by the top-feed operation, the yield of CO, CO2, and CH4 were highly increased, which enhancing the heating value of the effluent gas. The study results suggested the potential route of the biomass gasification at the different feeding location.     

YOLSO: You Only Look Small Object

Small object detection is challenging and far from satisfactory. Most general object detectors suffer from two critical issues with small objects: (1) Feature extractor based on classification network cannot express the characteristics of small objects reasonably due to insufficient appearance information of targets and a large amount of background interference around them. (2) The detector requires a much higher location accuracy for small objects than for general objects. This paper proposes an effective and efficient small object detector YOLSO to address the above problems. For feature representation, we analyze the drawbacks in previous backbones and present a Half-Space Shortcut(HSSC) module to build a background-aware backbone. Furthermore, a coarse-to-fine Feature Pyramid Enhancement(FPE) module is introduced for layer-wise aggregation at a granular level to enhance the semantic discriminability. For loss function, we propose an exponential L1 loss to promote the convergence of regression, and a focal IOU loss to focus on prime samples with high classification confidence and high IOU. Both of them significantly improves the location accuracy of small objects. The proposed YOLSO sets state-of-the-art results on two typical small object datasets, MOCOD and VeDAI, at a speed of over 200 FPS. In the meantime, it also outperforms the baseline YOLOv3 by a wide margin on the common COCO dataset.     

Location Privacy in Device-Dependent Location-Based Services: Challenges and Solution

With the evolution of location-based services (LBS), a new type of LBS has already gain a lot of attention and implementation, we name this kind of LBS as the Device-Dependent LBS (DLBS). In DLBS, the service provider (SP) will not only send the information according to the user’s location, more significant, he also provides a service device which will be carried by the user. DLBS has been successfully practised in some of the large cities around the world, for example, the shared bicycle in Beijing and London. In this paper, we, for the first time, blow the whistle of the new location privacy challenges caused by DLBS, since the service device is enabled to perform the localization without the permission of the user. To conquer these threats, we design a service architecture along with a credit system between DLBS provider and the user. The credit system tie together the DLBS device usability with the curious behaviour upon user’s location privacy, DLBS provider has to sacrifice their revenue in order to gain extra location information of their device. We make the simulation of our proposed scheme and the result convince its effectiveness.     

The location and sizing of urban freight loading/unloading lay-by areas

The volume of freight vehicles operating within city boundaries is steadily increasing, which contributes to road congestion, especially in urban areas with a parking deficit. The proper identification of the location and size of commercial parking lay-by areas, where delivery vehicles can park for a limited time to perform loading/unloading operations, can relieve road congestion. Compared to the existing literature, this paper presents an improved two-stage approach, which includes a mathematical programme and a simulation model for determining the location and sizing of lay-by areas. The robustness and soundness of the results from a methodological and practical point of view are discussed using an example application.     

The generalized discrete ‐centroid problem

The ‐centroid problem or leader–follower problem is generalized considering different customer choice rules where a customer may use facilities belonging to different firms, if the difference in travel distance (or time) is small enough. Assuming essential goods, some particular customer choice rules are analyzed. Linear programming formulations for the generalized ‐medianoid and ‐centroid problems are presented and an exact solution approach is applied. Some computational examples are included.     

Precipitation location of secondary phase and microstructural evolution during static recrystallization of as-cast Ti-25V-15Cr-0.3Si titanium alloy

The microstructural evolution and precipitation location of the secondary phase of an as-cast Ti-25V-15Cr-0.3Si titanium alloy were investigated via isothermal compression experiments and heat treatment. The average aspect (length-to-width) ratio, average area and size of the grains at different heat treatment temperatures and holding time were analyzed and the effects of deformation and annealing time on the grain area and size were considered. It was found that the grain size was strongly influenced by the height reduction and holding time. Grain growth was significant when annealing time increased from 10 min to 2 h at 950 °C and height reduction of 30%; however, grain growth was minimal at annealing time between 2 and 4 h. Many dispersion particles were observed to form in continuous chains; the precipitation location was confirmed to be along initial grain boundaries, and the dispersion particles were identified to be Ti₅Si₃ phase by TEM.     

Scalable Distributed Sensor Fault Diagnosis for Smart Buildings

The enormous energy use of the building sector and the requirements for indoor living quality that aim to improve occupants’ productivity and health, prioritize Smart Buildings as an emerging technology. The Heating, Ventilation and Air-Conditioning (HVAC) system is considered one of the most critical and essential parts in buildings since it consumes the largest amount of energy and is responsible for humans comfort. Due to the intermittent operation of HVAC systems, faults are more likely to occur, possibly increasing eventually building’s energy consumption and/or downgrading indoor living quality. The complexity and large scale nature of HVAC systems complicate the diagnosis of faults in a centralized framework. This paper presents a distributed intelligent fault diagnosis algorithm for detecting and isolating multiple sensor faults in large-scale HVAC systems. Modeling the HVAC system as a network of interconnected subsystems allows the design of a set of distributed sensor fault diagnosis agents capable of isolating multiple sensor faults by applying a combinatorial decision logic and diagnostic reasoning. The performance of the proposed method is investigated with respect to robustness, fault detectability and scalability. Simulations are used to illustrate the effectiveness of the proposed method in the presence of multiple sensor faults applied to a 83-zone HVAC system and to evaluate the sensitivity of the method with respect to sensor noise variance.      

A dynamic closed-loop location-inventory problem under disruption risk

In this paper, a dynamic closed-loop location-inventory problem is addressed that optimizes strategic decisions (i.e., facility location in terms of contracting/selection of distribution centers and reworking centers) along with tactical ones (i.e., allocation of centers, inventory management) under facility disruption risks. The presented model seeks to minimize total cost as the first objective function, and time as the second one in the considered network. Due to the NP-Hard nature of the model, a hybrid meta-heuristic algorithm based on Multi-Objective Particle Swarm Optimization (MOPSO) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is presented to solve the problem in large scales. Finally, applicability of the proposed model is tested via a real case study and the results are analyzed in depth.     

基于变异测试的错误定位研究进展

随着软件规模和复杂度的不断提高，软件的质量问题成为了关注的焦点，如何高效地找出软件中的错误成为一个亟需解决的问题。错误定位是软件质量保证的重要途径之一，近年来已经成为软件工程中一个非常重要的研究课题。基于变异测试的错误定位通过比较原程序和对应变异体的差异来计算每条语句的怀疑度，再由怀疑度大小进行排序，程序员根据排序逐个检查找出错误语句。汇总近7年（2012-2018）国内外的基于变异测试的错误定位技术的研究成果，介绍了错误定位的基本方法，介绍基于变异测试的错误定位思想，从变异算子、变异体及等价变异体3个方面对已有的研究工作进行分类归纳和总结，探讨了基于变异测试的错误定位未来可能的研究方向、机遇和挑战。