Sense, Model and Identify the Load Signatures of HVAC Systems in Metro Stations

The HVAC systems in subway stations are energy consuming giants, each of which may consume over 10, 000 Kilowatts per day for cooling and ventilation. To save energy for the HVAC systems, it is critically important to firstly know the "load signatures" of the HVAC system, i.e., the quantity of heat imported from the outdoor environments and by the passengers respectively in different periods of a day, which will significantly benefit the design of control policies. In this paper, we present a novel sensing and learning approach to identify the load signature of the HVAC system in the subway stations. In particular, sensors and smart meters were deployed to monitor the indoor, outdoor temperatures, and the energy consumptions of the HVAC system in real-time. The number of passengers was counted by the ticket checking system. At the same time, the cooling supply provided by the HVAC system was inferred via the energy consumption logs of the HVAC system. Since the indoor temperature variations are driven by the difference of the loads and the cooling supply, linear regression model was proposed for the load signature, whose coefficients are derived via a proposed algorithm . We collected real sensing data and energy log data from HaiDianHuangZhuang Subway station, which is in line 4 of Beijing from the duration of July 2012 to Sept. 2012. The data was used to evaluate the coefficients of the regression model. The experiment results show typical variation signatures of the loads from the passengers and from the outdoor environments respectively, which provide important contexts for smart control policies.Comment: 5 pages, 5 figure

Hybrid Radio-map for Noise Tolerant Wireless Indoor Localization

In wireless networks, radio-map based locating techniques are commonly used to cope the complex fading feature of radio signal, in which a radio-map is built by calibrating received signal strength (RSS) signatures at training locations in the offline phase. However, in severe hostile environments, such as in ship cabins where severe shadowing, blocking and multi-path fading effects are posed by ubiquitous metallic architecture, even radio-map cannot capture the dynamics of RSS. In this paper, we introduced multiple feature radio-map location method for severely noisy environments. We proposed to add low variance signature into radio map. Since the low variance signatures are generally expensive to obtain, we focus on the scenario when the low variance signatures are sparse. We studied efficient construction of multi-feature radio-map in offline phase, and proposed feasible region narrowing down and particle based algorithm for online tracking. Simulation results show the remarkably performance improvement in terms of positioning accuracy and robustness against RSS noises than the traditional radio-map method.Comment: 6 pages, 11th IEEE International Conference on Networking, Sensing and Control, April 7-9, 2014, Miami, FL, US

Looking into the Environmental Factors Affecting the Performance of Ubiquitous Technologies Deployment: An Empirical Study on Chinese Information and Communication Technology Companies

Effective deployment of ubiquitous technologies can help companies improve the business efficiency, especially for those ICT (Information communication technology) companies who are involved in M-business, M-commerce, and etc. However, there are many factors could affect the performance of the ubiquitous technologies deployment, such as the company’s management, the employee’s coordination, and etc. In this paper, we are focused on the environmental factors that would have an impact on the performance of organizations which have deployed or is deploying ubiquitous technologies, and investigate more than 50 Chinese ICT companies. According to our findings, in the context of China, a sensible, dependent, and interactive business relationship with the outside environment will have a positive impact on their ubiquitous technologies deployment’s performance, while the decentralization and hierarchism within organizational structure in the inside environment will have a negative impact on their ubiquitous technologies deployment’s performance

Increasing but Variable Trend of Surface Ozone in the Yangtze River Delta Region of China

Surface ozone (O-3) increased by similar to 20% in the Yangtze River Delta (YRD) region of China during 2014-2020, but the aggravating trend is highly variable on interannual time and city-level space scales. Here, we employed multiple air quality observations and numerical simulation to describe the increasing but variable trend of O-3 and to reveal the main driving factors behind it. In 2014-2017, the governmental air pollution control action plan was mostly against PM2.5 (mainly to control the emissions of SO2, NOx, and primary PM2.5) and effectively reduced the PM2.5 concentration by 18%-45%. However, O-3 pollution worsened in the same period with an increasing rate of 4.9 mu g m(-3) yr(-1), especially in the Anhui province, where the growth rate even reached 14.7 mu g m(-3) yr(-1). After 2018, owing to the coordinated prevention and control of both PM2.5 and O-3, volatile organic compound (VOC) emissions in the YRD region has also been controlled with a great concern, and the O-3 aggravating trend in the same period has been obviously alleviated (1.1 mu g m(-3) yr(-1)). We further combined the precursor concentration and the corresponding O-3 formation regime to explain the observed trend of O-3 in 2014-2020. The leading O-3 formation regime in 2014-2017 is diagnosed as VOC-limited (21%) or mix-limited (58%), with the help of a simulated indicator HCHO/NOy. Under such condition, the decreasing NO2 (2.8% yr(-1)) and increasing VOCs (3.6% yr(-1)) in 2014-2017 led to a rapid increment of O-3. With the continuous reduction in NOx emission and further in ambient NOx/VOCs, the O-3 production regime along the Yangtze River has been shifting from VOC-limited to mix-limited, and after 2018, the mix-limited regime has become the dominant O-3 formation regime for 55% of the YRD cities. Consequently, the decreases of both NOx (3.3% yr(-1)) and VOCs (7.7% yr(-1)) in 2018-2020 obviously slowed down the aggravating trend of O-3. Our study argues that with the implementation of coordinated regional reduction of NOx and VOCs, an effective O-3 control is emerging in the YRD region.Peer reviewe

Experimental study on the mechanical controlling factors of fracture plugging strength for lost circulation control in shale gas reservoir

The geological conditions of shale reservoir present several unique challenges. These include the extensive development of multi-scale fractures, frequent losses during horizontal drilling, low success rates in plugging, and a tendency for the fracture plugging zone to experience repeated failures. Extensive analysis suggests that the weakening of the mechanical properties of shale fracture surfaces is the primary factor responsible for reducing the bearing capacity of the fracture plugging zone. To assess the influence of oil-based environments on the degradation of mechanical properties in shale fracture surfaces, rigorous mechanical property tests were conducted on shale samples subsequent to their exposure to various substances, including white oil, lye, and the filtrate of oil-based drilling fluid. The experimental results demonstrate that the average values of the elastic modulus and indwelling hardness of dry shale are 24.30 GPa and 0.64 GPa, respectively. Upon immersion in white oil, these values decrease to 22.42 GPa and 0.63 GPa, respectively. Additionally, the depth loss rates of dry shale and white oil-soaked shale are determined to be 57.12% and 61.96%, respectively, indicating an increased degree of fracturing on the shale surface. White oil, lye, and the filtrate of oil-based drilling fluid have demonstrated their capacity to reduce the friction coefficient of the shale surface. The average friction coefficients measured for white oil, lye, and oil-based drilling fluid are 0.80, 0.72, and 0.76, respectively, reflecting their individual weakening effects. Furthermore, it should be noted that the contact mode between the plugging materials and the fracture surface can also lead to a reduction in the friction coefficient between them. To enhance the bearing capacity of the plugging zone, a series of plugging experiments were conducted utilizing high-strength materials, high-friction materials, and nanomaterials. The selection of these materials was based on the understanding of the weakened mechanical properties of the fracture surface. The experimental results demonstrate that the reduced mechanical properties of the fracture surface can diminish the pressure-bearing capacity of the plugging zone. However, the implementation of high-strength materials, high-friction materials, and nanomaterials effectively enhances the pressure-bearing capacity of the plugging zone. The research findings offer valuable insights and guidance towards improving the sealing pressure capacity of shale fractures and effectively increasing the success rate of leakage control measures during shale drilling and completion. © 2023 The Author

Familiarity-based Collaborative Team Recognition in Academic Social Networks

Collaborative teamwork is key to major scientific discoveries. However, the prevalence of collaboration among researchers makes team recognition increasingly challenging. Previous studies have demonstrated that people are more likely to collaborate with individuals they are familiar with. In this work, we employ the definition of familiarity and then propose MOTO (faMiliarity-based cOllaborative Team recOgnition algorithm) to recognize collaborative teams. MOTO calculates the shortest distance matrix within the global collaboration network and the local density of each node. Central team members are initially recognized based on local density. Then MOTO recognizes the remaining team members by using the familiarity metric and shortest distance matrix. Extensive experiments have been conducted upon a large-scale data set. The experimental results show that compared with baseline methods, MOTO can recognize the largest number of teams. The teams recognized by MOTO possess more cohesive team structures and lower team communication costs compared with other methods. MOTO utilizes familiarity in team recognition to identify cohesive academic teams. The recognized teams are in line with real-world collaborative teamwork patterns. Based on team recognition using MOTO, the research team structure and performance are further analyzed for given time periods. The number of teams that consist of members from different institutions increases gradually. Such teams are found to perform better in comparison with those whose members are from the same institution