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Solid-state color-saturated blue emitters are an essential part of light-emitting diodes (LEDs) and high-definition displays, and lead halide perovskite nanomaterials are one such type of emitter. ...

The multi-energy system (MES) provides a good environment for the local consumption of renewable energy such as wind and solar power because of its high operational flexibility. In the MES, the hybrid energy storage system (HESS) composed of the battery and thermal storage tank plays an important role in enhancing reliability, economics, and operational flexibility. Hence, determining the optimal size of HESS in the MES is a critical problem but has not received enough attention. In light of this problem, this paper focuses on the optimal HESS planning problem in the community MES (CMES) under diverse uncertainties. Firstly, a two-stage stochastic planning model is proposed for the CMES to coordinate the optimal long-term HESS allocation and the short-term system operation. The thermal inertia in the heating network, space heating demand, and domestic hot water demand is utilized to reduce both the planning and operational cost. Secondly, a deterministic equivalence is proposed for the two-stage planning model to convert it into a mixed-integer linear programming model, which is then solved by off-the-shelf solvers. Finally, simulation results verify the effectiveness of the proposed method. The results reveal that the HESS can enhance the operational flexibility of the CMES but only needs a very few investment costs and prove that the thermal inertia in the CMES can reduce the investment cost of HESS, the fuel, and the operational maintenance cost.

To satisfy the residential cooling demand in tropical large renewable microgrids (MGs), multiple ice-storage air conditionings (IACs) are usually used to supply cold energy. However, these IACs could not directly exchange the cold energy, which seriously limits the combined regulation effect. To address the problem, a novel cooling and power combined supply system (CPCSS) is proposed in this paper, where the trucks are used to distribute the ices among IACs. To evaluate the performance on the multi-time scales, a cooling and power combined supply model is established. On this basis, a two-stage (i.e., day-ahead and real-time stage) scheduling strategy is presented to minimize the MG operation cost. To speed up the solution, this scheduling model is first linearized as a classic mixed-integer second-order cone programming (MISCP) problem, and then is solved by Lagrange solution method. Simulation studies on an IEEE 14-node system indicates that compared with the existing MG operation cost, the annual operational cost of the based-two-truck CPCSS could be reduced by 9868800 yuan (12.6%), while the network loss could be decreased from 5.4 to 3.9 MWh. The results confirm the effectiveness of the proposed strategy.

The integrated energy system can realize the coupling and complementation of various energy sources such as cold, heat and electricity, and plays an important role in the consumption of renewable energy. This paper proposes a bi-level optimal design method for integrated energy system from both economic and carbon emissions aspects. The upper model aims at maximizing the system economy and optimizes the selection and capacity allocation of renewable energy power generation, storage and conversion equipment to meet the demands in the region. The lower model aims at maximizing the environment-protection performance and minimizes the system’s carbon emissions. Since the lower model contains binary variables to characterize trading states and charging and discharging states, the model cannot be transformed into a mathematical program with equilibrium constraints. To effectively handle this problem, the reformulation and decomposition method is adopted. Case studies show that this bi-level model can effectively consider the influence of the objective function in the lower model on the optimal capacity configuration in the upper model, avoid the influence of different objective weights when the multi-objective model is converted to the single-objective model, and obtain the global optimal solution.

In low latitudes, ice storage air conditioners (ISACs) have been widely used to cool while locally responding to the distribution network demand. However, due to the lack of the direct cold energy exchange between ISACs, the cooling load could only be shifted on the time scale instead of the space scale, resulting in an unsatisfactory regulation result. To address the above problem, this article proposes a novel collaborative expansion planning scheme for integrated cooling and power system. Firstly, to increase the regulation flexibility, a novel cold energy supply system is designed, where ice making stations and trucks are used to produce and deliver ices to multiple terminal ISACs. On this basis, taking the capacity of the wind turbines (WTs), large ice makers (LIMs), and trucks as configuration decisions, an optimal expansion planning model is established considering wind generation uncertainties. This model is converted into a classic mixed-integer second-order cone programming (MISOCP) problem using linear techniques, and efficiently solved by the Benders decomposition method. Finally, Shapley value method in economics is used to fairly distribute the revenues between the grid operator (GO) and ISAC owners. Simulation studies on IEEE 14-node distribution network indicate the proposed expansion model is effective and beneficial.

The development of integrated electricity and heat system (IEHS) promotes the renewable energy penetration and the complementary coupling between the electric power system (EPS) and the district heat system (DHS). The efficient energy flow calculation (EFC) in the IEHS has become a hotspot as the system scale and complexity increase. This paper firstly constructed the dynamic DHS model using Euler difference scheme. On this basis, the fast decomposed method is proposed to decompose the system with various scales and topologies, which improves the computation efficiency. Moreover, the modification mode is then developed to modify the EFC results in the decomposed systems and ensure the accuracy. Numerical simulation verifies the accuracy of the proposed method and its superiority in computation efficiency.

The hybrid neural network model proposed in this paper consists of two main parts: extracting local features of text vectors by convolutional neural network, extracting global features related to text context by BiLSTM, and fusing the features extracted by the two complementary models. In this paper, the pre-processed sentences are put into the hybrid neural network for training. The trained hybrid neural network can automatically classify the sentences. When testing the algorithm proposed in this paper, the training corpus is Word2vec. The test results show that the accuracy rate of text categorization reaches 94.2%, and the number of iterations is 10. The results show that the proposed algorithm has high accuracy and good robustness when the sample size is seriously unbalanced.

The problem of fossil energy shortage and pollution has become increasingly prominent. In China, government-led electric energy substitution projects are being carried out in nationwide, aiming at the end of energy consumption links, using electricity instead of coal-fired and oil-fired energy consumption, in order to optimize the energy structure and achieve the goal of energy conservation and emission reduction. Aiming at the planning and benefit sharing of electric energy substitution project, this research first designs the planning mechanism of electric energy substitution for productive users, analyses distribution network planning, transformation subsidy and so on, and puts forward three schemes. Secondly, based on the cooperative game theory, the benefit allocation model of electric energy substitution project is established. Finally, we take the flue-cured tobacco project about replacing coal with electricity in three areas of Yunnan province as an example to analyze, optimize the game strategy, and put forward three indicators to measure the benefits of the participants in the project. The pollution reduction situation that can not be converted into economic benefits is counted. The modeling results show that the density of users geographically will directly affect the cost of the distribution network transformation. The coal flue-cured tobacco heat loss and serious pollution, after optimization, power grid enterprises can quickly recover investment.

The rise of blockchain technology has injected new vitality into the energy market. At present, blockchain projects for distributed photovoltaic power generation and carbon trading are rapidly emerging, but due to the barrier of two markets and the independent operation of such projects in two chains, the two market values cannot be two-way circulation, which is not conducive to the expansion of power trading business and the scale of carbon market. This paper proposed a joint operation mechanism of cross-chain trading, combined distributed photovoltaic power generation market and the carbon market by the blockchain technology. The novelty is to construct two chains which includes the mainchain and sidechain that enables the two markets to share data and circulate value, and to design a two-way anchoring method that achieves equating between carbon trading and electricity trading by cryptocurrency. The simulation studies took the improved IEEE 33 system as an example to simulate transactions on the Ethereum platform, and technically verify the feasibility of cross-chain transactions in these two markets, and use Ether as the settlement currency.

Multiple reflection has been proven to be an effective method to enhance the gas detection sensitivity of Raman spectroscopy, while Raman gas probes based on the multiple reflection principle have been rarely reported on. In this paper, a multi-reflection, cavity enhanced Raman spectroscopy (CERS) probe was developed and used for in situ multi-component gas detection. Owing to signal transmission through optical fibers and the miniaturization of multi-reflection cavity, the CERS probe exhibited the advantages of in situ detection and higher detection sensitivity. Compared with the conventional, backscattering Raman layout, the CERS probe showed a better performance for the detection of weak signals with a relatively lower background. According to the 3σ criteria, the detection limits of this CERS probe for methane, hydrogen, carbon dioxide and water vapor are calculated to be 44.5 ppm, 192.9 ppm, 317.5 ppm and 0.67%, respectively. The results presented the development of this CERS probe as having great potential to provide a new method for industrial, multi-component online gas detection.

Under the environment of fierce market competition and the upgrading of the power market, the integrated energy service industry will gradually become a new trend in future power development. Integrated energy online e-commerce service is a key component of future grid smart energy, and it is also an important way for the transition of energy sources from grid energy integration services to multi-energy service providers' competitive supply strategy transformation. An effective integrated energy service recommendation method plays an important role for integrated energy companies to occupy an advantageous position in the energy competition. According to the user's interest characteristics and the user's behavior data in the system, this paper analyzes the user's interest preferences. Part of the data is taken to establish a user interest map and perform spectral clustering, and the clustered data is imported into the random forest model for scoring prediction training. Part of the data is taken to establish a user interest map and perform spectral clustering, and the clustered data is imported into the random forest model for scoring prediction training.

The microgrid is considered to have bright prospects in the future for its advantages of high economic benefits and environmentally friendly and alleviate the antinomy for energy’s supply and demand. Due to the introduction of renewable energy such as wind power into the microgrid, the intermittent, random, and unpredictability of its output makes it difficult to control the frequency when the microgrid operates in the isolated islands. In order to reduce the frequency fluctuation, this paper proposes a secondary frequency control strategy for isolated microgrid based on the linear active disturbance rejection control (LADRC) technology. Extended state observer (ESO) is used to estimate the extended state online and compensate the disturbance estimation actively so that the disturbance can be eliminated quickly and the frequency stability can be maintained. In addition, considering the demand response, the influence of different demand response coefficients on frequency control is analyzed. By a series of trial declarations, the LADRC control strategy can effectively suppress the frequency oscillation, and after adding the demand responses, the response speed can be faster than the traditional frequency modulation, reflecting its better robustness and stability. The simulation based on the frequency response model of AC microgrid results shows the effectiveness of the proposed method.

When combining very different images which often contain complex objects and backgrounds, producing consistent compositions is a challenging problem requiring seamless image editing. In this paper, we propose a general approach, called object-aware image editing, to obtain consistency in structure, color, and texture in a unified way. Our approach improves upon previous gradient-domain composition in three ways. Firstly, we introduce an iterative optimization algorithm to minimize mismatches on the boundaries when the target region contains multiple objects of interest. Secondly, we propose a mixed-domain consistency metric for measuring gradients and colors, and formulate composition as a unified minimization problem that can be solved with a sparse linear system. In particular, we encode texture consistency using a patch-based approach without searching and matching. Thirdly, we adopt an object-aware approach to separately manipulate the guidance gradient fields for objects of interest and backgrounds of interest, which facilitates a variety of seamless image editing applications. Our unified method outperforms previous state-of-the-art methods in preserving global texture consistency in addition to local structure continuity.

Combined cooling, heating and power (CCHP) microgrid has the advantage of high energy utilization efficiency. The fluctuation of renewable energy sources and multiple load demands challenges the economic operation of CCHP microgrid. In this paper, we propose a novel two-stage coordinated control approach for CCHP microgrid energy management, which consists of two stages: the economic dispatching stage (EDS) and the real-time adjusting stage (RTAS). In EDS, it utilizes a model predictive control incorporating piecewise linear efficiency curves to schedule the operation based on the forecast information. In RTAS, the schedule obtained in EDS is adjusted based on the real-time information to tackle the power fluctuations. A typical-structure CCHP microgrid is analyzed in the case study and simulation results are presented to demonstrate the performance of the proposed two-stage coordinated control approach.

In order to reduce the impact of the randomness and volatility of renewable energy on the economic operation of AC/DC hybrid microgrids, a multi-time-scale rolling optimization strategy is proposed for the grid-connected AC/DC hybrid microgrids. It considers the source-load uncertainty declined with time scale reduction, and the scheduling cooperation problem of different units on different time scales. In this paper, we propose a three-time-scale optimal strategy of the day-ahead, intraday and real-time dispatching stage and a two-level rolling optimal strategy of the intraday and real-time stage, aiming at minimizing the operating cost. We added the power penalty cost in the rolling optimization model to limit the energy state of the energy storage system in the constraint, and improve the power correction and tracking effect of the rolling optimization. A typical-structure AC/DC hybrid microgrid is analyzed in this paper and the simulation results are shown to demonstrate the feasibility and effectiveness of the proposed multi-time-scale rolling optimal dispatch.

The increasingly stringent regulations on automobile exhaust emission make it more and more important to study and improve the in-cylinder purification technology for gasoline engine. In the process, it is the core technology that using the three-dimensional CFD coupled chemical reaction kinetic mechanism to analyze the combustion and initial emission in the engine cylinder. Furthermore, the key point of this method is to develop high accuracy chemical reaction mechanism. Based on the physical and chemical properties of domestic 92# gasoline, this paper built a new model of multi-component alternative fuel, and then constructed and optimized the combustion chemical reaction mechanism according to the composition of alternative fuel. The results showed that physical and chemical characteristics of the domestic 92# gasoline are consistent with those of the alternative fuel consisted of iso-octane, n-heptane, toluene, diisobutylene (DIB) and methyl tertiary butyl ether (MTBE) by the percentage of 26%, 20%, 37.5%, 10.5% and 6%. The combustion reaction mechanism for multi-component alternative fuel includes 135 species and 635 elementary reactions. It can accurately predict the ignition delay time and flame propagation speed, and also can calculate the in-cylinder pressure and temperature in combustion process. This reaction mechanism can be used for in-cylinder three-dimensional combustion and initial emission simulation.

The pumped storage power station has the characteristics of frequency-phase modulation, energy saving, and economy, and has great development prospects and application value. In order to cope with the large-scale integration and intermittency of renewable energy and improve the ability of pumped storage units to participate in power grid frequency modulation, this paper proposed a load frequency control (LFC) strategy for pumped storage units based on linear active disturbance rejection technology. Firstly, based on the operating characteristics of the pumped storage power station, the LFC model of the two-area reheat steam turbine under nonlinear conditions such as governor dead zone and generation rate constrains was established. Secondly, a second-order linear active disturbance rejection control (LADRC) was designed. The feasibility and control performance of the proposed LFC system were quantitatively analyzed through simulation. The results show that the LADRC has better control effect and stronger robustness than fractional-order proportion integration differentiation (FOPID) and traditional proportion integration differentiation (PID) controller. Finally, the pumped storage power station was added, and it was found that it has better correction performance under both generating and pumping operations, which greatly improved the dynamic response of secondary frequency modulation.

With renewable generation resources and multiple load demands increasing, the combined cooling, heating, and power (CCHP) microgrid energy management system has attracted much attention due to its high efficiency and low emissions. In order to realize the integration of substation resources and solve the problems of inaccurate, random, volatile and intermittent load forecasting, we propose a three-stage coordinated optimization scheduling strategy for a CCHP microgrid. The strategy contains three stages: a day-ahead economic scheduling stage, an intraday rolling optimization stage, and a real-time adjustment stage. Forecasting data with different accuracy at different time scales were used to carry out multilevel coordination and gradually improve the scheduling plan. A case study was used to verify that the proposed scheduling strategy can mitigate and eliminate the load forecasting error of renewable energy (for power balance and scheduling economy).

Recent deep neural networks have achieved impressive performance in image classification. However, these networks are sensitive to the attack of adversarial examples, leading to a sharp drop in accuracy. To address this issue, this paper proposes a learning approach to improve the robustness by distilling deep neural networks with soft decision trees. This approach learns a decision tree in a softening manner by jointly using data and the predictions of a well-trained deep neural network. In this way, the resulting soft decision tree can distil the knowledge from deep neural network when preserving the efficiency of decision tree. Experimental results show that the proposed approach has better robustness again adversarial examples than deep neural networks and decision trees.

The effective interaction length of airflow ( L e ) is the key factor in the determination of the distance between an air duct nozzle and the working face in dead-end tunnels. At present, the current calculation methods of L e consider only the tunnel cross section dimension and are therefore not suitable for large underground cavern groups. Because air backflow is the direct promoting factor in the removal of pollutants and the construction specifications in underground space require a minimum wind speed, this paper proposed that L e is the distance between the nozzle and the critical cross section, where the mean velocity of the air backflow equals the minimum wind speed required. For this purpose, a computational study was conducted to investigate the flow pattern of air backflow in ventilated tunnels. Semi-empirical equations of the mean velocity distribution law of air backflow were derived based on the numerical results. By solving the equations, the location of the critical cross section can then be acquired. Accordingly, the value of L e could be obtained. The proposed method is related to the tunnel width, the tunnel height, the air duct diameter and the efflux velocity. Thus, it is more comprehensive and applicable compared to the current methods.

Recent deep trackers have achieved impressive performance in visual tracking. Typically, these trackers apply complex deep networks with massive parameters for representing objects, which makes their deployment on resource-limited devices very challenging due to two reasons: (1) high computation complexity, and (2) high storage footprint. To address these two issues, this paper proposes a lightweight deep tracker to facilitate efficient tracking by using a small deep convolutional neural network. This tracker adaptively extracts features of interest from different deep layers for representing different objects, and then integrates into discriminative correlation filter formulation. Due to the usage of small deep networks and selection of deep features, the drop on tracking accuracy could be effectively alleviated, while the costs in computation and storage could be greatly reduced. This tracker can run at a very fast speed of 55 fps when only taking 4.8M parameters. Experimental results on the public OTB2013 and OTB100 benchmarks demonstrate the effectiveness and efficiency of the proposed tracker.

Detecting faces with occlusions is a challenging task due to two main reasons: 1) the absence of large datasets of masked faces, and 2) the absence of facial cues from the masked regions. To address these two issues, this paper first introduces a dataset, denoted as MAFA, with 30, 811 Internet images and 35, 806 masked faces. Faces in the dataset have various orientations and occlusion degrees, while at least one part of each face is occluded by mask. Based on this dataset, we further propose LLE-CNNs for masked face detection, which consist of three major modules. The Proposal module first combines two pre-trained CNNs to extract candidate facial regions from the input image and represent them with high dimensional descriptors. After that, the Embedding module is incorporated to turn such descriptors into a similarity-based descriptor by using locally linear embedding (LLE) algorithm and the dictionaries trained on a large pool of synthesized normal faces, masked faces and non-faces. In this manner, many missing facial cues can be largely recovered and the influences of noisy cues introduced by diversified masks can be greatly alleviated. Finally, the Verification module is incorporated to identify candidate facial regions and refine their positions by jointly performing the classification and regression tasks within a unified CNN. Experimental results on the MAFA dataset show that the proposed approach remarkably outperforms 6 state-of-the-arts by at least 15.6%.

Combined cooling, heating, and power (CCHP) systems have been widely applied in various kinds of buildings. Most operation strategies for CCHP microgrids are designed based on day-ahead profiles. However, prediction error for renewable energy resources (RES) and load leads to suboptimal operation in dispatch scheduling. In this paper, we propose an online optimal operation approach for CCHP microgrids based on model predictive control with feedback correction to compensate for prediction error. This approach includes two hierarchies: 1) rolling optimization; and 2) feedback correction. In the rolling part, a hybrid algorithm based on integrating time series analysis and Kalman filters is used to forecast the power for RES and load. A rolling optimization model is established to schedule operation according to the latest forecast information. The rolling dispatch scheduling is then adjusted based on ultra-short-term error prediction. The feedback correction model is applied to minimize the adjustments and to compensate for prediction error. A case study demonstrates the effectiveness of the proposed approach with better matching between demand and supply.

An efficient way to identify the source of a web video is to detect some specified logos (e.g. a TV logo or a brand). Ln this paper, we propose a logo detection system "GeLoGo" for web-scale videos via four major modules. The keyframe module first extracts several frames with shot segment detection. In this manner, the data can be dramatically reduced. After that, the proposal module localizes candidate logos from the extracted frames by an ensemble of some pretrained logo detectors. Then, the spatial verification module takes the logo proposals as input and identifies them as real logos or not by performing the classification task within a ResNet network. Finally, the temporal verification module further identifies the detection results by checking the temporal consistency of logo locations. Experimental results on a collected large-scale TV logo video dataset show that the proposed approach can achieve a detection accuracy of 99.1% with a speed of 25 fps in detecting logos in videos.

The deployments of current object trackers on mobile or embedded devices are hindered due to high computation complexity and high resource footprint. To address these issues, this paper proposes an ultra-fast and energyefficient object tracker, to perform accurate and robust object tracking at a speed of more than 56fps with a memory cost of less than 40KB and a clock rate of 480MHz via a unified feature representation and matching. First, we propose a compact feature representation for describing the object, which utilizes the response distribution to provide sparse representation. In this manner, the object can be represented with a low-dimensional and fixed-point vector. Further, we propose a fast feature matching method to jointly perform object verification and regression. Experiments on the public OTB benchmark and our collected weak object tracking (WTB) dataset demonstrate the effectiveness of our proposed tracker.

The performance of face detection in heavy occlusion conditions (e.g., occluded by masks) will drop dramatically since facial cue is missing or polluted by occluders. To address this issue, we propose a novel method by cascaded outrageous proposal generation and deep convolutional neural network (DCNN) via two major modules. First, the proposal generation module assembles many fast face detectors with low threshold to generate massive candidate proposals in a high recall rate. Then, the verification module, which is trained with a ResNet network, jointly performs face classification and regression tasks to identifies a proposal to be real face or not and refine its location respectively. Experiments on OFD-UCI and our collected masked face dataset MAFA demonstrate that our approach effectively outperforms the three representative face detectors by at least 3.3% and 11.6% in detecting crowd occluded faces and masked faces respectively.

Na2BaMgP2O8phosphors were synthesized by a standard solid state reaction and their luminescent properties were investigated. The phase structure was analyzed by X-ray powder diffraction measurement. Under the excitation of 365nm, Na2BaMgP2O8:Tb3+, Eu3+phosphors show two color bands of green and red color due to5D4−7F5transition of Tb3+ions and5D0−7F2transition of Eu3+ions, respectively. The emission intensity of Tb3+deceased with the increasing concentration of Eu3+, which verified that an effective energy transfer occurred from Tb3+to Eu3+in Na2BaMgP2O8host. The present study indicated that the phosphors have a high potential application in solid state lighting.

Frequency stability of islanded smart distribution networks with peer-to-peer (P2P) controlled distributed generators (DGs) has attracted special attention recently. Using power line communication (PLC) technology, a multi-agent system (MAS)-based, decentralized, under-frequency load shedding (UFLS) scheme is investigated in this study for smart distribution networks with the communication constraint that each agent can only communicate with its neighboring agents. The proposed scheme uses the two-layer nearest neighbor consensus algorithm (NNCA) to overcome the drawback of the leader–follower consensus algorithm and guarantee the implementation of a decentralized UFLS. Based on the global information (i.e., the magnitude of the total active power imbalance) discovered in the first layer of the NNCA, the multi-stage UFLS can be executed by the second layer of the NNCA. Simulation results demonstrate that the proposed scheme can effectively implement the decentralized UFLS while maintaining the frequency stability of an islanded smart distribution network.

It is known that bulk acoustic wave (BAW) can increase the sensitivity of a metal oxide semiconductor gas sensor and decrease its lower detection limit by at least one order of magnitude. However, the structure of BAW metal oxide semiconductor gas sensors has been bulky and its energy efficiency has been low. In this work, a new structure to form BAW metal oxide semiconductor gas sensors is proposed and investigated. In the structure, the metal case which is usually used to protect the sensing resistor, is employed to excite the working ultrasonic-field. A piezoelectric ring is bonded onto the bottom of the metal case to serve as the vibration excitation source of the whole system. The structure not only makes the gas sensor system compact and miniaturized by reducing the volume and weight of vibration excitation unit by more than 99%, but also decreases power consumption of the ultrasonic transducer by more than 98% for the same ultrasonic effect. Meanwhile the ultrasonic system can enhance the sensing response of a commercialized SnO2 gas sensor by one order of magnitude.

Based on the current situation and development trend of domestic electric commercial vehicle, we research the mode and the key technology of battery quick change, analyze and compare the three common mode, then put forward a half automatic electric mode which is low cost and high flexibility, accordingly, it can adapt to the current electric car battery quick change development requirements, thus to provide a reference for battery quick change station infrastructure construction planning, and to provide the foundation to improve the battery quick change technology.

In the process of cellphone screen manufacture, equipment failures and human errors may lead to screen scratches. Traditional manual ways check scratches by human eyes, which often costs large manpower and time, but with poor effectiveness. To address this issue, we proposes an automated scratches detection method by cascading two main modules. First, the scratch filtering module detects big scratches and localizes small scratches candidates with a serial of low-level stages. Then, the scratches classification module applies a lightweight CNN model, ScratchNet, to identify each small scratch candidate whether it is real scratch or not. To train the ScratchNet, we build a Scratches on Cellphone Screen (SCS) dataset with 50K samples in 5 categories. The experimental results on SCS testing set show that the proposed method achieves an accuracy of \(96.35\%\) on classifying small scratches, which outperforms the LeNet model and the other classifiers.

From the Internet of Energy point of view, adjacent building combined cooling, heating and power with microgrids (BCCHP microgrids) should be interconnected, which will generate better benefits, both economically and environmentally, from scarce energy resources through intelligent coordination. The interconnected-BCCHP (IBCCHP) microgrids will not only support energy with each other but also bring better benefits compared to the non-interconnected-BCCHP (NBCCHP) microgrids. A mixed-integer linear programming (MILP) economic dispatching approach incorporating piecewise linear efficiency curves model is proposed to compares the performance of IBCCHP microgrids and NBCCHP microgrids. Three buildings with CCHP microgrids in Shanghai are studied as an example, and simulation results are presented to demonstrate that the primary energy consumption (PEC), the carbon dioxide emissions (CDE) and the operation cost of the proposed IBCCHP microgrids are better than the NBCCHP microgrids, and the overall lifetime of the complete battery is improved. Furthermore, a sensitivity analysis is presented to provide various policy recommendations to promote the interconnection among buildings with the change of electricity prices and gas prices.

According to the current situation of domestic passenger cars’ battery quick change equipment and its overall development trend, we first research on the key module technology of passenger cars’ battery quick change system. Then, this paper researches on network information control mode, internal dynamic management mode of the battery quick change system, radio frequency identification (RFID) and storage charging system mode, respectively. Thus, they can provide passenger cars’ battery quick change system with some references realizing the system’s network, intelligence and high efficiency.

According to the status of the china tobacco sorting system and equipment, this article provides an overall development trending of technology in sorting system and equipment based on the internet of things. Based on the research of key technologies in the high-speed automated sorting system and also the research of architecture mode, it can provide a reference for the overall system’s structure, capability, overall planning and the like through the network topology.

A new family of two-dimensional optical orthogonal code with zero cross-correlation zone (2-D ZCZ) is constructed, using primary LA sequence for time spreading and OCS (one-coincidence sequence) for wavelength hopping. Performances of autocorrelation and cross-correlation of 2-D ZCZ are analyzed. BER performance of two-dimensional OCDMA system is also analyzed.

A two-stage energy management strategy for CCHP microgrid is proposed in this paper. The first stage, named day-ahead scheduling stage (DASS), is an economic dispatch model to minimize the operating cost. This stage provides the operation plans including commitment or output power of each unit, which is solved by a revised particle swarm optimization. The second stage, called real-time adjusting stage (RTAS), deals with the fluctuations of power by adjusting the output power of the central air conditioning considering housing characteristics, and the batteries and other controllable units based on heuristic rules. RTAS is utilized to maintain the exchange power between the main grids and the CCHP microgrid constant. A typical CCHP microgrid is simulated to demonstrate the performance and effectiveness of the proposed energy management strategy.

The detection of dissolved gases in seawater plays an important role in ocean observation and exploration. As a potential technique for oceanic applications, Raman spectroscopy has already proved its advantages in the simultaneous detection of multiple species during previous deep-sea explorations. Due to the low sensitivity of conventional Raman measurements, there have been many reports of Raman applications on direct seawater detection in high-concentration areas, but few on undersea dissolved gas detection. In this work, we have presented a highly sensitive Raman spectroscopy (HSRS) system with a special designed gas chamber for small amounts of underwater gas extraction. Systematic experiments have been carried out for system evaluation, and the results have shown that the Raman signals obtained by the innovation of a near-concentric cavity was about 21 times stronger than those of conventional side-scattering Raman measurements. Based on this system, we have achieved a low limit of detection of 2.32 and 0.44 μmol/L for CO2 and CH4, respectively, in the lab. A test-out experiment has also been accomplished with a gas-liquid separator coupled to the Raman system, and signals of O2 and CO2 were detected after 1 h of degasification. This system may show potential for gas detection in water, and further work would be done for the improvement of in situ detection.

The increasing penetration of distributed generators (DGs) makes further interconnection of multiple energy carriers possible. In this paper, an integration model comprising IEEE-33 bus distribution network and a correspondingly designed water system is proposed with DG coupling of thermal and electrical energy. The integrated energy system incorporates the constraints of the distribution network, water system, and DGs and ensures that the electrical and thermal demands of the integrated energy system are satisfied simultaneously by energy flow. An optimal procedure including optimal sizing, positioning, and operation of DGs and gas boilers in a given system was implemented while minimizing the total costs of investment and operation. The model constitutes a mixed integer nonlinear problem, as it takes the power flow calculation and the nonlinear characteristics of DG efficiency into consideration. Therefore, the particle swarm optimization algorithm is employed to simulate the proposed model. The simulation results in conjunction with numerical studies reveal the impact of the water system on the optimal configuration of DGs and the advantages of the integrated energy system on economic considerations, voltage level, and net loss.

Prostate cancer stem-like cells (PCSLCs) are considered to be the ‘seed’ of prostate cancer. The aim of this study was to confirm that the PC-3 cells, which we isolated and enriched from PC-3 cells through magnetic bead cell sorting (MACS) and serum-free medium (SFM) culture, were PCSLCs. Combinations of MACS, flow cytometry (FCM), SFM and immunocytochemistry (ICC) were used to ensure the positive expression of CD133 and CD44 on PC-3 and sphere-forming cell membranes. Self-renewal, multi-potential differentiation, unlimited proliferation and permanency assays were also applied to indentify whether the PC-3 cells exhibited the characteristics of cancer stem cells (CSCs). As a result, there was a low proportion of PCSLCs in the PC-3 cells. In the FCM assay, the proportion of cells expressing CD133 or CD44 in the PC-3 cells was 0.51 and 0.31%, respectively. In addition, we found that the proportion of PC-3 cells sorted by MACS that expressed CD133 was significantly increased compared with that of the sphere-forming cells cultured in SFM (99.09 vs. 84.80%, P0.05). The expression of PAP and AR as detected by western blot analysis of induced PCSLCs was significantly increased compared with that of uninduced PCSLCs (P

The query privacy issue in location-based services (LBS) has recently received considerable attention. To protect the query privacy of users, current state-of-the-art techniques adopt cloaking which cloaks the sender's location to k-anonymized spatial region (ASR), such that an attack based on the sender's location cannot identify the query source with probability larger than 1/k, among other k-1 users. However, these techniques do not consider that these k-1 additional mobile users with different privacy requirements may send request at the same time. In this paper, we propose a new attack model that an adversary who observes all the ASRs at one time can identify the query source with probability larger than 1/k based on prior knowledge of the locations of all users. And we propose our two algorithms, namely, Basic and Adaptive Algorithms, which strengthen the privacy guarantee to defend such inference attack while still support personalized user privacy requirements. We verify the effectiveness of the proposed algorithms by experiments on location data which synthetically generated on real road maps.

Recently, several techniques have been proposed to preserve the user location privacy in road networks. To protect the location privacy of users, many of the existing techniques adopt cloaking which blurs the actual location into a set of segments. But these techniques do not take the weight of the segments into account in the situation of road networks. In this paper, we propose a new attack model that an adversary can infer the true location of the requester according to the weight distribution with a certain probability. And we also design an approach to solve this problem, which can guarantee better distribution of the weights in the cloaked set. The experiment result shows the effectiveness and feasibility of our algorithm.

Recent years, several techniques have been proposed to preserve the user location privacy in road networks. Some of the existing techniques may suffer replay attack, and leads to leaking the user location privacy. The existing technique to resist replay attack is add a randomize step into the cloak process, which may be unquestioning. In this paper, we propose a grouping-based method to prevent replay attack by dividing the segment set to form cloaked set. The experiment result shows the effectiveness and feasibility of our algorithm.

when mobile users search points of interest from location based service, they always should provide their accurate locations, which may leak users' location privacy. To protect users' location privacy, some two-tie approaches have been imposed. However, existing approaches are either hard to implement or may lead to high communication cost. In this paper, we present a novel approach, called Guess-Answer. Through the interaction between the user and the server, a cloaked region, which satisfies the user's personal privacy requirement, can be generated. The experimental results show that our approaches are scalable and robust.

Medium voltage power system applying on the shipboard of the reliability presents the new requirement. Because of the inertia technical reasons, the shipboard grounding of medium voltage generator has been used the landway-high resistor grounding, it has not been in-depth study. This paper through simulation and numerical analysis to make sure the appropriate grounding method of medium voltage generator used on shipboard, as the same time, in order to supply reference of the ship grounding device

In view of the motion process simulation and path planning for mechanic arm, this paper presents a visualized simulation method based on inverse kinematics. Based on Inverse kinematics of multi-joint mechanic arm and 3D simulation model technology, the whole motion process simulation has been solved, which can provide reference standards for the mechanic arm selection and the motion planning in automatic sorting system design.

Two spectroscopic instruments, a 1/4 meter Ebert-Fastie spectrometer and a Michelson interferometer, were flown aboard the NCAR Electra during the ALOHA-90 campaign. These instruments were designed to carry out measurements of the brightnesses and rotational temperatures of the O2 Atmospheric (0–1) band and several OH Meinel bands simultaneously with sodium lidar observations. The spectrometer results taken during the nights of March 25 and March 31 are reported. Wave-like oscillations in both O2 and OH airglow brightnesses were clearly present during both flights and a strong positive correlation of OH band brightness with sodium column density was found. A slightly weaker correlation of O2 band brightness with sodium column density was also detected. The measured brightness ratio between the OH (7–4) and (7–3) bands is found to be 11.2±0.3, which is in good agreement with the calculated ratios of 12.8 by Mies [1974] and 10.2 by Langhoff et al. [1986].

Thirty children with daytime urinary frequency (DUFC) were randomly divided into two groups. The first group was given oral indomethacin, 1 mg/kg per dose, three times a day for 7 days. The frequency of urination fell from 26.3 +/- 2.3 to 11.7 +/- 2.8 at the end of the treatment period. The second group was treated with oral chloramphenicol, 30 mg/kg per day, in four divided doses for 7 days. Their urinary frequency did not change. They were then treated with a 1-week course of indomethacin and responded in a similar manner to group 1 patients. The use of indomethacin was accompanied by significant increases in plasma carbon dioxide-combining power, urine osmolality and urine pH; urine sodium and potassium levels decreased. These results show that a short course of indomethacin, in low dose, improves DUFC. We propose that it acts by inhibiting prostaglandin synthesis which modifies tubular function and increases urinary pH.