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The transistor is the core component of the amplifying circuit. It can control the conversion of energy and amplify any small changes in the input without distortion. In the Multisim simulation environment, this article verifies the static operating point, voltage amplification and other performance parameters of the single-tube amplifier circuit according to the given circuit, and observes the nonlinear distortion phenomenon of the amplifier circuit.

Automotive electrical equipment can be summarized into three major components: power supply system, electrical equipment, and intermediate devices. For any electrical equipment and electronic control device to obtain power supply, the connection of intermediate devices is essential. The generator is the main power source of the car, and its function is to supply power to all electrical equipment (except the starter) when the engine is running normally (above idle speed), and to charge the battery at the same time. In this paper, by modeling the circuit of low-voltage electrical system of trams and designing and realizing the circuit model of low-voltage electrical system, this article masters the basic knowledge of circuit analysis, is familiar with the general process of circuit design, and can solve the general circuit faults in debugging.

In heating, ventilating and air conditioning (HVAC) systems, sensor faults cause improper control strategy resulting in both energy penalty and service costs. As the first and foremost step of an entire on-line fault-tolerant control strategy, sensor fault detection is crucial for maintaining system operation performance. Principal component analysis (PCA) is a widely studied sensor fault detection method in HVAC area. However, the noise information contained in field sensor measurements may confuse the PCA model training process and deteriorate the sensor fault detection performance. To address the problem, this study presented an enhanced PCA-based sensor fault detection method using ensemble empirical mode decomposition (EEMD) denoising. The proposed EEMD-PCA method includes two steps: (1) EEMD is adopted to decompose and denoise the raw measured data, namely eliminating the possible noise information and extracting the possible useful information; (2) PCA is employed to establish the Q-statistic with threshold for sensor fault detection. A case study on a real screw chiller system was conducted to validate the proposed method. 11 sensors were chosen for modeling by qualifying chiller energy performance. Field operating data were used for validation with various magnitudes of added biases. Results revealed that EEMD-PCA showed better detection performance than PCA for 8 critical sensors. The fault detection ratios of the 8 sensors were increased by 22% at average. After pre-processed by the EEMD method, the denoised data were smoother than the raw data. Data distributions in the PCA residual subspace indicated that EEMD-PCA is sensitive to the smaller biases since the denoised data can separate faulty data from the normal data.

Space-based optical (SBO) sensor is a special kind of star sensor, which can be used in not only the space-based observation for space objects but also satellite navigation, called optical navigation in this paper. The aim of this paper is to design optical navigation strategy for low earth orbit (LEO) satellites, including the selection of beacon objects, and the sensor scheduling for improving navigation accuracy. The processing and observation geometry of space-based optical observation is illustrated first. Based on the characteristics of the space-based optical observation, the distribution of beacon geosynchronous orbit satellites is designed for optical navigation of LEO satellites. And the sensor scheduling criterion for optical navigation is deduced to analyze the characteristics of optical navigation and schedule the navigation mission. It is found that an efficient way to improve the optical navigation accuracy is to add an additional sensor to track other beacon objects, whose line of sight (LOS) should be perpendicular to the original LOS of the sensor as far as possible. Therefore, global positioning system satellites are used as the supplement of beacon objects. Finally, based on the sensor scheduling criterion, navigation for a LEO satellite with two SBO sensors is scheduled, which obtains more accurate results and converges more rapidly.

This paper presented a chiller sensor fault detection method based on Empirical Mode Decomposition (EMD) threshold denoising and Principal Component Analysis (EMD-TD-PCA). EMD-TD-PCA was developed to remove noise in the original data set so as to improve fault detection efficiency for temperature sensor bias faults. First, EMD was used to enhance the data quality by removing the noise contained in the raw data set. A threshold denoising way was employed since it only eliminated noise but maintained the useful information. Second, the data processed with EMD threshold denoising were used to build the PCA model. The Q-statistic was adopted to detect the sensor faults. The proposed method was compared with the traditional PCA method. The operational data of a screw chiller system in an electric factory were used to evaluate this method. Results show that the EMD-TD-PCA method can effectively improve the fault detection efficiency especially for temperature sensor faults with introduced biases ranging from −2°C to +2 °C at the 0.5 °C interval, and also there will be certain differences in fault detection efficiency for different sensors.

Sensor failures can lead to an imbalance in heating, ventilation and air conditioning (HVAC) control systems and increase energy consumption. The partial least squares algorithm is a multivariate statistical method, compared with the principal component analysis, its compression factor score contains more original data characteristic information, therefore, partial least squares have greater potential for fault diagnosis than the principal component analysis. However, there are few studies based on partial least squares in the field of HVAC. In order to introduce partial least squares into the field, based on the partial least squares fault detection theory, a fault analysis method suitable for this field is proposed, and the RP1403 data published by ASHARE was used to verify this method. The results show that on the basis of selecting the appropriate number of principal components, partial least squares have the ability to diagnose the fault of the chiller sensor. With the known fault source, partial least squares regression, a method with better data reconstruction accuracy than principal component analysis, is used to repair the fault. Finally, the purpose of fault identification can be achieved.

This study was to explore the effect of computed tomography (CT) images processed by image edge detection technology based on the improved Canny algorithm in the diagnosis of stroke patients with mobility dysfunction and to evaluate the clinical application value of early rehabilitation nursing (ERN). 114 patients who were diagnosed and treated in hospital and were suspected of having stroke movement dysfunction were selected as the research objects, and they were randomly divided into two groups, each with 57 patients. Patients in the control group were diagnosed with conventional CT examination, and the patients in observation group were diagnosed based on the CT images processed by the image edge detection technology based on the improved Canny algorithm. Patients in the observation group were divided into a group C and a group O. Patients (27 cases) in group O received rehabilitation training within 3 days after their vital signs were stabilized, and patients (30 cases) in group C received rehabilitation training within 3∼7 days after their condition was stabilized. The CT image diagnosis effects on patients of the control group and the observation group were analyzed, and the ERN effect on patients of the C group and the O group was compared. The results showed that the mean square error (MSE) of the improved Canny algorithm (233.78) was smaller than that of the traditional Canny algorithm and Sobel and Prewitt algorithm, and the peak signal-to-noise ratio (PSNR) (27.89) was greater than that of the traditional Canny algorithm and Sobel and Prewitt algorithm ( P < 0.05 ). Compared with the control group, the sensitivity (85.00% vs. 62.12%), specificity (70.59% vs. 36.36%), and accuracy (80.70% vs. 54.39%) of the examination method of the observation group were much higher ( P < 0.05 ). In addition, the total effective rate of patients in group O was 89.47%, which was greatly higher than that of group C (70.18%), and the scores of Meyer index and Barthel index were also higher in contrast to those of group C ( P < 0.05 ). In conclusion, the improved Canny algorithm showed a clearer display on the edge detection of CT images and good application effect. It showed the effect of making conventional CT more accurate in the examination and diagnosis of stroke patients, and it was worthy of clinical application and promotion. The research showed that the timelier rehabilitation training, the better the treatment effect of patients.

Engineering education model of CDIO is the latest achievement of international engineering education reform. CDIO focus on cultivating the engineering consciousness of the students.It is a major challenge to achieve the complete class teching in so less class hour casued by the adujusting of the professional talent cultivating plan. This paper introduces education reform of electrical engineering and electronics based on CDIO. It takes the three-person answering circuit as an example to design the teaching mode of CDIO.

One of the most fundamental qualities for science research and technology development is the innovation quality, which covers many aspects such as consciousness, method and spirit. The innovation quality developed in the undergraduate period would be the most basic foundation in their over forty-years working life. A supplemental cultivation plan with a routine consisting of hardware, programming, simulation and optimization was presented in this paper for the undergraduates of Building Environment and Energy Engineering (BEEE). The hardware manufacture of digital devices was trained to promote the freshmen manipulative capability, while software programming with general-purpose languages was introduced to enhance the comprehension of data analysis in the 2nd academic year. During the period when some professional courses were taught, the building simulation modular could be introduced to enhance the understanding for the building thermal process and its corresponding Heating, Ventilating, Air-conditioning and Refrigeration (HVAC & R) system. A building management system (BMS) would be developed step by step and interactive with virtual building platform. That BMS and its virtual cases could be employed as the testbed for optimization of HVAC & R system, such as energy consumption prediction, fault detection and diagnosis. Python and EnergyPlus were used in the custom training program oriented to the employment direction for the system operation and maintenance. Group achievements and individual cases show that the presented supplemental cultivation plan played a very important role in the cultivation of BEEE graduates’ innovative quality. The effect for the graduated students need pay more attention to investigate the long-term effects, and more volunteers will participate in this plan to find out their better future.

Based on the comprehensive geological reservoir research on conglomerate reservoirs, the research contents and methods of a heterogeneous geological model for conglomerate reservoirs were systematically summarized. The model has been built making full use of seismic data, geologic data, drilling data, logging data, test data, etc. based on sedimentary facies modeling and property modeling. The model finely characterizes the transverse and longitudinal distribution characteristics of oil and gas in the field. The application of the above technology has provided effective guidance to new well optimization. 3 existing platforms have been optimized and utilized for the conglomerate gas reservoir, and 3 cluster inclined wells for conglomerate strata have been optimized, with the average single-well daily gas production of 6*104m3 and the daily condensate oil production of 56 barrels per day. A breakthrough has been made in reservoir fracturing in the conglomerate reservoirs. Reservoir fracturing has been implemented in 2 wells for the conglomerate oil reservoir, the daily oil production has been increased by 263 barrels per day, and the daily gas production has bee increased by 3.9*104m3.

Since the outdoor and indoor load conditions always change continuously, it may lead to the various operation characteristics in chillers. If a single principal component analysis (PCA) model is applied to detect and diagnose sensor faults under a wide range of chiller operation conditions, the various operation characteristics may confuse the sensor fault analysis process in two aspects. One is that the single PCA model may not be sensitive enough to detect less server faults due to the inadaptable and fixed fault detection boundary; the other is that the single PCA model can hardly distinguish some new normal operation conditions from serious sensor faults since it is impossible for a training PCA model to cover all chiller operation conditions. To overcome the limitations, this study proposed an improved sensor fault detection, diagnosis and estimation (FDD&E) method combining density-based clustering with PCA. Clustering analysis, i.e., the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) can automatically classify operation data into clusters and recognize the corresponding operation conditions. Instead of a single PCA model, using sub-PCA models to describe each normal operation condition improved the sensitivity and reliability of fault detection and diagnosis as well as the accuracy of sensor fault estimation. The proposed method was validated using field operation data of an existing screw chiller plant while various sensor faults of different magnitudes were introduced. Results reveal that the proposed method shows better sensor FDD&E results than conventional PCA-based sensor FDD&E method using a single PCA model.

Building heating/cooling consumption prediction is of great importance for HVAC system management tasks, such as optimal operation/control strategies, demand and supply management, abnormal energy diagnosis, etc. Compared to traditional methods, data-driven methods have received a lot of attention due to their flexibility and efficiency. In particular, this paper investigates the potential of data partitioning techniques in improving prediction performance of ultra-short-term building heating load prediction. Specifically, with three proposed statistical attributes of 32 days considered by clustering analysis, similar daily operation patterns of pumps (OPPs) in a water-source heat pump system (WSHPS) were identified stepwise. Afterward, the sub-models based on different OPPs were developed by machine learning methods and their performance were compared to the general model without data partitioning. In additional, an operation tree was constructed to predict daily OPPs based on historical weather conditions and available date information. With the assistance of the operation tree, the proposed method can be applied in online prediction. Based on the validation, it can be concluded that the introduction of OPPs-clustering can improve the performance of building heating load prediction.

This paper proposes an improved decision tree (DT)-based fault diagnosis method for practical variable refrigerant flow (VRF) system. The proposed method is a three-stage method combining DT model with virtual sensor-based fault indicators (FIs). First, FIs are developed based on the virtual sensor (VS) theory for VRF faults, i.e., condenser air-side fouling (Fouling), refrigerant undercharge (RU) and overcharge (RO). Second, FIs are employed as additional input variables to induct a DT-based classification model classification and regression tree (CART). Third, the FIs-CART classification model is used to diagnose on-line data. Validation is conducted using two different datasets, the experimental testing dataset and the on-line testing dataset. Results indicate that the method correctly isolates the three faults i.e., Fouling, RU and RO. The improved DT method is also compared with three tree-based data-driven methods including CART, random forest (RF) and generalized boosted regression (GBM). Comparative results reveal that the proposed method has better fault diagnosis performance for both the experimental and the on-line testing datasets.

A fault detection and diagnosis (FDD) strategy is critical for the refrigerant charge amount (RCA) fault since improper RCA may affect the operational performance of a variable refrigerant flow system. The author’s former work proposes a FDD strategy for the RCA fault. However, three aspects of the former FDD strategy need improvement, i.e. model performance evaluation, more feature information preservation and fault diagnosis accuracy (FDA), especially for the undercharge fault. Firstly, with regard to the model performance evaluation, the concept of a confidence space is proposed to evaluate the FDD model. Secondly, principle component analysis (PCA) is used to reduce the dimension of all feature variables to improve the computational efficiency while preserving almost all feature information. Finally, in order to improve the FDA for the undercharge fault, a dual neural network model for the RCA fault diagnosis strategy has been adopted. The results show that a confidence space can effectively reflect the reliability of fault diagnosis, and the PCA reduces nearly half of the dimension while preserving more than 97% of the feature information, more importantly, the dual neural network improves the correct classification ratio (CCR) more than 9% for three classes (undercharge, normal charge, overcharge), with CCR for the undercharge fault improving by 26.8%.

Autonomous absolute orbit determination (OD) for a satellite constellation using only inter-spacecraft measurements has considerable value in space systems engineering. This paper proposes a novel solution for cooperative autonomous OD for a system comprised of an arbitrary number of spacecraft, with inertial angles-only measurements. We establish a link between observability and a graph representation of measurements among spacecraft. For a system with a beacon or without beacons, it is shown that if a measurement graph topology has graph theoretic equivalence properties with a two-level tree form, the system is second-order and third-order locally weakly observable, respectively. To solve the distributed OD problem, the new interleaved distributed extended Kalman filter (IDEKF) is proposed. Several evaluation cases are designed to demonstrate the influence of measurement topology, number of spacecraft, and orbital configuration on the degree of system observability. Finally, the performance of the IDEKF estimation is illustrated for a ten-spacecraft constellation.

The outdoor air-side fouling fault is almost inevitable for the practical variable refrigerant flow system. Fouling fault grows gradually and naturally causing increasingly energy penalty and performance degradation to the system. Implementing a proper and reliable fault detection and diagnosis strategy is crucial for practical systems keeping away from fouling fault and maintaining optimal operations. However, traditional model-based and data-driven methods cannot work in practical systems due to the lack of critical sensors and interpretation for model reliability, respectively. Therefore, this study proposes a data mining approach to identify and isolate fouling faults using only built-in sensors. Density-based spatial clustering of applications with noise(DBSCAN) is used for data pre-processing. The classification and regression tree(CART)-based classifier is employed for fault detection. Based on Pearson’s correlation analysis, a multiple linear regression(MLR)-based fault indicator is developed for fault isolation. A case study on a 29.8 kW cooling capacity R410A variable refrigerant flow system is used to validate the proposed strategy. Four levels of foulings are experimentally investigated under three cooling conditions. Results reveal that the proposed strategy correctly identifies 98% of fouling data using only three built-in sensor measurements. It also isolate fouling data from both normal and refrigerant charge fault data.

Sensor faults of air conditioning systems are harmful to optimal control strategies and system performance resulting in poor control of the indoor environment and waste of energy. In order to improve the fault detection and diagnosis (FDD) performance, this paper presents an enhanced sensor fault detection and diagnosis method based on the Satizky-Golay (SG) method and principal component analysis (PCA) method for the VRF system, namely SG-PCA method. Due to the volatility of the original data set of VRF system, the original data are smoothed using SG method at first. Then, the smoothed data are used for PCA model training and fault detection and diagnosis. In order to determine parameters of the SG method, an optimization index is proposed, which is calculated by the signal to noise ratio (SNR), the standard deviation (SD) and the self-detection efficiency. This SG-PCA method for VRF system sensor FDD is validated using field operation data of the VRF system. Various sensor faults at different fault levels are introduced. The results have showed that the SG-PCA method can significantly improve the fault detection and diagnosis performance compared to conventional PCA method.

Developing fault detection and diagnosis (FDD) for the variable refrigerant flow (VRF) system is very important for saving energy and improving reliability of the equipment. For indoor unit (IDU) faults of VRF system, it is especially necessary to detect faults and identify which IDU is faulty. Therefore, this paper has proposed a fault detection strategy based on modularized PCA method, which cannot only detect faults but also specify the faulty IDU. Fault detection models are established respectively for outdoor unit (ODU) and IDUs of VRF system using the modularized PCA method. Then, the expert-based multivariate decoupling strategy with six variables for VRF system is developed to isolate faults. Four common faults are taken into account for VRF system, which include two IDUs faults (electronic expansion valve fault and IDU air-side fouling), one ODU fault (reversing valve stick) and one system fault (refrigerant undercharge). The proposed FDD strategy is evaluated by experimental data of four faults. The test results have shown that modularized PCA-based fault detection method and rule-based diagnosis method are effective for the four typical faults in VRF system. Therefore, it is quite suitable for FDD of VRF system.

A proper refrigerant charge amount (RCA) is critical for a variable refrigerant flow (VRF) system since RCA may affect the operational performance. However, there were few studies of RCA fault for the VRF system in the open literature. Therefore VRF systems are calling for a fault diagnosis strategy. This paper develops a highly efficient fault diagnosis model (FDM), which employs the ReliefF algorithm for feature ranking (FR) and applies the neural network for fault diagnosis. Firstly, the artificial neural network (ANN) model is built on the N-best features data subset and optimized by the Bayesian regularization algorithm. Secondly, the model is verified by testing data subset, the correct diagnosis rates (CDR) using the N-best features data subset can be obtained. The optimal FDM is selected in consideration of CDR and the computational efficiency. Finally, optimal FDM is further optimized by selecting the best hidden neurons. The results show that the CDR of the FDM based on 6-best features is sufficiently high in comparison to the CDR achieved when 22 features are used, while the training time decreases by 98.8%.

Variable refrigerant flow systems account for a considerable portion of energy consumption in buildings. In order to improve the energy efficiency and estimate the energy saving potentials of variable refrigerant flow systems this study proposes a data mining based method to identify and interpret the power consumption patterns and associations. Two descriptive data mining algorithms, clustering analysis and association rules mining, are used for data partitioning and association mining. The proposed method consists of four phases: data pre-processing, data partitioning, data association mining and knowledge interpretation. Experimental data collected from a tested variable refrigerant flow system in the standard psychrometer testing room are pre-processed and prepared to examine the proposed method. Three time independent influential factors: part load ratio, refrigerant charge level and cooling condition are analyzed. Results show that the method is able to help identify energy consumption patterns and extract energy consumption rules in variable refrigerant flow systems. Three distinct energy consumption patterns are identified: undercharge fault, low and high part load ratio conditions. For compressor operation frequency switch control and refrigerant undercharge patterns, the energy saving potentials could be estimated by making comparisons between energy patterns and rules in a top-down way.

Along with production of a condensate gas field, new infill wells are needed during the period of late development. How to determine the suitable new well location is an important topic. At this time, a lot of reservoir description has been done. Structures, horizons, sedimentary distribution, porosity, permeability and geological model were already completed. The key problems to be solved are to integrate all above datasets to screen and classify the different levels of reservoir distribution. We summarized six properties (sedimentary micro-facies type, connectivity, cumulative thickness of sand, ratio of sandstone thickness to formation thickness, porosity and permeability), determine each weighting factor of each property, then this reservoir evaluation system is divided into four grades according to the total score. According to the above prospective reservoir division result and the research on remaining oil and gas distribution, 18 wells have been deployed optimally and developed progressively; in recent two years, six wells have been implemented on site, and the initial production has reached 150,000 m3 per day.

N gas field is one of the main gas suppliers with an oil-rim at the slope in the south Sumatra basin. Complicated by high CO2 in the faulted condensated reservoir, the distribution of gas, oil and water, and residual fluid were not easy to explore, which is essential for drilling future wells. This paper builds a four-step workflow to determine the fluid distribution to obtain maximum production. The first step in our workflow was to finely delineate the faults by the techniques of coherence and dip detection, which built basis for oil–gas compartment division. Second, petrophysical modeling was carried in geology software platform to compute the properties. The reservoir and fluid cutoffs can be cross-plotted between measured properties for the non-core wells. Then the fluid systems were delineated in vertical and areal directions according to DST and fluid cutoffs. The vertical and areal CO2 samples were finely compared to determine the compartment boundaries in consideration of fault combinations. Finally, the residual oil and gas potential were simulated after history match in well and field level. Five proposed wells have been drilled and gotten the 5% increase compared with the planned rate production. The result showed this workflow is useful for improving production and field performances.

Degradation occurs in a VRF system after years of operation due to refrigerant leakage, mechanical failure or improper maintenance. VRF systems require approaches to detect faults and sustain its normal operation. This paper proposes a creative statistical method to detect the refrigerant charge faults in VRF systems, which is based on principal component analysis (PCA) and exponentially-weighted moving average (EWMA) control charts. The EWMA model is built with the residual vector of the PCA model. Data of the experimental VRF system is used to validate the advantages of the PCA–EWMA method. Results show that the combined use of PCA and EWMA methods can achieve better fault detection efficiency than PCA based T2-statistic and Q-statistic methods at low fault severity levels. The robustness of the PCA–EWMA method in online fault detection is verified using the data from different type of VRF systems.

Detecting the faults at the incipient stage is important for keeping chiller systems healthy and saving energy and maintenance cost. Traditional principle component analysis (PCA) and support vector data description (SVDD) methods are insensitive to two common faults, condenser fouling (CdF) and refrigerant leakage (RfL). To improve the fault detection performance, this study proposed a PCA-R-SVDD based method. Instead of principle component subspace (PCs), it develops a SVDD model in the residual subspace (Rs) using the PCA modeling residual data. The SVDD based distance based monitoring statistic was used for fault detection. The proposed method shows significant improvement comparing with the traditional methods due to the better fault data distribution and tighter monitoring statistic. It is sensitive to six common faults. At least 50% of the fault data can be correctly detected even at the least severe fault level. Centrifugal chiller experimental data from the ASHRAE Research Project 1043 (RP-1043) was used to evaluate the methods.

This paper presents an algebraic solution of erroneous sensor's undetectable boundary to evaluate the sensitivity of chiller sensor fault detection based on principal component analysis. Q-statistic of PCA is normally applied as a collective statistical index to detect sensor fault by comparing its value with the threshold. However, Q-statistic has no specific physical meaning and cannot evaluate the sensitivity of the provided method for sensor fault detection. We analyzed the definition of Q-statistic and derived the numerical value of the minimum range not to detect sensor fault. Bias sensor fault of a fielded screw chiller was studied for each sensor in PCA model by introducing different severity levels. Results showed that each sensor has different fault detection sensitivity using the same PCA model. The undetectable boundary can be a criterion used to evaluate the detection sensitivity of PCA-based method easily.

Lean-stratified water injection is one of the most important technologies to increase production and develop potentials for the oilfield with extreme high water content. However, traditional models cannot entirely solve the inner boundary conditions of lean-stratified water injection. Therefore, we established the injection wellbore constraint equations, which were coupled with the oil/water two-phase numerical reservoir models, and then the seven diagonal form sparse coefficient matrix was solved by block precondition of generalized minimal residual algorithm. Considering the specific situation of lean-stratified water injection wells, reservoir geology and production schemes of the middle part of the sixth Oilfield in Xing Shugang, three mechanism models of multi-layer heterogeneous reservoir were constructed to simulate the lean-stratified water injection. The influences of different segments numbers, modes of combination in segment layer and rhythm characteristics of remaining oil reserves and distribution are evaluated.

A proper refrigerant charge amount (RCA) prediction algorithm is important to air conditioning systems. In variable refrigerant flow (VRF) systems, the traditional virtual refrigerant charge (VRC) sensor models perform well at undercharge situations but produce large prediction errors at overcharge situations. When the refrigerant charge level (RCL) is over 90%, the correlation coefficient data-based method was introduced to select the additional input variables to modify the VRC models. Two data-based algorithms, multiple linear regression (MLR) and non-linear support vector regression (SVR), were used to improve the prediction performance. The prediction performance of the pure SVR model was also compared. Results reveal that the overall prediction errors for SVR based modified VRC model (SVR-VRC) is 5.53%, the minimum among the four models. The SVR-VRC model improves the VRC models and extends the application in the VRF system when only the system self-provided sensor measurements are used.

This paper presents a statistical training data cleaning strategy for PCA-based chiller sensor Fault Detection, Diagnosis and Data Reconstruction method. Finding and removing outliers from the original training data set, the training data quality can be improved by the presented data-cleaning strategy. This can enhance the efficiency of the fault detection and increase the accuracy of the data reconstruction. Outliers cannot be easily found in the original data set used for training the PCA model. These outliers would severely affect the projection directions of PCA's two orthogonal subspaces (PC subspace and Residual subspace). Therefore, the threshold of Q-statistic is changed by the unexpected projection subspaces so that the detection efficiency of the sensor fault is decreased. The Euclidean distance was employed as an index to detect outliers from the original training data. In order to achieve optimal training data for the sensor FDDR, the z-scores of each sample's Euclidean Distance were employed as the key to remove the outliers. A field measured data set of a screw water-cooled chiller was used to validate the presented strategy. Results demonstrate that the quality of the training data is optimized and sensor fault detection efficiency, as well as the reconstruction data accuracy, is improved when compared to the normal PCA method.

Thermal discomfort is a widespread problem in the built environment, due in part to the variability of individual occupants' thermal preferences. Personal comfort systems (PCS) address this individual variability, and also enable more energy-efficient thermal conditioning in buildings by reducing the need for tight indoor temperature control. This study evaluates a novel approach to PCS that leverages the time-dependence of human thermal perception. A 6.25 cm2 wearable device, Embr Wave, delivers dynamic waveforms of cooling or warming to the inner wrist. In three thermal comfort tests conducted in a climate chamber with N = 49 subjects and temperatures between 20 and 28 °C, the device exhibited a corrective potential of 2.5 °C within 3 min for both warm and cool populations, while consuming ~1 W of power. The effect is even more pronounced (corrective potential up to 3.3 °C over periods of 3- and 45-min) when subjects are given control of the device's operation. Subjects are found to optimize the device settings for pleasantness, not for the intensity of sensation. These results indicate that this low-power, wearable device improves whole-body thermal sensation, comfort, and pleasantness. It is an appropriate tool for addressing the problem of thermal discomfort in moderate indoor environments.

China’s urban rail transit (URT) construction is coming into the stage of rapid development under the guidance of national policies. However, the URT construction projects belong to high-risk projects and construction safety accidents occur frequently. Presently, safety risk management is in continuous development. Unfortunately, due to risk data deficiencies and lack of relationship between participants and safety risk factors, most of the research results cannot be well applied to URT projects. To overcome the limits, this paper has applied the text mining method into safety risk analysis. Through word frequency analysis and cluster analysis, 15 safety risk factors and 3 participants are identified from 156 accident reports. In addition, the accident descriptive model has been established, which is composed of indirect safety risk factors (management defects), direct safety risk factors and participants. In this model, each accident is the standardized description of the corresponding accident information. This is useful for risk data accumulation and analysis. Then the network structure analysis and risk assessment methods are utilized to make clear 63 relationships among participants, management defects and direct safety risk factors. Subsequently, the risk value of each relationship is evaluated. These safety risk information is integrated into the accident descriptive model by using accident points. Finally, ABC analysis which is a popular and effective method used to classify items into specific categories that can be managed and controlled separately is used to analyze the safety risk management’s core process(A), important process(B) and general process(C) in the accident descriptive model. The research results show that the constructor should pay attention to construction coordination, safety specifications, safety measures and personnel education, the supervisor should attach importance to timely communication, the monitoring unit should pay attention to advanced forecast and dynamic control. The main research contributions are as follows: (1) A method of obtaining risk data from unstructured content has been provided; (2) The accident descriptive model could be utilized for risk data continuous accumulation; (3) The emphases of URT construction safety risk management are made clear.

Most of major oilfields in China have reached high water cut stage, but still, they contribute to more than 70% of domestic oil production. How to extract more oil from mature oilfields has become a hot topic in petroleum engineering. Carbon dioxide flooding is a win–win strategy because it can enhance oil recovery and simultaneously reduce CO 2 emissions into the atmosphere. In order to evaluate the potentials of CO 2 flooding in high water cut oil reservoirs, various 3-D heterogeneous geological models were built based on Guan 104 fault block in Dagang Oilfield to perform reservoir simulations. The D-optimal design was applied to build and verify the Rapid Assessment Model of CO 2 flooding in high water cut oil reservoirs. Five quantitative variables were considered, including average horizontal permeability, permeability variation coefficient, ratio of vertical to horizontal permeability, net thickness of formation and percentage of recoverable reserves by water flooding. The process of weighting emphasized the contributions of linear terms, quadratic terms and first-order interactions of five quantitative parameters to improved recovery factor and Net Present Value of CO 2 flooding. Using the Rapid Assessment Model of CO 2 flooding in high water cut oil reservoirs, significant first-order interactions were sorted out and type curves were established and analyzed for the evaluation of technical and economic efficiency of CO 2 flooding in high water cut oil reservoirs. Aimed at oil reservoirs with the similar geological conditions and fluid properties as Guan 104 fault block, the Rapid Assessment Model and type curves of CO 2 flooding in high water cut oil reservoirs can be applied to predict improved recovery factor and Net Present Value of water-alternating-CO 2 flooding at different conditions of reservoir parameters and development parameter. The approach could serve as a guide for the application and spread of CO 2 -EOR projects.

The prevalence of camera-equipped smartphones and optoelectronic displays opens up a novel framework for wireless communication-optical camera communication. With displays as transmitters and cameras as receivers, a reliable optical communication link is established. In practice, the frame synchronization between transmitters and receivers is an inevitable challenge confronted in this new communication mode, whereas few previous studies are involved in this issue. Thus, we dedicate to model the frame synchronization issue, discuss the phenomenon that the receiver might lose frames or capture mixed frames, and propose frame loss detecting algorithm based on statistic characteristics of difference frames, while maintaining perceptual transparency. Our preliminary experiments have confirmed the validity of the theoretical analysis and the proposed algorithms.

This paper considers low-complexity transmit beamforming schemes for signal-to-interference-plus- noise ratio (SINR) balancing problem in the Gaussian multiple-input single-output multi-receiver wiretap channel (MISO-MRWC). The formulated max-min-fair problem are shown to be non-convex. To overcome the computational complexity, we develop the leakage-based and zero forcing (ZF) based beamforming algorithms for finding the local optimum transmit beamformers. Extensive simulation results illustrate that the proposed beamforming algorithms achieve low computational complexity without serious performance deterioration.

This paper presents a novel iterative scheme of maximum-likelihood (ML) sequence estimation in the absence of timing information. The ML estimates of the symbols are obtained from the oversampling samples of the matched filter output without timing estimation or sample interpolation, eliminating the need for a separate synchronizer. With unsynchronized samples, the detection problem is treated as ML estimation from incomplete data, and the expectation-maximization algorithm is applied to find an iterative solution. The proposed scheme is compared with conventional techniques under different modulation orders. The simulation results indicate that acceptable bit error rate performance can be achieved for short package transmission.

This paper presents a self adaptive chiller sensor fault detection strategy based on Principal Component Analysis (PCA) method, namely a self-Adaptive Principal Component Analysis (APCA) method. The original data set used to train the PCA model usually contains some error samples, whose useless residual subspace information make the threshold of Q-statistic higher than the expected threshold. This leads to a low sensitivity and low fault detection efficiency at low sensor fault levels. APCA is developed to automatically remove error samples in the original data set in order to improve fault detection efficiency especially for temperature sensor faults with absolute magnitude less than 1 °C. The self adaptive process of APCA has been presented and been compared with the Normal Principal Component Analysis (NPCA) method. The APCA strategy is validated by the operational data of a screw chiller system in a real electric factory. The results show that the APCA method can significantly enhance the fault detection efficiency, and the symmetry for positive and negative fault levels with same absolute magnitude becomes better than NPCA method.

Intelligent security technology provides important clues and basis for case detection, however, the traditional intelligent security technology can not alarm a crime's happening in advance. By researching the relationship between criminal psychology and speech feature, we proposed a crime degree theory of internet speech which can alarm a crime's happening in advance by taking advantage of the internet speech. The theory which was based on criminal psychology, using multiple analytical methods such as machine learning and emotional dictionary, establishing the mathematical model and theoretical framework, gave the preliminary implementation method of the real system. Experimental result has shown that the pre-alarming system based on our theory has good pre-alarming capability against crime.

In this paper, a novel multi-antenna spectrum sensing scheme utilizing phase difference between antennas in frequency domain is proposed. Different from other multi-antenna spectrum sensing schemes just combining signal information from every antenna, the proposed scheme attempts to make use of the information between different antennas with the help of multi-antenna technology. As no prior information of signals is required, the proposed scheme can be seen as a kind of blind sensing (BS) schemes. Its theoretical analysis as well as practical implementation is designed based on the least squares(LS) estimation. The simulation results suggest that the proposed scheme is capable of achieving a better performance even under the condition of moderately low signal-to-noise ratio (SNR) and small number of symbols.

With the SWOT analytical tool, the paper has analyzed the strengths and weaknesses in PFI mode and the opportunities and threats in the implementation of PFI financing in our urban infrastructures. The author puts forward a variety of countermeasures to promote PFI mode in our country and the achievement of this paper can be used for reference on how to introduce PFI to the construction of urban infrastructure.

The expanding of construction and demolition waste (C&D waste) not only represents an enormous dissipation of resources but also results in serious environmental pollution, which has created great negative impacts on the sustainable development of environment and society. The paper analyzes the present status of disposal and use of domestic construction waste, and focuses on analysis of the main sources and generating reasons of construction waste. Compared with construction waste management in developed countries, the paper has put forward some relative suggestions and solutions for better improvements.

In this paper, we devoted to semi-blind carrier frequency offset estimation for burst-mode transmission on the AWGN channel. Based on the framework of the well-known expectation-maximization (EM) algorithm, in addition to the uses of known pilot symbols, the proposed semi-blind estimation (SBE) algorithm jointly exploits the a posteriori information of the unknown data symbols which were referred to as the missing vector. Compared with the pilot-aided maximum-likelihood (ML) estimation, the performance of the estimation can be improved effectively without increasing the number of pilot symbols with the SBE algorithm. Simulation results are presented to verify the rationality of the SBE in terms of the mean square error (MSE), while it also shows that the performance can reach the CRLB at moderately high signal-to-noise ratios (SNR).