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Thermal conductivity of polymers gains much attention in many fields, and the formation of a heat conduction network is very important for polymer coatings. Carbon nanofiller is one type of excellent candidate fillers for improving thermal conductivity, but the poor incorporation between the filler and the polymer restricts coating performances. In this study, the heat transfer oil (HTO) was used as a medium to communicate the carbon nanofiller with the polymer (poly(vinylidene fluoride) (PVDF), fluorinated ethylene propylene (FEP)) and to improve the thermal conductivity and corrosion resistance of PVDF composite coatings. The micronanostructure of superhydrophobic PVDF/FEP/SiO2 (modified PVDF or m-PVDF) composite coatings was used as a storage space for the HTO, so that various carbon nanofiller including carbon nanotubes (CNTs), graphene, carbon black, and carbon nanofibers can take effect in improving thermal conductivity. Among the m-PVDF/carbon nanofiller composite coatings, the HTO/m-PVDF/CNTs coating gained the highest thermal conductivity of 0.53 W·m−1·K−1 after HTO modification, which was 3.3 times that of the m-PVDF/CNTs coating and was attributed to the connectivity of HTO. The anticorrosion of HTO/m-PVDF/CNTs was increased by 3 orders of magnitude than that of m-PVDF/CNTs owing to the synergistic effect between the HTO and CNTs in blocking the invasion of corrosive species. This research provides a unique way to design and fabricate high-thermal-conductivity composite coatings with high anticorrosion by the synergism between carbon nanofiller and HTO.

In this paper, a spatial-temporal correlation aware data collection mechanism is proposed for a event-driven sensor network in terms of the realistic requirements such as real-time data sensing and dynamic network topology. Firstly, in order to reduce the path congestion and the data transmission delay, the perceived data states are classified based on binary representation. Secondly, a low cost manner is studied to aggregate the perceived data at the representative nodes and aggregation nodes respectively based on the spatial-temporal correlation. Furthermore, the best data collection path is obtained by carrying out a particle swarm optimization (PSO). Simulation results validate that the proposed algorithm can effectively reduce the amount of data transmissions in the network event area. Besides, the proposed mechanism also has advantages in reducing the delay and energy consumption.

This paper analyzes the current situation and shortcomings of traditional security chip production program management, then proposes a management approach of a chip issue program management method and develope a management system based on Webservice technology. The program management method and system of chip production proposed in this paper simplifies the program management process of chip production and improves the working efficiency of chip production management.

Although smart meters bring convenience to people’s lives, they also bring unnecessary losses to grid operations due to intrinsic defects. Compared with traditional electric meters, the smart meters are susceptible to interference from external magnetic fields, and may stop charging when strong interference occurs, which reduce its reliability. This also brings opportunities for lawbreakers, and the phenomenon of electricity leakage occurs from time to time. In order to overcome the shortcomings of electric meters, this project makes an in-depth research on high frequency electromagnetic field monitoring technology, and carries out the antenna simulation technique, the envelope detection technique and the alarming control system design based on the experimental data of high frequency electromagnetic field environmental parameter monitoring. A technical solution that can quickly detect external high-frequency interference sources and give alarm when stealing occurs is proposed, and a high frequency electromagnetic field monitoring module is designed to protect the operating environment of the meter.

This thesis proposes a spatial three-dimensional positioning optimization method based on error probability, based on the positioning method based on time difference of arrival (TDOA), through the research on the positioning of substation inspection operators and equipment under complex working conditions, The method used optimizing the distance difference between the node to be positioned and different base stations can effectively improve the positioning accuracy of the staff and equipment in the station. The test results in a complex power frequency electric field environment show that the calculated results of this optimization method are better than the existing traditional positioning methods, and can effectively improve the positioning accuracy under line-of-sight conditions and realize the positioning of moving targets in the substation space.

Identity-Based Cryptograph (IBC) takes the user's identity (such as email address, mobile phone number, personal name, etc.) as the public key, which reduces the process of exchanging digital certificates and public keys, effectively reduces the complexity of key and certificate management in the public-key system, and makes the security system easy to deploy and manage. It is very suitable for end-to-end offline secure communication, cloud data encryption, attribute-based encryption, policy-based encryption of various occasions. In order to improve the safety level of end-to-end off-line safety communication in electric vehicle pile sharing application and reduce the system complexity, this paper proposes an identity authentication system solution based on identification algorithm.

An ultra-low power consumption SoC integrated with the power management unit that used for edge computing is present in this paper. The Rocket Core that bases on low power open-source hardware instruction set RISC-V is used as the core in the SoC, and open source deep learning accelerator NVDLA is integrated into the SoC. Besides, rich peripheral interfaces are integrated to meet the most demands for IoT applications. To analyze and evaluate the power feature, the proposed SoC is synthesized in SMIC 40nm LP CMOS technology; and some applications are run on this SoC to estimate the power consumption.

This paper present the industrial case study on quality improvement of the post reflow cleaning process in an assembly industry. Ceramic carrier boards are widely used in industrial fields due to their good thermal conductivity, strength and insulation. In order to reduce the terminal volume, flexible plates are also widely used. For the high quality industry application, a number of manufacturing related issues needed to be addressed. Of all Potential defects, the PCBA pollution is most critical defect which will affect the long term reliability. The ceramic board and FPC was assembly with Sn96.5Ag3Cu0.5 solder paste an orthogonal experimental design was conducted to understand the relationship among the washing temperature, washing solution and the cleanness level. From the experiment we got the optimal post-reflow process conditions for achieving highest quality cleaning status, which meet the requirements of IPC standard.

The effects of the synergy of pressure regulation and Eu substitution on the microstructure and thermoelectric (TE) properties are investigated for type-I clathrates Eu[Formula: see text]Ba[Formula: see text]Cu6Si[Formula: see text]Ge[Formula: see text] ([Formula: see text], 0.5, 1, 1.5). The rare-earth-substituted and high-pressure modulated samples show complicated morphologies composed of abundant grains and rich in lattice defects. The Seebeck coefficient and the electrical conductivity of the rare-earth-substituted samples synthesized by HPHT method are consistent with [Formula: see text]-type conduction and metal-like behavior. The carrier concentration increases and the Hall carrier mobility decreases with increasing Eu substitution at room temperature. Although the increase of Eu filling rate decreased the Seebeck coefficient of the samples, it increased the power factor (PF) of the samples significantly. The thermal conductivity also reduced on account of Eu filling. As a result, a minimum [Formula: see text] value (0.67 Wm[Formula: see text]K[Formula: see text]) and a relatively higher zT value (0.68) are achieved. Compared to the Eu-free sample, the achieved zT value of the Eu-substituted sample is a [Formula: see text][Formula: see text]62% enhancement. So, high-pressure technique is an effective route to synthesize clathrate materials and optimize TE properties simultaneously.

Since most outdoor transmission line equipment suffers from harsh disasters, they are prone to wire breakage, tower collapse and insulator flashover. When anomaly occurs, too much time is required for the State Grid Corporation to fix it manually. To reduce the inspection burden, many methods have been presented in the past to diagnose and locate anomaly. In this paper, we propose an improved AlexNet model for anomaly detection. In the aspect of feature extraction, the proposed model extracts the characteristics of transmission line equipment through a deep convolutional neural network (DCNN). In the aspect of the recognition algorithm, by referring to the advantages of the traditional machine learning method and incorporating the advantages of the support vector machine (SVM), an SVM classification method incorporating deep learning is proposed. Finally, the improved AlexNet model and SVM classification method are used to classify images of various types of power equipment. The results show that the proposed methods can be effectively applied to the image recognition of various types of power equipment, and they greatly improve the recognition rate of power equipment images, which has great potential for future real-time transmission line monitoring platform design.

After the single-phase ground fault occurs in the distribution network, a wealth of electrical transient characteristics are generated in the system. The system has the characteristics that the transient zero-sequence currents at both ends of non-fault section are similar and the difference of transient zero-sequence current at both ends of the fault section is large. In this paper, a method for fault location based on the generalized S-transform energy remainder of transient zero-sequence current is proposed. The time-frequency matrix and each band energy are obtained through the generalized S-transform of the faulty transient zero-sequence current. By calculating the energy remainder between the two ends of each section and normalizing it, the section with the largest energy remainder is the fault section. Lots of simulation experiments show that the proposed method is immune to the fault position, transition resistance and fault time. The fault section can be selected accurately and reliably.

With the comprehensive construction of smart grid, Internet of Things technology has been widely applied to the power generation, transmission, scheduling and many other aspects. First, a framework for the information communication system of power Internet of things is proposed Second a method based on fuzzy logic is given to judge the sensitive information. Third, the way to deal with different information is shown according to the sensitivity of the data. The feasibility and correctness of the proposed framework and method are verified by tests.

RFID plays a very important role as auto-identification solution for many industrial applications. In electric power industry, UHF RFID tags are attached on smart meters, and a tag provides a unique electronic identification for a smart meter, so RFID is applied to make asset inventory for smart meters in a warehouse. However, there are just about 95% of tags that are readable for their reader because of the interference of PCB and electronic components in smart meter, thereby it has to take a lot of time to manually find out the non-responsive tags. This paper puts forward a feasible method of testing tag's performance according to multi-tags group-reading environment in the application of smart meter asset inventory, and verifies the feasibility of the method through inventory experiments, and finally gives out an optimized parameters to read 100% of tags with the sensitivity of −16.25dBm in a multi-tags environment. The method is suitable for the test of large quantities of smart meters that are densely packed in a plastic box. The proposed method can guide electric power companies to select tags and readers, and improve the efficiency of inventory for smart meters asset management.