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Autonomous navigation is the premise of the application of intelligent orchard machinery, which can help the orchard mobile robot to carry out multi-functional operations without human control. This paper describes the current research status of orchard autonomous navigation technologies, mainly discusses sensing solutions for autonomous navigation in orchard, navigation data processing algorithms and navigation control strategies. First, existing single-sensor based navigation solutions in orchard and their limitations are described. Multi-sensor based navigation has high accuracy, which is the development trend of orchard navigation in the future. Second, navigation data processing algorithms is the key of environmental perception and path planning for orchard robots. Different algorithms for orchard feature extraction and data fusion are introduced. Third, navigation control strategies are discussed.

In the provincial electricity-heat integrated energy system with combined heat and power (CHP) as the coupling point in northern China, the configuration of heat storage tank (HST) in CHP plants for power-heat decoupling is an effective way to release operation flexibility of CHP units and improve the renewable energy accommodation capacity of the system. In order to give full play to the function of HST, this paper analyzes the mechanism of HST’s heat supply reserve transforming to unit’s generating reserve and establishes the corresponding model. In order to adapt to the day-to-day volatility and uncertainty of wind power, a heuristic day-time coordinated operation strategy is proposed. The HST stores heat as much as possible in the period of no power curtailment of renewable energy generation and releases heat according to the demand during the period of power curtailment of renewable energy generation. And an economic dispatch model of the electricity-heat integrated energy system is established considering the reserve benefits of the HST under the proposed operation strategy. Case studies are carried out based on actual grid data to verify the effectiveness of the proposed model and the benefits in saving fuel, accommodating renewable energy generation, and saving capacity.

Energy consumption predictions for residential buildings play an important role in the energy management and control system, as the supply and demand of energy experience dynamic and seasonal changes. In this paper, monthly electricity consumption ratings are precisely classified based on open data in an entire region, which includes over 16 000 residential buildings. First, data mining techniques are used to discover and summarize the electricity usage patterns hidden in the data. Second, the particle swarm optimization-K-means algorithm is applied to the clustering analysis, and the level of electricity usage is divided by the cluster centers. Finally, an efficient classification model using a support vector machine as the basic optimization framework is proposed, and its feasibility is verified. The results illustrate that the accuracy and F-measure of the new model reach 96.8% and 97.4%, respectively, which vastly exceed those of conventional methods. To the best of our knowledge, the research on predicting the electricity consumption ratings of residential buildings in an entire region has not been publicly released. The method proposed in this paper would assist the power sector in grasping the dynamic behavior of residential electricity for supply and demand management strategies and provide a decision-making reference for the rational allocation of the power supply, which will be valuable in improving the overall power grid quality.

With the rapid development of economy and the continuous progress of science and technology, electric power engineering plays a very important role and has been widely used in all walks of life, making some gratifying achievements in electrical engineering automation technology. PLC technology refers to programmable controller, which is a new type of industrial automation control device, with convenient programming, high reliability and powerful functions. Its application in the field of electrical automation control is an inevitable trend of development. After a large amount of data investigation, it is found that this technology can be well applied in the automation control of electrical engineering, and has a good effect. The application of PLC technology in electrical engineering can not only achieve better automation control, but also be the development direction of electrical engineering in the future. With the development and progress of the times, the demands and requirements of work are getting higher and higher, and many problems are gradually found in the application process of PLC technology. Based on this, this paper mainly analyzes the application of PLC technology in electrical engineering and its automation control.

3D object detection based on point cloud data is an essential part of L4 automatic driving. This paper proposes a novel end-to-end trainable deep learning network structure, PPMGNet, which can quickly encode point clouds, obtain the spatial feature of point clouds, predict multiple categories, and perform 3D object detection in real time. A large number of experiments show that in terms of speed and accuracy, PPMGNet's detection performance is very suitable for direct deployment in autonomous driving applications.

Acoustic emission (AE) detection, as a non-electrical detection method, is very suitable for effective fault detection of power equipment with a strong electromagnetic field. However, the AE signal collected at industrial sites often contains a lot of interference noise, affecting the analysis and prediction of faults. In this study, a wavelet denoising method based on a new threshold function is proposed, to achieve a noise reduction in the low signal-to-noise ratio (SNR) signals. Simulation experiment results show that the proposed threshold function not only overcomes the shortcomings of the discontinuous hard threshold function, but also solves the constant deviation of the soft threshold function. What’s more, the proposed function achieves a good adaptability. When SNR = 10 dB: The SNR of the new threshold is 20.6622, and the RMSE is 0.0026. The SNR of the hard threshold is 20.2246 and the RMSE is 0.0027, compared with the traditional hard threshold method, the SNR of the new threshold is increased by 2.16% and the root mean square error (RMSE) is reduced by 3.7%; the SNR of the soft threshold is 15.5656, and the RMSE is 0.0047, compared with the traditional soft threshold method, the new threshold has a 32.74% increase in SNR and a 40.43% reduction in RMSE. When SNR = -10 dB: The SNR of the new threshold is 4.2602, and the RMSE is 0.0172. The SNR of the hard threshold is 3.8558 and the RMSE is 0.0182, compared with the traditional hard threshold method, the SNR of the new threshold is increased by 10.49% and the RMSE is reduced by 5.49%; the SNR of the soft threshold is 2.1625, and the RMSE is 0.0212, compared with the soft threshold method, SNR is improved by 97% and RMSE is reduced by 18.87%. Performance analyses have proved that the improved wavelet denoising method can obtain a good noise reduction effect. It is very helpful for AE signal analysis with the generally low SNR, which can improve the accuracy of failure identification in subsequent acts.

In recent years, with the rise of artificial intelligence, the research in the field of pattern recognition is developing rapidly. Its supporting algorithms mainly include neural network and support vector machine (SVM), which are widely used in various fields such as industrial detection, aviation, medical treatment, finance, internet and so on. On the one hand, the effect of pattern recognition is related to the selection and optimization of the algorithm, on the other hand, it is closely related to the noise reduction methods to improve the signal quality, the feature extraction and feature selection in feature engineering. Mel frequency cepstrum coefficient (MFCC) is widely used in the field of speech recognition, and has achieved good recognition effect for low-frequency speech signal pattern recognition, but its pattern recognition in ultrasonic field is rarely studied. In this paper, a method of extracting MFCC in ultrasonic frequency range and applying it to SVM pattern recognition and fault prediction is proposed. In the experimental verification stage, GIS-PD model is used to collect data of acoustic emission (AE) signal induced by partial discharge (PD) of gas insulated switchgear (GIS), and MFCC eigenvalues are extracted in the ultrasonic frequency range of 20-200 kHz. Finally, MFCC and optimized selected time-domain and frequency-domain eigenvalues are used for SVM pattern recognition to monitor and warn the running state of GIS equipment. Based on the comprehensive scheme proposed in this study, the overall accuracy rate is improved to ninety percent after fusion of MFCC eigenvalues, which is improved by 22.5 percent compared with only using time-frequency domain eigenvalues. At present, the research on MFCC extraction in the ultrasonic frequency range of 20-200 kHz and its application in SVM pattern recognition has not been reported. This study will promote the research of MFCC in the field of ultrasonic detection, and provide reference for the research in related fields.

Data privacy has drawn a lot of attention from people in sectors of commerce, data processing and credit reporting, especially with the adoption of GDPR, CCPA, PIPA, GB/T35273 and ISO27701 etc. These regulations aim to give people the ownership of data they produce by law, however, due to the lack of standards, tools, and best practices, many enterprises struggle to adapt their workflow and database to ensure that the data processing is in compliance with the data owner's consent and suffer from heavy fines. In light of this challenge, we develop a blockchain based platform to enable both privacy preserving and privacy compliance. UDPP (User Data Privacy Protection) platform is constructed on an immutable blockchain system, including the compliance module and the privacy tool set. UDDP provides utilities and tools to make desensitization, encryption and self-check process easier. The compliance module can assist companies to self-check on the privacy policy as well as the data processing both in their apps and backend services, while the tool set provides multiple combinations of privacy protection schemes to solve different privacy preserving and user authorization cases. Moreover, UDPP will provide the user with a “token”, which represents the permissions of the data ownership. The token can be viewed as a privacy badge, and it records hash of all the data processing and storage infos as well as the update of user consents.

Token represents the right to do some operations in software. In blockchain, there are two types of tokens: utility token and security token. They endow the items of token more value in blockchain world. In order to achieve asset tokenization, we propose a new kind of token in this paper, the asset-backed token, which is used for the proposed blockchain based Open Asset Protocol (OAP). Using OAP, we show to how to convert both of the real and virtual objects to asset-backed tokens on blockchain. Then we discussed a new method for data utilization and privacy-preserving based on OAP and compare with our previous scheme, the Secure Multi-Party Computation (SMPC). Additionally, we introduce the Policy-Backed Token(PBT), which is an instance implementing OAP in insurance industry. We have applied PBT in the airline travel insurance product E-life of an insurance company.

With the widespread of cloud computing, the delegation of storage and computing is becoming a popular trend. Concerns on data integrity, security, user privacy as well as the correctness of execution are highlighted due to the untrusted remote data manipulation. Most of existing proposals solve the integrity checking and verifiable computation problems by challenge-response model, but are lack of scalability and reusability. Via blockchain, we achieve efficient and transparent public verifiable delegation for both storage and computing. Meanwhile, the smart contract provides API for request handling and secure data query. The security and privacy issues of data opening are settled by applying cryptographic algorithms all through the delegations. Additionally, any access to the outsourced data requires the owner’s authentication, so that the dat transference and utilization are under control.

While the multiple party computation (MPC) has been suggested for over two decades, we still have not witnessed implementations that make MPC or its derivates deployed in a palpable reality. Difficulties stem from serial security and trust issues, typically expressed in data leakage, lazy calculation, and deny of payment etc. Many solutions rely on a third party to organize parties co-working, however, this requires each participant to totally trust the third party and delegate the source data and payment to it. In this paper, we suggest a protocol under blockchain structure to achieve efficient MPC without a trusted third party. The cooperation and schedule tasks are executed by a zero-knowledge coordinator, while the smart contracts is used to handle user request and safe payment. Cryptography techniques are adopted to ensure confidentiality, privacy-preserving and verifiability. All the data are transferred and calculated in the cipher state, and only the payed user can decrypt for the calculation result.

Skew Redundant Inertial Measurement Unit (SRIMU) is used to ensure that flight vehicle can achieve high reliability and accuracy. There are two essential discussions for SRIMU, one is Fault Detection and Isolation (FDI), and the other is how to get more accurate navigation parameters. Currently, two parts are investigated separately. This paper presents a FDI and navigation algorithm for SRIMU in an integrated solution framework. Considering various error sources, a more precise output model is established firstly. Unitary transformation is applied to project redundant information into data estimation space and null space for fault monitor. Kalman filter is used not only to compensate stochastic error for inertial sensors, but also in GNSS / INS integrated navigation system. Simulation is performed to show the performance of the suggested method. It is proofed that proposed frame has a sensitive capability of fault detection and high accuracy of the navigation solution.