Circuit Breaker Fault Diagnosis Method Based on Improved One-Dimensional Convolutional Neural Network

Aiming at the problems of manual feature extraction and poor generalization ability of model in traditional circuit breaker fault diagnosis technology, a circuit breaker fault diagnosis method based on improved one-dimensional convolutional neural network is proposed. Firstly, the input feature sequence is adaptively weighted by self-attention mechanism to highlight the weight of important information; Secondly, 1 1 convolution layer and global average pooling layer are used to replace the full connection layer, which reduces the model training parameters, improves the training efficiency and prevents the phenomenon of over-fitting. Aiming at the problem of small number of data samples, the data is enhanced by Generative Adversarial Network. After adding the generated data to the original data, the accuracy of fault identification is further improved. The experimental results show that this method can effectively and accurately identify different fault types of circuit breaker, and verify the feasibility of its engineering application

A critical review of a computational fluid dynamics (CFD)-based explosion numerical analysis of offshore facilities

In oil and gas industries, the explosive hazards receive lots of attention to achieve a safety design of relevant facilities. As a part of the robust design for offshore structures, an explosion risk analysis is normally conducted to examine the potential hazards and the influence of them on structural members in a real explosion situation. Explosion accidents in the oil and gas industries are related to lots of parameters through complex interaction. Hence, lots of research and industrial projects have been carried out to understand physical mechanism of explosion accidents. Computational fluid dynamics-based explosion risk analysis method is frequently used to identify contributing factors and their interactions to understand such accidents. It is an effective method when modelled explosion phenomena including detailed geometrical features. This study presents a detailed review and analysis of Computational Fluid Dynamics-based explosion risk analysis that used in the offshore industries. The underlying issues of this method and current limitation are identified and analysed. This study also reviewed potential preventative measures to eliminate such limitation. Additionally, this study proposes the prospective research topic regarding computational fluid dynamics-based explosion risk analysis

Opportunities and challenges for Chinese elderly care industry in smart environment based on occupants' needs and preferences

New developments in intelligent devices for assisting elderly people can provide elders with friendly, mutual, and personalized interactions. Since the intelligent devices should continually make an important contribution to the smart elderly care industry, smart services or policies for the elders are recently provided by a large number of government programs in China. At present, the smart elderly care industry in China has attracted numerous investors’ attention, but the smart elderly care industry in China is still at the beginning stage. Though there are great opportunities in the market, many challenges and limitations still need to be solved. This study analyzes 198 news reports about opportunities and challenges in the smart elderly care industry from six major Chinese portals. The analysis is mainly based on needs assessment for elderly people, service providers, and the Chinese government. It is concluded that smart elderly care services satisfy the elders’ mental wants and that needs for improving modernization services are the most frequently mentioned opportunities. Also, the frequently mentioned challenges behind opportunities are intelligent products not being able to solve the just-needed, user-consumption concept and the ability to pay, which is the most frequently mentioned challenge. The results of this study will enable stakeholders in the smart elderly care industry to clarify the opportunities and challenges faced by smart elderly care services in China’s development process and provide a theoretical basis for better decision making

Integrating virtual reality and Building Information Modeling for improving highway tunnel emergency response training

During the last two decades, managers have been applying Building Information Modeling (BIM) to improve the quality of management as well as operation. The effectiveness of applications within a BIM environment is restrained by the limited immersive experience in virtual environments. Defined as the immersive visualization of virtual scenes, Virtual Reality (VR) is an emerging technology that can be actively explored to expand BIM to more usage. This paper highlights the need for a structured methodology for the integration of BIM/VR and gives a generic review of BIM and VR in training platforms for management in infrastructures. The rationales for fire evacuation training were formed based on the review. Then, methods of configuring BIM + VR prototypes were formulated for emergency response in highway tunnels. Furthermore, a conceptual framework integrating BIM with VR was proposed to enable the visualization of the physical context in real-time during the training. The result indicated that, extended to the training system of highway management via the “hand” of BIM, the VR solution can benefit more areas, such as the cost of fire evacuation drills in highway tunnels and the tendency of accidents to occur in the emergency response

Fragile X Mental Retardation Protein Regulates Proliferation and Differentiation of Adult Neural Stem/Progenitor Cells

Fragile X syndrome (FXS), the most common form of inherited mental retardation, is caused by the loss of functional fragile X mental retardation protein (FMRP). FMRP is an RNA–binding protein that can regulate the translation of specific mRNAs. Adult neurogenesis, a process considered important for neuroplasticity and memory, is regulated at multiple molecular levels. In this study, we investigated whether Fmrp deficiency affects adult neurogenesis. We show that in a mouse model of fragile X syndrome, adult neurogenesis is indeed altered. The loss of Fmrp increases the proliferation and alters the fate specification of adult neural progenitor/stem cells (aNPCs). We demonstrate that Fmrp regulates the protein expression of several components critical for aNPC function, including CDK4 and GSK3β. Dysregulation of GSK3β led to reduced Wnt signaling pathway activity, which altered the expression of neurogenin1 and the fate specification of aNPCs. These data unveil a novel regulatory role for Fmrp and translational regulation in adult neurogenesis

Effects of Deep Cryogenic Treatment on the Microstructures and Tribological Properties of Iron Matrix Self-Lubricating Composites

The effects of deep cryogenic treatment on the microstructures and tribological properties of the self-lubricating iron matrix composites are investigated. The self-lubricating composites are deeply cryogenically treated at about −196 °C. The results show that with deep cryogenic treatment, the martensite phase transformation occurred from phase γ to α′, and the fine particle carbides precipitated between martensites with the extension of cryogenic treatment time, measured by X-ray diffractometry (XRD) and scanning electron microscope (SEM). Compared with the as-sintered specimen, the maximum hardness of the specimens processed by cryogenic treatment increases by 172.8% from 253.2 HV to 690.7 HV. The materials with deep cryogenic treatment for 8 h show the best tribological properties, i.e., the average friction coefficient decreases by 75% from 0.36 to 0.09, and the wear coefficient decreases by 63% from 341 to 126 × 10−6 mm3/Nm at 150 N and 8 mm/s. The improvement of the tribological property can be primarily attributed to the martensite phase transformation from γ to α′ and the precipitation of fine particles carbides between the martensites, which increase the hardness and the wear resistance after the cryogenic treatment

Enhanced Performance of PAM7 MISO Underwater VLC System Utilizing Machine Learning Algorithm Based on DBSCAN

After visible light communication drawing increasing attention, underwater visible light communication (UVLC) has attracted more interest in the research community nowadays. As multiple input single output (MISO) is getting increasingly widely used to improve the transmission speed in UVLC system, the unbalance between multiple transmitters’ power is still a common phenomenon, which leads to the unequal spacing between each adjacent level and damages the system performance. In this paper, we study and analyze the unbalance between the two transmitters. Compared to a traditional hard decision, a density-based spatial clustering of applications with noise (DBSCAN) of a machine learning method is employed to get the actual center of each cluster and distinguish each level of PAM7 signals. In this way, a new decision curve substitutes traditional standard straight line as a constellation discrimination method. The experimental results show that up to 1.22 Gb/s over 1.2 m underwater visible light transmission can be achieved by using DBSCAN for PAM7 MISO signals. The measured bit error rate is well under the hard decision-forward error correction threshold of 3.8 × 10−3

Distribution characteristics on droplet deposition of wind field vortex formed by multi-rotor UAV.

When the unmanned aerial vehicle (UAV) is used for aerial spraying, the downwash airflow generated by the UAV rotor will interact with the crop canopy and form a conical vortex shape in the crop plant. The size of the vortex will directly affect the outcome of the spraying operation. Six one-way spraying were performed by the UAV in a rice field with different but random flying altitude and velocities within the optimal operational range to form different vortex patterns. The spraying reagent was clear water, which was collected by water sensitive paper (WSP), and then the WSP was analyzed to study the droplets deposition effects in different vortex states. The results showed that the formation of the vortex significantly influenced the droplet deposition. To be specific, the droplet deposition amount in the obvious-vortex (OV) state was about 1.5 times of that in the small-scale (SV) vortex state, and 7 times of that in the non-vortex (NV) state. In the OV state, the droplets mainly deposited directly below and on both sides of the route. The deposition amount, coverage rate and droplet size increased from top to bottom of the crops with the deposition amount, coverage rate, and volume median diameter (VMD) ranging 0.204-0.470 μL/cm2, 3.31%-7.41%, and 306-367μm, respectively. In the SV state, droplets mainly deposited in the vortex area directly below the route. The deposition amount in the downwind direction was bigger than that in the upwind direction. The maximum of deposition amount, coverage rate and droplet size were found in the middle layer of the crops, the range are 0.177-0.334μL/cm2, 2.71%-5.30%, 295-370μm, respectively. In the NV state, the droplet mainly performed drifting motion, and the average droplet deposition amount in the downwind non-effective region was 29.4 times of that in the upwind non-effective region and 8.7 times of the effective vortex region directly below the route. The maximum of deposition amount, coverage rate and droplet size appeared in the upper layer of the crop, the range are 0.006-0.132μL/cm2, 0.17%-1.82%, 120-309μm, respectively, and almost no droplet deposited in the middle and lower part of the crop. The coefficient of variation (CV) of the droplet deposition amount was less than 40% in the state of obvious-vortex and small-scale vortex, and the worst penetration appeared in the non-vortex amounting to 65.97%. This work offers a basis for improving the spraying performance of UAV