Modeling and Control Techniques in Smart Systems

Energy and food crisis are two major problems that our human society has to face in the 21st century. With the world’s population reaching 7.62 billion as of May 2018, both electric power and agricultural industries turn to technological innovations for solutions to keep up the increasing demand. In the past and currently, utility companies rely on rule of thumb to estimate power consumption. However, inaccurate predictions often result in over production, and much energy is wasted. On the other hand, traditional periodic and threshold based irrigation practices have also been proven outdated. This problem is further compounded by recent years’ frequent droughts across the globe. New technologies are needed to manage irrigations more efficiently. Fortunately, with the unprecedented development of Artificial Intelligence (AI), wireless communication, and ubiquitous computing technologies, high degree of information integration and automation are steadily becoming reality. More smart metering devices are installed today than ever before, enabling fast and massive data collection. Patterns and trends can be more accurately predicted using machine learning techniques. Based on the results, utility companies can schedule production more efficiently, not only enhancing their profitabilities, but also making our world’s energy supply more sustainable. In addition, predictions can serve as references to detect anomalous activities like power theft and cyber attacks. On the other hand, with wireless communication, real-time soil moisture sensor readings and weather forecasts can be collected for precision irrigation. Smaller but more powerful controllers provide perfect platforms for complicated control algorithms. We designed and built a fully automated irrigation system at Bushland, Texas. It is designed to operate without any human intervention. Workers can program, move, and monitor multiple irrigation systems remotely. The algorithm that runs on the controls deserves more attention. AI and other state of art controlling techniques are implemented, making it much more powerful than any existing systems. By integrating professional crop yield simulation models like DSSAT, computers can run tens of thousand simulations on all kinds of weather and soil conditions, and more importantly, learn from the experience. In reality, such process would take thousands of years to obtain. Yet, the computers can find an optimum solution in minutes. The experience is then summarized as a policy and stored inside the controller as a lookup table. Furthermore, after each crop season, users can calibrate and update current policy with real harvest data. Crop yield models like DSSAT and AquaCrop play very important roles in agricultural research. They represent our best knowledge in plant biology and can be very accurate when well calibrated. However, the calibration process itself is often time consuming, thus limiting the scale and speed of using these models. We made efforts to combine different models to produce a single accurate prediction using machine learning techniques. The process does not require manual calibration, but only soil, historical weather, and harvest data. 20 models were built, and their results were evaluated and compared. With high accuracy, machine learning techniques have shown a promising direction to best utilize professional models, and demonstrated great potential for use in future agricultural research

Risk Assessment of Nautical Navigational Environment Based on Grey Fixed Weight Cluster

In order to set up a mathematical model suitable for nautical navigational environment risk evaluation and systematically master the navigational environment risk characteristics of the Qiongzhou Strait in a quantitative way, a risk assessment model with approach steps is set up based on the grey fixed weight cluster (GFWC). The evaluation index system is structured scientifically through both literature review and expert investigation. The relative weight of each index is designed to be obtained via fuzzy analytic hierarchy process (FAHP); Index membership degree of every grey class is proposed to be achieved by fuzzy statistics (FS) to avoid the difficulty of building whiten weight functions. By using the model, nautical navigational environment risk of the Qiongzhou Strait is determined at a “moderate” level according to the principle of maximum membership degree. The comprehensive risk evaluation of the Qiongzhou Strait nautical navigational environment can provide theoretical reference for implementing targeted risk control measures. It shows that the constructed GFWC risk assessment model as well as the presented steps are workable in case of incomplete information. The proposed strategy can excavate the collected experts’ knowledge mathematically, quantify the weight of each index and risk level, and finally lead to a comprehensive risk evaluation result. Besides, the adoptions of probability and statistic theory, fuzzy theory, aiming at solving the bottlenecks in case of uncertainty, will give the model a better adaptability and executability.</p

Rehabilitation recognition skeleton data depth learning based on RNN

With the extensive application of deep learning in the field of human rehabilitation, skeleton based rehabilitation recognition is becoming more and more concerned with large-scale bone data sets. The key factor of this task is the two intra frame representations of the combined co-and the inter-frame. In this paper, an inter frame representation method based on RNN is proposed. Pointtion of each joint is joint-coded they are assembled into semantic both spatial and temporal domains.we introduce a global spatial aggregation which is able to learn superior joint co features over local aggregation

Acteoside From Ligustrum robustum (Roxb.) Blume Ameliorates Lipid Metabolism and Synthesis in a HepG2 Cell Model of Lipid Accumulation

We aimed to ascertain the mechanism underlying the effects of acteoside (ACT) from Ligustrum robustum (Roxb.) Blume (Oleaceae) on lipid metabolism and synthesis. ACT, a water-soluble phenylpropanoid glycoside, is the most abundant and major active component of L. robustum; the leaves of L. robustum, known as kudingcha (bitter tea), have long been used in China as an herbal tea for weight loss. Recently, based on previous studies, our team reached a preliminary conclusion that phenylpropanoid glycosides from L. robustum most likely contribute substantially to reducing lipid levels, but the mechanism remains unclear. Here, we conducted an in silico screen of currently known phenylethanoid glycosides from L. robustum and attempted to explore the hypolipidemic mechanism of ACT, the representative component of phenylethanoid glycosides in L. robustum, using RNA-seq technology, quantitative real-time PCR (qPCR) and Western blotting. First, the screening results for six compounds were docked with 15 human protein targets, and 3 of 15 protein targets were related to cardiovascular diseases. Based on previous experimental data and docking results, we selected ACT, which exerted positive effects, for further study. We generated a lipid accumulation model using HepG2 cells treated with a high concentration of oleic acid and then extracted RNA from cells treated for 24 h with 50 μmol/L ACT. Subsequently, we performed a transcriptomic analysis of the RNA-seq results, which revealed a large number of differentially expressed genes. Finally, we randomly selected some genes and proteins for further validation using qPCR and Western blotting; the results agreed with the RNA-seq data and confirmed their reliability. In conclusion, our experiments proved that ACT from L. robustum alters lipid metabolism and synthesis by regulating the expression of multiple genes, including Scarb1, Scarb2, Srebf1, Dhcr7, Acat2, Hmgcr, Fdft1, and Lss, which are involved several pathways, such as the glycolytic, AMPK, and fatty acid degradation pathways

An Adaptive Security Protocol for a Wireless Sensor‐based Monitoring Network in Smart Grid Transmission Lines

In this paper, we propose a new security protocol for a wireless sensor network, which is designed for monitoring long range power transmission lines in smart grid. Part of the monitoring network is composed of optical fiber composite over head ground wire (OPGW), thus it can be secured with conventional security protocol. However, the wireless sensor network between two neighboring OPGW gateways remains vulnerable. Our proposed security protocol focuses on the wireless sensor network part, it provides mutual authentication, data integrity, and data confidentiality for both uplink and downlink transmissions between the sensor nodes and the OPGW gateway. Besides, our proposed protocol is adaptive to the dynamic node changes of the monitoring sensor network; for example, new sensors are added to the network, or some of the sensors are malfunctioning. We further propose a self‐healing process using an “i‐neighboring nodes” public key structure and an asymmetric algorithm. We also conduct energy consumption analysis for both general and extreme conditions to show that our security protocol improves the availability of the monitoring sensor network

Identification and characterization of novel amphioxus microRNAs by Solexa sequencing

An analysis of amphioxus miRNAs suggests an expansion of miRNAs played a key role in the evolution of chordates to vertebrate

GJB2 mutation spectrum in 2063 Chinese patients with nonsyndromic hearing impairment

Background: Mutations in GJB2 are the most common molecular defects responsible for autosomal recessive nonsyndromic hearing impairment (NSHI). The mutation spectra of this gene vary among different ethnic groups. Methods: In order to understand the spectrum and frequency of GJB2 mutations in the Chinese population, the coding region of the GJB2 gene from 2063 unrelated patients with NSHI was PCR amplified and sequenced. Results: A total of 23 pathogenic mutations were identified. Among them, five (p.W3X, c.99delT, c.155_c.158delTCTG, c.512_c.513insAACG, and p.Y152X) are novel. Three hundred and seven patients carry two confirmed pathogenic mutations, including 178 homozygotes and 129 compound heterozygotes. One hundred twenty five patients carry only one mutant allele. Thus, GJB2 mutations account for 17.9% of the mutant alleles in 2063 NSHI patients. Overall, 92.6% (684/739) of the pathogenic mutations are frame-shift truncation or nonsense mutations. The four prevalent mutations; c.235delC, c.299_c.300delAT, c.176_c.191del16, and c.35delG, account for 88.0% of all mutantalleles identified. The frequency of GJB2 mutations (alleles) varies from 4% to 30.4% among different regions of China. It also varies among different sub-ethnic groups. Conclusion: In some regions of China, testing of the three most common mutations can identify at least one GJB2 mutant allele in all patients. In other regions such as Tibet, the three most common mutations account for only 16% the GJB2 mutant alleles. Thus, in this region, sequencing of GJB2 would be recommended. In addition, the etiology of more than 80% of the mutant alleles for NSHI in China remains to be identified. Analysis of other NSHI related genes will be necessary