bluuebunny/crossref_metadata_2025 huggingface dataset:
AVC Fine Control Rules of Urban Power Grid Based on Improved Deep Probabilistic Neural Network
Authors: Wang Wei , Wang Haiyun , Chen Qian , Wei Huayue , Zhang Jianfang
Published: December 2022

Aiming at the problems that the voltage of urban power system is greatly affected by load and the control is complex, this paper proposes a fine control rule of AVC (Automatic Voltage Control) based on improved deep probabilistic neural network. The rule is based on a variety of power grid operation modes, and different control variables are extracted to form state variables. The deep probabilistic neural network algorithm is used to study the internal relationship between control variables and state variables. At the same time, the genetic algorithm is used to optimize the AVC limit parameters, and the AVC fine control rules are obtained. Compared with the traditional AVC control rules based on linear regression model, the deep probabilistic neural network combined with genetic algorithm model proposed in this paper has smaller error, stronger robustness and effective solution to the nonlinear problem of urban power grid data for AVC limit parameter optimization, it can effectively improve the power factor of the system, ensure the safety of the system and improve the stability of the system.
TVS Diode Selection and Theoretical Calculation in Automotive Electronics
Authors: Wang Qinzheng
Published: December 2022

In the automotive electronic function module protection, in addition to electrostatic protection (ISO10605), the most important to calculate the road vehicle electrical transient interference protection, that is, we usually mentioned the automotive electronic test standard ISO 7637-2 and enhanced version of ISO 16750-2 standard. Among them, the load test is to simulate the battery power loss, by the generator to supply power to the load, verify whether the load at this time can’t meet the requirements of the situation [1]. For the load throwing test, TVS is usually used to release the energy injected by the power line to achieve the purpose of protecting the later circuit.
Aerodynamic Platform Prototype for Pre-Flight Testing of Nanosatellites and Unmanned Aerial Vehicles
Authors: Baranova Vasilina , Liashkevich Siarhei , Spiridonov Alexander , Saetchnikov Vladimir
Published: December 2022

The results of the simulation and development of an aerodynamic platform for pre-flight testing of nanosatellites and Unmanned Aerial Vehicles are presented. The platform movable part is realized in the form of a regular hexagonal prism with a honeycomb hollow structure. It provides levitation conditions and allows a check of the attitude control system performance. The airflow dumping elements of the air injection subsystem which provides platform stability are suggested. The aerodynamic platform model is developing as part of a project to introduce practical elements of prototyping and pre-flight testing of nanosatellites and Unmanned Aerial Vehicles into the educational process. It will help university students to solve scientific and technological problems.
Variable-Length Iterative Learning Control for Live Working Manipulators
Authors: Yang Zhechuan , Xiong Xiaoyu , Zhu Yicheng , Ma Caiguo , Ma Zhengshu , Sun Lei , Song Xiaohua , Guo Xin
Published: December 2022

The rapid development of computer technology and robot technology in recent years makes it possible for robotic arm to replace manual work to carry out dangerous live work, which will greatly improve the efficiency of maintaining electric power facilities, shorten maintenance time and protect workers’ safety. Aiming at the control of manipulators, an iterative learning control algorithm based on variable length and time-varying system is developed in this paper. This method can well solve the problem of different operating time of live-line operation of manipulator, and improve the limitations of original iterative control laws that each iteration time is required to keep same to adapt to the actual working conditions. Specifically, this paper is divided into two parts: Firstly, machine vision is used to locate the spatial relationships among the manipulator base, the manipulator actuator, and the target to be worked; Then, a novel iterative learning control schemes are constructed for the tracking control purpose.
Research on Active Equalization System of Power Battery
Authors: Xiang Jundong
Published: December 2022

A hierarchical active equalization topology based on Cuk converter and flyback converter was proposed to solve the problem of the inconsistency of series battery packs used in electric vehicle power cells. Using state of charge (SOC) as equalization standard, two equalization control strategies were developed, the second-order equivalent circuit model of lithium ion battery and open circuit voltage (OCV) characteristic curve were established to identify parameters and estimate SOC. The equalization of single battery in series battery was realized, and the inconsistency of series battery was reduced. Finally, a balanced experimental platform was built for verification. The layered active equalization framework gives full play to the advantages of the two kinds of active equalization, and improves the problem of the inconsistencies of the series battery. According to the SOC distribution of all the single cells in the series battery, the balancing control strategy is selected, which can effectively shorten the balancing time.
Lightweight Application of SM2 Co-Signature Algorithm in the Power IOT
Authors: Zhang Yanrong , Yang Feng , Yuan Yanfang
Published: December 2022

The access of massive terminal devices at the perception layer poses security threat to the power Internet of Things (IOT) of the State Grid Corporation. Due to lack of computing resources, the IOT perception layer terminal devices cannot embed the security chips. The lightweight authentication technologies are urgently needed to be developed to protect the authentication security of the IOT perception layer terminal devices. This paper proposes a lightweight identity authentication system based on the SM2 co-signature algorithm, which can be applied to the identity authentication of the IOT perception layer terminal devices. This system is implemented in software and characterized by low cost and strong compatibility, and most importantly, it can strengthen the identity authentication security of the power IOT which security protection capability will be improved consequently.
A Method for Scientific Cultivation Analysis Based on Knowledge Graphs
Authors: Wu Aiyan , Zhang Yongmei , Yang Shang
Published: December 2022

The development of big data and artificial intelligence has improved the intelligence and informatization of scientific planting. A scientific cultivation analysis method based on knowledge graphs is proposed in this paper. First, the logical representation and the ontological representation are combined to realize the access of static cultivation information and dynamic cultivation experience, as well as their representation with graphs. Second, according to the characteristics of plant cultivation information, knowledge extraction is realized via relational computing. A relationship determination method based on the first derivative and a multi-level classification retrieval method based on a tree structure are proposed to extract cultivation experience from the experimental data. Then, multimedia technology is embedded in the RDF framework and implemented, which further realizes the display of decision suggestions after scientific cultivation information analysis. Finally, taking perennial flowers as an example, the realization and application performance of cultivation knowledge graphs are demonstrated.
Traffic Flow Imputation Based on Multi-Perspective Spatiotemporal Generative Adversarial Networks
Authors: Shen Guojiang , Liu Nali , Liu Yinghui , Zhou Wenfeng , Kong Xiangjie
Published: December 2022

Traffic data occupies an important position in intelligent transportation systems (ITS). However, the collected traffic data is often incomplete. We propose a generative adversarial network (GAN) model based on multi-perspective spatiotemporal learning (MST-GAN) to repair data. To achieve the effect of interpolating data from three perspectives: temporal, spatial, and spatiotemporal, we utilize chained generator with independent parameters to progressively refine the learning of temporal and spatial features. In addition, we achieve high-level fusion of multi-perspective features by adversarial between multiple generators and one discriminator. We conduct experiments on two real datasets, showing that the imputation effect of the MST-GAN model is better than other baseline models under different missing patterns. For example, the root mean square error (RMSE) is less than 7.5% and the mean absolute error (MAE) is less than 5% in the random missing scenario, which is much lower than the best performance error of other models.
Anti-Jamming Method of Cognitive Radio Based on Q-Learning
Authors: Xiao Yichen , Ren Haiyu , Wu Shan , Liu Lixiang , Meng Xiandong , Ding Pengcheng
Published: December 2022

Due to the exposed nature of wireless links, the communication of wireless networks is vulnerable to jammers. And because the jammer models are usually unknown to communication users, particularly in military confrontation applications, how to ensure maintain communication under different jamming is an active research topic. In this paper, we take the anti-jamming task of cognitive radio as a Markov decision process and propose an anti-jamming method based on Q-learning. The method aim to learn an efficient policy for users to maximize the total channel transmission capacity in different typical jamming scenarios. The simulation results indicate that compared with the traditional anti-jamming methods, the anti-jamming method based on Q-learning can obtain better performance, and more effective against several kinds of typical jamming models.
Citrus Appearance Quality Grading System Based on OpenCV Image Processing
Authors: Bao Yiqin , Liu Qin , Bao Yulu
Published: December 2022

The production technology of Citrus in China is still at the initial stage of development, and the classification of Citrus mainly depends on manual work, which is subjective and inefficient. To solve the problem of time-consuming, laborious and low efficiency of citrus classification, this paper uses the OpenCV image processing technology, Candy edge detection operator for edge detection and DP algorithm for contour extraction to find the two points with the largest distance in the contour to achieve citrus diameter detection; The RGB color space is converted into HSV color space, and the parameters of H component are extracted to obtain the citrus coloring rate, thus realizing the citrus appearance quality grading system. The experiment proves that the accuracy of diameter detection is more than 99%, reducing the artificial classification deviation.
Application of SCMA Based on LoRaWAN Architecture
Authors: Jin Minhao , Fan Libo , Luo Shaojie , Guo Xiaotian
Published: December 2022

Low-power Wide-Area Network (LPWAN), as a wireless connection technology of the Internet of things, has met the requirements of power consumption of network providers and operators, network coverage and Internet of things equipment. As the main technology of LPWAN, Long Range (LoRa) has entered the stage of scale deployment. However, in the MAC protocol of LoRa, multiple end nodes send data to the gateway at the same time, which will cause serious collision problems. When data collision occurs, the gateway will no longer receive collision data, and the data packet needs to be retransmitted after a certain time. In order to solve this problem and improve the throughput of the whole system, this paper proposes to introduce the sparse code multiple access (SCMA) technology of 5G non-orthogonal multiple access (NOMA) into Long Range Wide Area Network (LoRaWAN) architecture from the perspective of code domain. The theory and simulation results show that SCMA can effectively solve the problem of data collision and improve the system throughput.
Research on the Construction and Evolution Model of Cloud Ecosystem
Authors: She Guolong , Yao Mengdi , Deng Guohua
Published: December 2022

Clarifying the positioning of participants in the cloud ecosystem and analyzing the competition and cooperation among subjects are important for the sustainable and healthy development of cloud ecosystem. This paper analyzed the structure and the evolution law of the participants in the cloud ecosystem. The results show that, in a certain degree of competitive environment, companies with different positions and competitive relationships have different competitiveness. Cooperation is conducive to the coexistence of individuals. The stronger the cooperative relationship, the greater the enterprise’s profit. These findings give a beneficial motivation for the coordinated relationship and development strategy for cloud providers.
Research on Intelligent Research Model of Clues in Cases of Production and Sale of Counterfeit Drugs
Authors: Gu Linfeng , Li Sisi , Wang Yutian , Lin Zhihui , Wu Jingsong
Published: December 2022

This model takes the vigorous promotion of the construction of “smart policing” and the construction of a modern police system as the background, creates a data-based, technology-based, vigorously improves the analysis and mining capabilities of public security organs on the hot issues of counterfeit drug crimes, and transforms the traditional post-processing and passive response methods of police work to the direction of pre-prediction and active intervention. At the same time, drawing on the design ideas of the system architecture, the model system is layered and decoupled, creating a multi-channel data collection, to case text preprocessing and standard definition, forming a “four-category personnel database”, and then classifying according to the theme of public security business to achieve a criminal model of production and sales of counterfeit drugs The overall framework is reused vertically in the field and flexibly extended horizontally.
Continuous-Speech Spoken Dialogue System Based on Joint Decision-Making of Audio and Semantics
Authors: Deng Qiuwei , Peng Qiang , Ma Zhifang , Zhao Pei , Wang Di
Published: December 2022

To avoid the frequent wakeups of general spoken dialogue systems, we studied continuous-speech spoken dialogue systems. The experiment results show that continuous-speech systems can efficiently enhance the success rate of interactions by 8%, in contrast with discontinuous ones. The full-duplex continuous-speech dialogue systems, which are continuously listening surrounding voices, are often interfered with by invalid sounds, such as the voice played on TV. Therefore, we have studied the audio recognition algorithm to reject those noises, and the experiments show that the recognition accuracy of this algorithm can reach about 94%. In this paper, we propose joint decision-making of audio and semantics, which can effectively reject invalid speech and further improve the success rate of interaction in continuous-speech spoken dialogue systems.
A One-to-Two Filter Power Divider Based on LTCC
Authors: Li Yuanyuan , Wu Xianliang , Hu Yunfeng , Dong Mengying , Ma Yue , Geng Xiaolei , Fang Rongzhen
Published: December 2022

In order to miniaturize microwave devices in RF systems, a power divider with filtering performance is designed. The filter power divider is based on the LTCC process, and miniaturization is achieved by cascading a bandpass filter and a one-to-two power divider. The band-pass filter adopts a fourth-order comb-line structure, and the one-to-two power divider adopts an LC lumped structure. The size of the one-to-two filter power divider designed is 10mm×4mm×5mm, which is working in 3.3GHz–3.6GHz. The simulation results show that the insertion loss(S21 and S31) are less than or equal to 4 dB, the return loss(S11) is greater than 15 dB, the isolation(S23) is greater than 17 dB, and the out-of-band suppression is greater than 18 dB in 3.8–4.3GHz and 22 dB in 0–3.2GHz and 4.3–6GHz.
Tiny Deep Convolution Recurrent Network for Online Speech Enhancement with Various Noise Types
Authors: Deng Qiuwei , Wang Di , Luan Tianxiang , Hao Bin
Published: December 2022

Nowadays, voice interaction is increasingly applied to smart home appliances. There are various types of noises in our real lives, which requires speech enhancement technology to deal with multiple noisy speech scenarios and to process them in real-time. Traditional technologies of speech noise reduction require estimating the noise power spectrum first, then estimating the spectrogram gain value of noise reduction, such as minima controlled recursive averaging (MCRA), which can only deal with stationary environmental noises but cannot estimate noises with serious fluctuations of the power spectrum within quite limited durations. A highly complicated deep-learning model can estimate the power spectrum of various types of noise, but it cannot meet the requirement of real-time processing due to the large number of parameters of these general models. In this paper, we proposed a method combining deep-learning technologies with traditional signal processing techniques to estimate the power spectrum of various types of noises by designing a new model with fewer parameters, tiny deep convolutional recurrent network (TDCRN), and computing the speech gain value with the power spectrum. The result of our experiment indicates that, compared with the traditional technology and complicated deep-learning model, the proposed method, with only 0.29M parameters, increases the PESQ by more than 0.6, the STOI by more than 0.2 and the wake-up rate by more than 6%.
Measuring the State of ECN Readiness in Mail Servers on the Internet
Authors: Chen Chun-Xiang , Shigeyasu Tetuya , Nagaoka Kenichi
Published: December 2022

Congestion control on the Internet has been a challenge issue so far. Since there is no one-size-fits-all scheme for TCP congestion control, many various schemes have been developed to suit different kinds of communication environments. Among them, ECN (Explicit Congestion Notification) is the one which is able to feed the congestion state in the routers along a connection path back to the TCP transmitter. To promote the deployment of ECN and provide a concrete reference parameter to the Internet industry, in this paper, we actually measure the readiness of ECN in mail servers and analyze its deployment rate on the Internet. To clarify the characteristics of ECN in the mail servers, deployment rate of ECN is compared with that of ECN in major web servers. Based on the analysis results, we show some unique features of ECN in mail servers which are quite different from that of web servers.
Coherent Accumulation Algorithm for Maneuvering Weak Target Based on Angular-Stepped-GRFT
Authors: Sun Yuxian , Chang Shaoqiang , Wang Gang , Liang Zhennan , Guo Jinpeng , Liu Quanhua
Published: December 2022

This paper proposes an improved angular stepped generalized Radon Fourier transform (angular-stepped-GRFT) algorithm for maneuvering weak target detection in synthetic wideband radar. The proposed method can achieve motion parameters estimation for the maneuvering weak target based on stepped frequency signal. On this basis, the motion compensation of maneuvering weak target and range profile focusing method are realized.
Content Connectivity Based Polyhedron Protection Against Multiple Link Failures in Optical Data Center Networks
Authors: Li Xin , Liu Yu , Wang Hong , Xu Wei , Huang Shanguo
Published: December 2022

To further improve the resource efficiency of the p-polyhedron protection scheme against multi-link failures in optical data center networks (ODCNs), the content connectivity is considered when constructing the p-polyhedron structure. In this paper, the content connectivity-based polyhedron protection (CCPP) scheme is proposed. An ILP model and a heuristic algorithm are developed to realize the CCPP scheme. Numerical results show that the proposed CCPP scheme has a lower network redundancy. Moreover, the network redundancy of the CCPP scheme is positively correlated with the degree of content connectivity.
Study on Fault Prognostics and Health Management for UAV
Authors: Pan Jihui , Qu Wenqing , Xue Hao , Zhang Lei , Wu Liang
Published: December 2022

With the improvement of automation and intelligence degree of unmanned aerial vehicle, its application scenarios and service scope continue to expand. The UAV system is complex and the task environment is changeable, which poses new challenges to its safety and reliability. Fault prediction and Health management (PHM) technology can effectively reduce the risk of mission interruption caused by faults, and improve the quality of UAV mission throughout its life cycle. Firstly, the framework of UAV PHM technology is proposed based on the basic concepts of UAV and PHM technology, and then the research status of UAV fault diagnosis and fault prediction technology is analyzed and summarized. Finally, the challenges of UAV fault diagnosis and prediction technology are discussed. In addition, the development trend of UAV PHM technology is summarized from four aspects: failure mechanism basis, condition monitoring technology, fault model construction and intelligent technology application, aiming to provide certain reference for the research and development of the new generation of UAV PHM technology.
Time-Domain Feature Parameter Extraction Algorithm of Pulse Wave Based on Morphological Features
Authors: Geng Xingguang , Zhang Yitao , Zhang Jun , Wang Yunfeng , Zhang Haiying
Published: December 2022

Pulse wave’s Time-domain characteristics have clear physiological and pathological significance, and are widely used in pulse recognition and disease prediction. At present, for different forms of pulse wave feature points, the accuracy of time-domain feature point extraction algorithms varies greatly. Therefore, this paper proposes a time-domain feature extraction algorithm of pulse wave based on morphological features. Firstly, the pulse wave is normalized and averaged by using normalization and correlation averaging methods. Secondly, the morphological characteristics of the main wave are divided into two states: extreme point and non-obvious feature. The characteristic morphology of the dicrotic front wave and the dicrotic wave are divided into three states: extreme point, non-obvious feature, feature fusion or disappearance. Finally, the location range of each feature point is determined, and the pulse wave feature points are identified by the curvature method and the difference method. The experiment shows that the algorithm can effectively identify the pulse wave characteristic points of common pulse waves, which creates conditions for the effective extraction of subsequent relevant human physiological and pathological information.
Deep Learning Based Method for the Estimation of Patient’s Angles from Lateral Skull Radiographs
Authors: Nakazeko Kazuma , Kojima Shinya , Watanabe Hiroyuki , Kudo Hiroyuki
Published: December 2022

Radiography is used for initial diagnosis and postoperative follow-up. If a radiograph is deemed unsuitable for diagnosis, it is rejected. Retaking a radiograph is disadvantageous for the patient because it prolongs the examination time and increases the radiation dose. Skull radiography is the position in which retaking occurs most frequently. In skull radiography, the patient’s rotational direction is estimated from minute changes in the inner ear’s structure in the lateral skull radiograph. When retaking, the amount of correction for patient positioning is generally estimated from the errors in the rejected image through empirical evidence. Therefore, considerable expertise is needed to correct the positioning appropriately, and inexperienced radiologic technologists take considerable time to estimate this error. This study aimed to estimate the patient’s angle from lateral skull radiographs to compensate for radiologic technologists’ lack of experience and reduce the burden on patients. The radiograph is a simulated 2-D line-integral projection of a 3-D CT image, and we developed an estimation method using deep learning with supervised training. The network is based on a re-scaled ResNet. The patient’s angle was estimated in the lateral and superior-inferior directions. We evaluated the accuracy of estimation with the projected images of 256 simulated cases. The estimation errors were 0.48 ± 0.41° and 0.55 ± 0.50° in the lateral and superior-inferior angles, respectively. These findings suggest that a patient’s angle can be accurately estimated from a radiograph using deep learning, compensating for the lack of experience and reducing retaking time.
Tribological Property Behavior of Sinusoidal Texture with Various Intervals Under Scarce Oil Supply
Authors: Ma Jian , Liu Yancong , Zhang Na , Yang Xiaoshuang , Jia Hongyu
Published: December 2022

Reasonable texture area density can effectively form continuous oil film and improve the surface tribological properties. The combination of friction experiment and simulation analysis studied the influence of sinusoidal texture surface density on the surface friction performance, and also discussed its influence on the surface oil retention behavior. The experimental results and simulation analysis show that the texture synergistic lubrication has a significant positive correlation with the tribological properties. Moreover, it is noticed that tribological properties are more sensitive when the smaller texture interval is employed. And when the texture interval is 0.216 mm, the effect of sinusoidal texture on surface wettability and dynamic pressure lubrication is more significant. The results of this research contribute to better understanding of sinusoidal texture friction mechanism.
Analysis of Expressway Intelligent Service Area for Better Vehicles/Passengers Service
Authors: Duan Jianxian , Li Yanyan , Yu Qingqing , Shao Yongjun , Li Anqi
Published: December 2022

Expressway is known as a bridge for a country to modernize and is the only way to develop the modern transportation industry. Traditional expressway service area is difficult to meet the needs of current users, thus in order to improve the service level of the service area and meet the current high-level demand of the society for the service area, a set of highway intelligent service area integrated management system based on cloud computing and big data technology is proposed in this paper, which can fully and reasonably schedule resources, facilitate the use of user, and improve the user’s experience of the service area.
Optimizing IEEE 802.11a Preamble Design for Vehicle-to-Vehicle Communication
Authors: Xu Jianghai , Zheng Hao , Liu Huaiyuan , Liu Heng
Published: December 2022

The popularity of the autonomous driving requires to establish a data communication path among vehicles. Due to its low price and easy deployment, the WiFi device is a possible approach. Motivated by this, in this paper, we study the performance of IEEE 802.11a for vehicle-to-vehicle communication. We especially focus on the impact of multi-path and Doppler spreading to the time synchronization, which is the main challenges in the vehicle-to-vehicle communication due to the high mobility. We also propose the possible approach to improve the time synchronization performance in the IEEE 802.11a by optimizing the preamble design. We evaluate the average synchronization error of our proposed methods through simulation. Simulation result shows that replacing the preamble with the Zadoff-Chu sequence can obtain the best time synchronization performance.
SÎMÎN BEHBEHÂNÎ’NİN ŞİİRLERİNDE EFSANEVÎ UNSURLAR
Authors: KOPUZ ÇETİNKAYA Fatma
Published: December 2022

Çağdaş İran edebiyatının önemli kadın şairleri arasında yer alan Sîmîn Behbehânî (ö. 2014) İran’da modern şiirin kurucularından biri olarak kabul edilmiştir. Aktivist bir şair olan Behbehânî, iki kez Nobel Edebiyat Ödülü’ne aday gösterilmiştir. Şiirlerinde toplumsal meselelere, kadınların karşılaştığı sorunlara, halkın yaşadığı sıkıntılara, toplum tarafından yaratılan baskılara değindiği görülmektedir. Bu bağlamda İran toplumuna bir ayna tuttuğunu söylemek mümkündür. Klasik nazım türlerinden gazeli sıkça kullanmıştır ve bu türde başarılı olduğunu kanıtlamıştır. He ne kadar klasik nazım türünde şiir söylemiş olsa da kaleme aldığı konular, kendi toplumundan ve zamanından izler taşımaktadır. Şiirlerinde folklorik ve efsanevî unsurlardan da faydalandığı göze çarpmaktadır. Kadim efsaneleri, masalları, atasözü ve eski inanışları hatta çocuk oyunlarını dahi şiirlerinde kullandığı görülmektedir. Bu anlamda geçmiş ile modern toplumu usta bir şekilde harmanladığı ve klasik edebiyata hâkim bir birikime sahip olduğu ortaya çıkmaktadır. Behbehânî bu birikimini, modern zaman ve toplumla yoğurarak kendi şiirini kurgulamıştır.

Efsaneler, eski metinlerde geçen öyküler, halk inanışları, hurafeler Sîmîn Behbehânî’nin şiirinde belirgin bir yere sahiptir. Bu anlamda, çalışmada kısaca Sîmîn Behbehânî’nin hayatına değindikten sonra şiirlerinde göze çarpan efsanevî unsurlar ele alınacaktır. Bu unsurların eski kanyaklarda nasıl geçtiği ve Behbehânî’nin ise bu unsurları nasıl kullandığı üzerinde durulacaktır.
The Problem of Truth in Biology: Levels of Reflexive Analysis
Authors: Zuev Vasily Viktorovich Зуев Василий Викторович
Published: December 2022

The problem of objectivity or truth of scientific knowledge is understood in science as a complex of questions of a theoretical, philosophical-scientific, and philosophical nature. The basis of the analysis is the allocation of hierarchical levels of reflexive awareness of the problem of truth, different in degree of abstraction, each of which uses specific methodological tools of analysis that have developed in science. In biology, there are several levels of reflexive awareness of the truth of knowledge. Theoretical – a method of evaluating the truth of a particular theory, which is formed in different ways in classical and non-classical science. In classical science, the theory has an individual character – the initial empirical material for each researcher is different, so the true, from the point of view of each researcher, is the model he built, which, as he believes, fixes the object in reality "by itself". In non-classical science, the theory is universal and the question of truth is asked to theoretical constructions that have not yet been empirically confirmed. For example, a "gene" was a hypothetical construct until empirical evidence for its existence was found. Philosophical-scientific-reflection on the corresponding theory of truth, accepted by scientists at the theoretical level of reflection. The question of the direct comparison of elements of knowledge and objects of reality is a big problem that is discussed in the philosophy of science. The possibility of identifying the correspondence of knowledge and object is questioned, since it turns out that we cannot go beyond our knowledge of the object and oppose the object to knowledge. Metatheoretical – a choice between two or more theoretical concepts for truth. As an example, the confrontation between classical biology and non-classical (genetics) in the 1900s – 1960s can be considered a situation of philosophical choice.
Understanding acceptance of Autonomous Mobility Services using statistical and deep learning approaches
Authors:   , Bala Haimanti
Published: December 2022

The emergence of vehicle automation and its subsequent growth has led to new transport service offerings, generally known as Autonomous Mobility Services (AMS), that have the potential to facilitate or even replace human-operated vehicles. AMS contains different forms of potential mobility options which may contradict current transport modal concepts in terms of functionalities. For example, an autonomous shuttle bus which is a form of autonomous transit may serve similarly as an autonomous taxi/robo-taxi in terms of functionalities, coinciding with the concept of Shared Autonomous Mobility Services (SAMS). Even if the functionalities or operational principles are different, peoples' perceptions of sharing rides in any of these services may be alike. Apart from these confusions in functionalities mentioned above, peoples' attitudes and acceptance of AMS, once it's implemented in any form in the public road environment, remains a significant research aspect. To address these issues, this thesis tried to first clearly distinguish different types of AMS. Second, it tried to assemble the progress till now in acceptance-related research of AMS while reviewing the previous study approaches, outcomes, policy implications, and future research directions. Third, this study attempted to understand the acceptance of AMS using statistical and deep learning approaches leveraging both survey and social media data. Fourth, this study tried to present the consequent applicabilities and limitations of using both types of data sources for autonomous vehicle acceptance research. Eventually, this thesis intends to present an overall idea of the AMS acceptance research with future directions leveraging both data sources in an individualistic or combined manner.
ClaimChain: secure Blockchain platform for handling insurance claims processing
Authors:   , Bhamidipati Naga Ramya
Published: December 2022

Insurance claims processing involves multi-domain entities and multi-source data, along with a number of human-agent interactions. Consequently, this processing is traditionally manually-intensive and time-consuming. Blockchain technologybased platforms for intelligent automation can significantly improve the scale and response time of claims processing. However, there is a need to secure such platforms against fraud (e.g., duplicate claims) and the loss of data integrity caused due to cyber-attacks (e.g., Sybil attack). This thesis proposes a novel "Claim- Chain", a consortium Blockchain platform that transforms the state-of-the-art NICB/ISO database architecture approach through increased shared intelligence and participation of insurance companies. ClaimChain features include: (a) automation of insurance claim processing via implementation of a Blockchain infrastructure, (b) infrastructure-level threat modeling via attack tree formalism for data integrity attacks, and (c) application-level fraud modeling for identified prominent red flags through machine learning models and risk scoring on the basis of risk severity. The scalability of ClaimChain is evaluated by simulating realistically large number of Blockchain transactions of claim processing. It is shown that data integrity attacks at the infrastructure-level can be mitigated (reduction of 24 percent probability in loss) through implementation of security design principles. Also, fraud-detection is performed over an open dataset in ClaimChain to show how machine learning models can detect fraudulent activity with 98 percent accuracy.
Location prediction and trajectory optimization in multi-UAV application missions
Authors:   , Singh Rounak
Published: December 2022

Unmanned aerial vehicles (a.k.a. drones) have a wide range of applications in e.g., aerial surveillance, mapping, imaging, monitoring, maritime operations, parcel delivery, and disaster response management. Their operations require reliable networking environments and location-based services in air-to-air links with cooperative drones, or air-to-ground links in concert with ground control stations. When equipped with high-resolution video cameras or sensors to gain environmental situation awareness through object detection/tracking, precise location predictions of individual or groups of drones at any instant possible is critical for continuous guidance. The location predictions then can be used in trajectory optimization for achieving efficient operations (i.e., through effective resource utilization in terms of energy or network bandwidth consumption) and safe operations (i.e., through avoidance of obstacles or sudden landing) within application missions. In this thesis, we explain a diverse set of techniques involved in drone location prediction, position and velocity estimation and trajectory optimization involving: (i) Kalman Filtering techniques, and (ii) Machine Learning models such as reinforcement learning and deep-reinforcement learning. These techniques facilitate the drones to follow intelligent paths and establish optimal trajectories while carrying out successful application missions under given resource and network constraints. We detail the techniques using two scenarios. The first scenario involves location prediction based intelligent packet transfer between drones in a disaster response scenario using the various Kalman Filtering techniques. The second scenario involves a learning-based trajectory optimization that uses various reinforcement learning models for maintaining high video resolution and effective network performance in a civil application scenario such as aerial monitoring of persons/objects. We conclude with a list of open challenges and future works for intelligent path planning of drones using location prediction and trajectory optimization techniques.
Structural and kinetic studies of proline metabolic enzyme inhibitors and inactivators
Authors:   , Bogner Alexandra Nicole
Published: December 2022

Structural and kinetic studies of the proline metabolic enzymes proline dehydrogenase (PRODH), [delta]1-pyrroline-5-carboxylate reductase (PYCR), and aldehyde dehydrogenase 4A1 (ALDH4A1) are presented in this thesis. Identifying inhibitors and inactivators of these enzymes can be purposed for chemical probes in studies elucidating the role of proline metabolism in cancer and other diseases. Chapter 1 introduces the enzymes of proline metabolism and provides a summary of literature on the involvement of PYCR isoforms in cancer. Additionally, a compilation of RNA transcript data was used to explore differential gene expression of PYCRs across 28 cancer types. Chapter 2 details a structural and enzyme kinetic investigation of compounds that noncovalently bind within the PRODH active site. These data provide insight into structure-affinity relationships of the best-known PRODH inhibitor, L-tetrahydro-2-furoic acid. Chapter 3 focuses on the inhibition of the proline/hydroxyproline catabolic enzyme ALDH4A1 by the stereoisomers of proline and 4-hydroxyproline. We found that hydroxyproline catabolism is subject to substrate inhibition by trans-4-hydroxy-L-proline, analogous to the known inhibition of proline catabolism by L-proline. Finally, following on our recent discovery of photoinduced covalent inactivation of PRODH by 1,3-dithiolane carboxylate, Chapter 4 preliminarily explores the scope of this reaction using seven acyclic carboxylic acids that contain an S atom bonded to the a-carbon, a feature we hypothesize is important for inactivation. Crystallographic evidence for three of these compounds covalently and irreversibly inactivating the flavin of PRODH was obtained. Also, a structure of one compound noncovalently bound in the active site was determined, which represents the initial step of the inactivation mechanism. Four compounds showed signs of reversibility, which is a new feature of this class of inactivator and remains to be explored.
Computer vision for plant and animal inventory
Authors:   , Chen Guang
Published: December 2022

The population, composition, and spatial distribution of the plants and animals in certain regions are always important data for natural resource management, conservation and farming. The traditional ways to acquire such data require human participation. The procedure of data processing by human is usually cumbersome, expensive and time-consuming. Hence the algorithms for automatic animal and plant inventory show their worth and become a hot topic. We propose a series of computer vision methods for automated plant and animal inventory, to recognize, localize, categorize, track and count different objects of interest, including vegetation, trees, fishes and livestock animals. We make use of different sensors, hardware platforms, neural network architectures and pipelines to deal with the varied properties and challenges of these objects. (1) For vegetation analysis, we propose a fast multistage method to estimate the coverage. The reference board is localized based on its edge and texture features. And then a K-means color model of the board is generated. Finally, the vegetation is segmented at pixel level using the color model. The proposed method is robust to lighting condition changes. (2) For tree counting in aerial images, we propose a novel method called density transformer, or DENT, to learn and predict the density of the trees at different positions. DENT uses an efficient multi-receptive field network to extract visual features from different positions. A transformer encoder is applied to filter and transfer useful contextual information across different spatial positions. DENT significantly outperformed the existing state-of-art CNN detectors and regressors on both the dataset built by ourselves and an existing cross-site dataset. (3) We propose a framework of fish classification system using boat cameras. The framework contains two branches. A branch extracts the contextual information from the whole image. The other branch localizes all the individual fish and normalizes their poses. The classification results from the two branches are weighted based on the clearness of the image and the familiarness of the context. Our system achieved the top 1 percent rank in the competition of The Nature Conservancy Fisheries Monitoring. (4) We also propose a video-based pig counting algorithm using an inspection robot. We adopt a novel bottom-up keypoint tracking method and a novel spatial-aware temporal response filtering method to count the pigs. The proposed approach outperformed the other methods and even human competitors in the experiments.
Improvement of co-digesting swine manure and waste kitchen oil
Authors:   , Duong Cuong Manh
Published: December 2022

Anaerobic co-digestion of swine manure (SM) and waste kitchen oil (WKO) was conducted to evaluate the effect of substrate loading rates on biogas production efficiency. Biogas yields of M4O2 (4 g-VSSM L-1 d-1 + 2 g-VSWKO L-1 d-1) and M2O1 were 917 [plus or minus] 43 and 909 [plus or minus] 37 mL g-VS-fed-1, which were 25.7 percent and 24.6 percent higher than the mono-digestion of M2, respectively. However, higher OLRs of SM and WKO did not increase biogas yield. A significant increase of bacterial alpha-diversity was observed in M2O1, at 233.0 [plus or minus] 3.6 compared with 218.7 [plus or minus] 5.1 of M2. Less bacterial alpha-diversity and volatile fatty acids accumulation were observed in M4O1 and M4O2. When the digesters were fed with M2, introduction of more than 1.2 g-VSWKO L-1 d-1 did not increase biogas yield per VS-fed compared to M2O1, and might cause system imbalance. It took up to 84 days to observe system failure. Biogas potential (BP) study showed significantly higher of biogas yield produced from WKO compared to SM. Therefore, addition of WKO into SM digesters resulted in the increase of biogas production, from 9.0 [plus or minus] 3.2 percent to 22.6 [plus or minus] 3.1 percent, compared with digestion of only SM. However, BP of M4O1 and M4O2 were 93.2 [plus or minus] 6.6 percent and 95.6 [plus or minus] 4.8 percent, compared with BP of separate substrates, which indicated that the synergistic effects of co-digestion were due to the increase of organic loading rate and nutrient balance during the process. Co-digesting SM and WKO at ambient or thermophilic working temperature resulted in the low biogas productions compared to mesophilic condition. Application of M2O1 in the on-farm ADs could result in the highest biogas production while maintaining system stability. However, water consumption and digester size were both higher than when M4O1 or M4O2 was applied. A polynomial regression model with variable interaction was developed which showed the effectiveness in terms of biogas production from SM and WKO. The differences of biogas yield estimated by the model and the lab results were in the range of 0.2 percent to 8.6 percent, except only one OLR, in which 15.9 percent difference was observed. However, only the selected dataset with removal of zero biogas values was utilized for the model improvement, and model application was limited in the ranges of each key variable. Two decision support tools were developed based on the model to estimate biogas production and other operating factors of on-farm ADs.
Structural modeling of the 3D genome using machine learning
Authors:   , Highsmith Max Richard
Published: December 2022

This dissertation, submitted as a partial requirement for completion of the Doctorate of Philosophy, outlines the research performed by Max Highsmith in the BDM Lab. This work includes a functional expansion of a three-dimensional genome conformation database, the development of a novel, deep-learning based strategy for the enhancement of Hi-C data, The development of deep learning approach for domain identification using epigenetic features, and the development of a novel computational tool for 4D modeling of chromosome dynamics.
Theoretical analysis and experimental investigation of a combined compact evaporative cooler
Authors:   , Ismael Laith Abdulmonem
Published: December 2022

Evaporative cooling utilizes the fact that water can absorb a relatively large amount of heat during the evaporation process. When this evaporation process takes place in ambient air, the ambient air temperature can be dropped significantly resulting in the cooling effect. In the current study, theoretical analysis and experimental investigation of an innovative compact combined evaporative cooler (CCEC) have been conducted. The innovative CCEC mainly consists of an innovative compact heat exchanger, a blower, a water distribution system, and a water reservoir. The compact heat exchanger has integrated two sets of orthogonally orientated air-air channels, which are made of aluminum sheet with a thickness of 0.3 mm and a fin gap of 3 mm. Aluminum surfaces are coated with nanolayer coatings using a vapor deposition process, which can form thin liquid film for high evaporating heat transfer coefficient and meniscus of liquid-vapor interface for low local saturation pressure. This aluminum surface can significantly modify the wetting characteristics and reduce the local saturation pressure. The contact angle can be reduced from 50 degrees to almost zero degrees. With this wetting condition, water can be readily spread, allowing a thin water film to be easily formed on the surface of the compact heat exchanger to produce thin-film evaporation resulting in an extra high evaporating heat transfer coefficient. More importantly, when the curved liquid-vapor interface exists, the liquid saturation pressure can be reduced which can be predicted by the Kelvin equation, i.e., pk=p[infinity]e^-2[sigma][nu]1/rRT resulting in a decrease of evaporating temperature. In order to predict the heat transfer performance occurring in this innovative CCEC, a mathematical model is developed. The model can be used to predict the effects of relative humidity, dry bulb temperature, channel spacing, air flow rate, thin-film evaporation, mixing process, and saturation pressure on the production air temperature and relative humidity. The prediction is compared with experimental data and shows that the prediction agrees with the experimental data. In addition, the investigated cooler does not use an evaporative pad as media, so no replacement of pad or maintenance is needed, furthermore, the investigated cooler eliminates corrosion, mineral deposits, mold, and bacteria that could potentially accumulate on the media pads, particularly during the off-season or in the presence of dust.