In network information theory, the relay channel models a communication scenario where there is one or more relay nodes that can help the information transmission between the source and the destination. Although the capacity of the relay channel is still unknown even in the single-relay case, two fundamentally different relay schemes have been developed by (Cover and El Gamal, 1979) for such channels, which, depending on whether the relay decodes the information or not, are generally known as Decode-and-Forward (D-F) and Compress-and-Forward (C-F). In the D-F relay scheme, the relay first decodes the message sent by the source and then forwards it to the destination, and the destination decodes the message taking into account the inputs of both the source and the relay. In contrast, the C-F relay scheme is used when the relay cannot decode the message sent by the source, but still can help by compressing its observation into some compressed version, and forwarding this compression into the destination; the destination then either successively or jointly decodes the compression of the relay's observation and the original message of the source. For the single-relay case, it is known that joint compression-message decoding, although providing more freedom in choosing the compression at the relay, cannot achieve higher rates for the original message than successive decoding. This thesis addresses some fundamental issues in generalizing and unifying the above D-F and C-F relay schemes to the multiple-relay case. We first generalize the C-F scheme to multiple-relay channels, and investigate the question of whether compression-message joint decoding can improve the achievable rate compared to successive decoding in the multiple-relay case. It is demonstrated that in the case of multiple relays, there is no improvement on the achievable rate by joint decoding either. More interestingly, it is discovered that any compressions not supporting successive decoding will actually lead to strictly lower achievable rates for the original message. Therefore, to maximize the achievable rate for the original message, the compressions should always be chosen to support successive decoding. Furthermore, it is shown that any compressions not completely decodable even with joint decoding will not provide any contribution to the decoding of the original message. We also develop a new C-F relay scheme with block-by-block backward decoding. This new scheme improves the original C-F relay scheme to achieve higher rates in the multiple-relay case as the recently proposed noisy network coding scheme. However, compared to noisy network coding which uses repetitive encoding/all blocks united decoding, our new coding scheme is not only simpler, but also reveals the essential reason for the improvement of the achievable rate, that is, delayed decoding until all the blocks have been finished. Finally, to allow each relay node the freedom of choosing either the D-F or C-F relay strategy, we propose a unified relay framework, where both the D-F and C-F strategies can be employed simultaneously in the network. This framework employs nested blocks combined with backward decoding to allow for the full incorporation of the best known D-F and C-F relay strategies. The achievable rates under our unified relay framework are found to combine both the best known D-F and C-F achievable rates and include them as special cases. It is also demonstrated through a Gaussian network example that our achievable rates are generally better than the rates obtained with existing unified schemes and with D-F or C-F alone.

京都大学 / 新制・課程博士 / 博士(工学) / 甲第23478号 / 工博第4890号 / 新制||工||1764(附属図書館) / 京都大学大学院工学研究科社会基盤工学専攻 / (主査)教授 川池 健司, 教授 藤田 正治, 准教授 米山 望 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

Payment Channel Networks (PCNs) provide a solution to the scalability problem in blockchain technology. They facilitate multiple-hop transactions via payment channels between peers, allowing for the execution of several transactions before updating each node’s balance on the blockchain. However, the performance of the network in effectively routing payments is affected by unbalanced channels. This imbalance reduces the network’s ability to route payments in both directions within a channel, resulting in decreased overall performance. Previous research has identified unidirectional payment flows in the network as the underlying cause of this issue. The payment flow is based on the demand for payments between peers and the paths these payments take. The aim of this study is to investigate the relationship between payment patterns and the average channel imbalance, where the payment pattern is a combination of demand patterns and what pathfinding algorithm is used. The study uses a model of PCNs that allows the relationship of the variables of interest to be measured, while other impacting variables are controlled. A deterministic value of the average channel imbalance for a PCN topology, demand pattern, and pathfinding strategy is achieved by computing the steady-state imbalance. The results show that the less homogeneous the demand pattern is, the more asymmetric the payment flow becomes, and the more imbalanced the network’s channels become. The results confirm what previous research mentions: unidirectional flow causes the payment channel to become imbalanced. The demand pattern that generates unidirectional flow imbalance significantly affects the entire network, regardless of the pathfinding strategy employed. Even with the possibility of a pathfinding strategy influencing payment flow to make it less unidirectional, the inherent unbalanced nature of the demand pattern remains a considerable challenge. The results also show that the pathfinding strategy has a less significant impact than the demand pattern on the balance of the network over time. / Payment Channel Networks (PCN) tillåter fler transaktioner utföras på kortare tid för en mindre kostnad, än att publicera varje enskild transaktion direkt på blockkedja och är en bra lösning på skalbarhetsroblem kopplat till blockedjor som baseras på proof-of-work. PCNs möjliggör för flerhoppstransaktioner via noder som kan vidarebefodra flera betalningar på varje enskild betalningskanal de håller öppna med andra noder. Inte förrän en kanal stängs publiceras den senaste uppdateringen av saldot för enskild betalningskanal på blockkedjan. Trots detta lider nätverkets prestanda av att kanaler blir obalanserade och ökar risken för att transaktioner misslyckas routas. Tidigare forskning nämner att orsaken till att kanaler blir obalanserade är enriktade betalningsflöden i nätverket, vilket beror av betalningsflödet som är en kombination av betalningsefterfråga (vem vill betala vem) mellan noderna och vilka vägar som dessa betalningar tar. Syftet med denna studie är att undersöka sambandet mellan betalmönster och genomsnittlig kanalobalans, där betalmönstret är en kombination av efterfrågansmönster och vägvalsstrategier. Studien använder en PCN-modell som möjliggör att undersöka sambandet mellan de variabler som är tänkt ska mätas, samtidigt som andra variabler kan kontrolleras. Genom att beräkna den stabila obalansen uppnås ett deterministiskt värde för genomsnittlig kanalobalans i varje nätverkstopologi. Resultaten visar att ju mindre homogent efterfrågemönstret är, desto mer asymmetriskt blir betalningsflödet och desto mer obalanserade blir nätverkets kanaler. Resultatet bekräftar det som tidigare forskning nämner: enriktade flöden orsakar obalans i betalningskanalen. Specifikt är det efterfrågansmönstern som skapar enriktade flöden som har de högsta stabila obalansen, oavsett vilken vägvalsstrategi som används. Resultaten visar också att vägvalsstrategin har mindre betydelse än efterfrågemönstret, åtminstone för vägvalsstrategier som leder till liknande valda vägar.

Research into the use of LiDAR data for purposes other than simple topographic elevation determination, such as urban land cover classification and the identification of forest biomass, has become prominent in recent years. In many cases, alternative analysis methodologies conducted using airborne LiDAR data are possible because the raw data collected during a survey can include information other than the classically used elevation and coordinate points, the X, Y, and Z of the plane. In particular, intensity return values for each point in a LiDAR grid have been found to provide a useful data set for wet and dry channel classification. LiDAR intensity return data are, in essence, a numeric representation of the characteristic light reflectivity of the object being scanned; the more reflective the object is, the higher the intensity return will be. Intensity data points are collected along the course of the channel network and within the perceived banks of the channel. Intensity data do not crisply reflect a perfectly wet or dry condition, but instead vary over a range such that each location can be viewed as partially wet and partially dry. It is advantageous to assess problems of this type using the methods of fuzzy logic. Specifically, the variance in LiDAR intensity return data is such that the use of fuzzy logic to identify intensity cluster centers, and thereby assign wet and dry condition identifiers based on fuzzy memberships, is a possibility. Membership within a fuzzy data set is characterized by a value representing the degree of membership. Typically, membership values range from 0 (representing non-membership) through 1 (representing full membership), with many observations found to be not at either extreme but instead at some intermediate value representing partial membership. The ultimate goal of this research was to design and develop an automated algorithm to identify wet and dry channel sections, given a previously identified channel network based on topographic elevation, using a combination of intensity return values from LiDAR data and fuzzy logic clustering methods, and to implement that algorithm in such a way as to produce reliable multi-class channel segments in ArcGIS. To enable control of calculations, limiting parameters were defined, specifically including the maximum allowable bank slope, and a filtering percentage to more accurately accommodate the study area. Alteration of the maximum allowable bank slope has been shown to affect the comparative quantity of high and low intensity centroids, but only in extreme bank slope conditions are the centroids changed enough to hamper results. However, interference from thick vegetation has been shown to lower intensity values in dry channel sections into the range of a wet channel. The addition of a filtering algorithm alleviates some of the interference, but not all. Overall results of the tool show an effective methodology where basic channel conditions are identified, but refinement of the tool could produce more accurate results.

Groundwater flow and transport in deep crystalline rock is an important area of research. This is partly due to its relevance for constructing a long term repository for storing radioactive spent nuclear fuel in deep bedrock. Understanding the behavior of flow and transport processes in deep crystalline rock is crucial in developing a sustainable solution to this problem. This study aims to increase the understanding of how channel network models (CNM) can be applied to represent groundwater flow and solute transport in sparsely fractured crystalline rock under site specific conditions. A main objective was to determine how to incorporate structural and hydrogeological site characterization data in the construction of the CNMs. In addition to this, the associated key parameters of the CNMs were investigated to gain further understanding of model site application. To that end, a scripting approach with the python scripting library Pychan3d was used to create alternative channel network representations of a field site. A conceptual discrete fracture network (DFN) model was constructed using field site data obtained from a structural model of the fractures present at the site of the Tracer Retention Understanding Experiments (TRUE) - Block Scale at the Äspö Hard Rock Laboratory (HRL). This conceptual model was used as a base for constructing two different alternatives, denoted respectively as sparse and dense, of a CNM. The sparse CNM consisted of a limited amount of channels for each fracture, while the dense CNM acted as a DFN proxy, taking the full extent of the fracture areas into account and creating a dense, large network of flow channels for each fracture. In order to verify the performance of the generated CNMs, a reproduction of tracer tests performed at the same specific field site was attempted using a particle tracking technique. In addition to this, long term predictions of solute transport without the interference of the pumps used during the tracer tests were done in order to estimate transport time distributions. Pychan3d and the scripting approach was successfully used to create CNMs respecting specific conditions from the TRUE-Block Scale site. The sparse CNM was found to give very adequate flow and transport responses in most cases and to be relatively easier to calibrate than its dense counterpart. The long term transport predictions at the site according to the models seem to follow a channelized pattern, with only a few select paths for transport. The difficulties encountered in matching the dense CNM with the tracer tests most likely stem from difficulties in flow calibration, as well as certain key parameters being assigned too generically.

Industry 4.0 is a concept that emphasizes system and process automation, digitalization, and data interchange in the workplace. Its purpose is to create a smart factory that will cut lead times and increase system productivity by responding to client demand or unforeseen circumstances. Manufacturing, supply chain (SC), and logistics could all benefit from this notion. Industry 4.0 adoption in supply chain management (SCM) is a new and important topic that requires further investigation.  This study is to research the impact of Industry 4.0 on supply chains based on probable Industry 4.0 drivers and barriers. For future supply chains, Industry 4.0 is expected to introduce new difficulties and opportunities. The study addressed a number of issues and provided a method for effectively adapting and integrating the Industry 4.0 concept into supply chains. The research should help supply chain manager’s better grasp the challenges of integrating Industry 4.0 in their networks. Simulation study enables for the assessment of Industry 4.0 adoption in terms of its impact on SC performance, as well as the incorporation of both the drivers and impediments of this technological change. A conceptual framework for Industry 4.0 use in supply chains is proposed in addition to giving an empirical basis for this relationship.  Then we examined the 4.0 industry in Zagros Petrochemical Company. The statistical population in this study is the official employees of Zagros Petrochemical. Then, in order to evaluate the impact of Industry 4.0 on the supply chain management of Zagros Petrochemical Company, a supply chain management methods questionnaire was used. The tool of analysis in this research is a questionnaire which was converted into mathematical output through SPSS software and was statistically analyzed. Using inferential statistics (T-Test), it was found that Industry 4.0 has a significant effect on Zagros Petrochemical Company.  Then, using Friedman test, the rank of the studied features was compared. Cochran's sampling formula was used for sampling at 5% error level. In the end, based on inferential statistics, it was found that considering that in the first question of the research, the p-value obtained is more than 0.05, unlike the rest of the research questions, the null hypothesis is not rejected. Therefore, it can be inferred that Industry 4.0 is not effective in terms of strategic partnership with the supply chain management supplier of Zagros Petrochemical Company.

This research work concentrate on developing digital soil maps to support field based plant phenotyping research. We have developed soil organic matter content (OM), cation exchange capacity (CEC), natural soil drainage class, and tile drainage line maps using topographic indices and aerial imagery. Various prediction models (universal kriging, cubist, random forest, C5.0, artificial neural network, and multinomial logistic regression) were used to estimate the soil properties of interest.