Search

A novel framework has recently been proposed for designing the molecular structure of chemical compounds with a desired chemical property using both artificial neural networks and mixed integer linear programming. In the framework, a chemical graph with a target chemical value is inferred as a feasible solution of a mixed integer linear program that represents a prediction function and other requirements on the structure of graphs. In this paper, we propose a procedure for generating other feasible solutions of the mixed integer linear program by searching the neighbor of output chemical graph in a search space. The procedure is combined in the framework as a new building block. The results of our computational experiments suggest that the proposed method can generate an additional number of new chemical graphs with up to 50 non-hydrogen atoms., arXiv admin note: substantial text overlap with arXiv:2107.02381

In this paper, the electric characteristics and DLTS profiles of 600V RC-IGBT (Reverse Conducting Insulated Gate Bipolar Transistor) with electron beam (EB) irradiation are compared in order to investigate the influence of wafer materials. Floating Zone (FZ) and Magnetic Czochralski (MCZ) wafers with various kinds of carbon (C) and oxygen (O) concentration are evaluated. O concentration does not have a clear impact on V CEsat . In contrast, C concentration has a clear tendency. DLTS analysis shows that C concentration affects the degree of Si defects induced by EB irradiation which results in the differences of the electric characteristics.

Micro solder balls are used in flip-chip mounting to form bumps on a BGA (Ball Grid Array) substrate. As electronic devices become more compact, solder balls have become smaller in size and narrower in pitch. We examined to utilize the gravure offset printing method, which applies the micro wire printing technology to mount 30 µm micro solder balls in a narrow pitch.

In wireless sensor networks (WSNs), wireless power transfer (WPT) has been studied as energy harvesting technique for prolonging their network lifetime. The WPT can supply power resource to sensor nodes (SNs) wirelessly, however, the reception power at the SNs depends on the distance from a WPT equipment, leading to the non-uniformity in the power reception among the SNs. For the fixed-located WPT equipment, the SNs distant from the WPT equipment suffer from insufficient power reception. Against this problem, this paper proposes novel network configuration introducing multiple hybrid access points (HAPs), which equip two functions of data collection and relay transmission (CH function) and WPT, and then, one of HAPs provides these both functions alternately. By periodically rotating the CH with the WPT function, the location of the WPT equipment can be changeable, which reduces the non-uniformity in the power reception among SNs. This paper also proposes a clustering scheme considering the residual power at SNs to reduce their power depletion. The simulation results show that the proposed system reduces power depletion at the SNs and improves the data collection rate compared with the conventional systems.

Internet of Things (IoT) platforms are desired for realizing an early warning system of COVID-19 detection. The key challenge is to provide solution including sensors to meet the application requirements. We manipulate the diarrhea symptom to assess the COVID-19 case where the virus indirectly can be detected in the wastewater. We proposed COVID-19 early warning detection system architecture from the wastewater comprises of five layers, namely perception layer, connectivity layer, middleware layer, application layer and business layer. Our architecture integrates IoT and machine learning (ML) components to build the total solution. Benefits on the stakeholders can be anticipated including the local authority and hospitals. Key challenges are also discussed in developing the COVID-19 early warning detection system based on the wastewater circumstances.

In recent years, technical innovation is also expanded in the field of housing construction. The unit basic reinforcing rod system (BRS) is one of important innovation of “layout of reinforcement bar process” in the making of the housing base. This process was performed by handwork in the past, many housing construction sites are using BRS at now. The BRS is made in factory and production process are complicated. Therefore, the process systematization is behind. A manufacturing premise of BRS is regarded as a flow shop system that the lot sizing condition is different every process. In addition, the first process of BRS is a cutting process. It is also important for making of BRS. In this study, an efficient scheduling method is proposed using the priority of the lot sizing condition and a cutting process.

Since productivity depends on the operator's skill in the labor-intensive c manufacturing system, effective skill training is a type of important issue. We proposed the operator allocation method using the skill index as a new index of human factor in our previous study. However, the training and productivity are in trade-off relation in general. In this study, we propose the operator allocation method of the labor-intensive manufacturing system by the case of cell manufacturing system which considers operator training and productivity at the same time by using the sum of skill index and the makespan in multi-objective function. For using the different unit of indexes, the standardization point in this method was adopted. The pilot test was executed and standardized an index to deal with a different index at the same time. And Simulated Annealing was using to find a solution.

Early diagnosis of diabetes peripheral neuropathy (DPN) is necessary for prevention of diabetes foot diseases in the early stage. In this study, we propose a quantitative DPN screening system which uses the change of the foot plantar images on different hardness floor based on skin hardness of patients. The purpose of this study is to estimate the severity of the DPN based on the foot plantar image. We compared the evaluation between the method of the change of the foot plantar image and classical way to calculate severity points, neurological, and hemodynamic assessments. A positive correlation (0.517) was presented between the proposed evaluation and the classical assessments. Also, we compared the area change rate with the hardness of the foot plantar skin measured by durometer and the hardness based on proposed image diagnosis. We measure the foot plantar images, the hardness, and classical assessment for 113 persons. A significant positive correlation was found between the area change rate and the hardness of the skin of big toe (0.300), first metatarsal head (0.448), and heel (0.400). A significant positive correlation (0.468) was also found between severity and skin hardness in the heel.

Bin picking of various daily items is an important research problem in robotics. If the target items are diverse, multiple grippers are normally used. A design of gripper combinations depends not only on the item variations but also on the state of the bins, which changes while robots pick items from them. In this paper, we propose a gripper combination strategy to change the gripper combination during a bin-picking task based on the sparseness of objects inside bins. As an experiment, we build a robot system which has three different types of grippers. By using the proposed combination strategy, the system effectively changed the gripper combination during the task, and picked 18/20 items to obtain the 3rd place in the Stow task at the Amazon Robotics Challenge 2017. The successful picking rate and Mean Picks Per Hour (MPPH) were higher than the 1st place team. In this paper, we describe the problem, method to switch the combination, system including gripper design and recognition algorithm, and experimental results from the competition.

The demand for smart control technology is glowing according to the increase of expectation for autonomous driving. This paper presents an integrated planning and control scheme for automated lane-change behavior based on real-time nonlinear model predictive control (NMPC). In the proposed method, switched weighting parameters are used in the cost function over the prediction horizon. This enables us to reduce the number of reference points for control compared with the conventional framework. In addition, some safety constraints, such as collision avoidance and/or speed limitation are considered in a consistent manner. Although the NMPC usually requires huge computational cost, by applying the Continuation/GMRES (C/GMRES) method, it can be drastically reduced and become possible to be implemented in real time. Finally, validity of the proposed method is demonstrated by simulation study.

We discuss in this paper a recommendation-oriented information retrieval for music data. Particularly, we present a general framework of retrieving unexpected objects for a given query, where each data object is represented as a feature vector and assigned a multi-label as well. Given an object-feature matrix X1 and an object-label matrix X2, we simultaneously factorize X1 and X2 as X1 is approximately equal to B V and X2 to S W by means of Nonnegative Shared Subspace Method, where the basis S is a part (subspace) of the basis B. Such a shared subspace associates the label-information with the feature-information of the original matrices. Based on the shared subspace, thus, we can predict a multi-label for a query feature-vector with unknown labels. Our unexpected object for the query is defined as an object which is similar to the query in the feature space, but is apart from the query in the label space. In order to obtain those unexpected objects from several viewpoints of similarity, we formalize our retrieval task as a problem of finding formal concepts satisfying a constraint w.r.t. the unexpectedness. Our experimental result for a dataset of music pieces created from Million Song Dataset Benchmarks shows we can actually detect an interesting music cluster as the extent of a formal concept including some unexpected music pieces for a given music query.